From 2d7c3756500eac7be114905341a3e8c2cc44aac5 Mon Sep 17 00:00:00 2001 From: Julian Giordani Date: Tue, 31 Oct 2023 17:53:15 +1100 Subject: [PATCH 1/6] V2.15.1 PR (#674) * starting for new release uw2.15 * Update surfaceProcesses.py Update timeField to record time of deposition of sediment in SP functions * Update surfaceProcesses.py Fix typo in 3D surface. Also reset time of the air material to the model time * Update surfaceProcesses.py fixed typo in func * updating the setonix jobs runners * Update surfaceProcesses.py Simplify 2D velocity and diffusivity SP functions * Update surfaceProcesses.py Didn't create object to bcast surface function to * Update surfaceProcesses Revert back to MPI gather of data over evaluate_global Update the examples * Adding melt dynamic heating to the advection diffusion solver see issue #669 * docker utils updates * Adding some final fixes to the dockerfile scripts * Update CHANGES.md * Remove obsolete glucifer bits * Update dockerfile Badlands install and CHANGES.md * Update CI for docker builds * Fixing the velocitySurface 2D and 3D implementation to handle restarts. Changes also include: * Renaming the surface tracers to prevent unexpected overwrites * The surfaceProcess class saving the passive tracer key to use during initialisation and solve. This is to make the code not save an instance of the pt to the swarm incase the pt is swapped in/out of Model memory. And helps with code readability. * Get the latest release of badlands from pypi --------- Co-authored-by: Ben Knight <55677727+bknight1@users.noreply.github.com> --- .github/workflows/CI.yml | 4 +- CHANGES.md | 15 + LICENSE.md | 2 +- ...al_6_3_3Dsedimentation_erosion_rates.ipynb | 7675 ----------------- ...rial_6_1_sedimentation_erosion_rates.ipynb | 332 +- .../Tutorial_6_2_diffusive_surface.ipynb | 331 +- ...al_6_3_3Dsedimentation_erosion_rates.ipynb | 517 ++ docs/development/docker/docker-builder.sh | 23 +- docs/development/docker/petsc/Dockerfile | 4 +- .../development/docker/underworld2/Dockerfile | 12 +- docs/install_guides/setonix_baremetal.sh | 35 +- docs/test/UWGeodynamics/image_tests.py | 4 +- underworld/UWGeodynamics/_melt.py | 4 +- underworld/UWGeodynamics/_model.py | 6 + underworld/UWGeodynamics/surfaceProcesses.py | 842 +- underworld/_version.py | 2 +- 16 files changed, 1255 insertions(+), 8553 deletions(-) delete mode 100644 docs/UWGeodynamics/examples/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb create mode 100644 docs/UWGeodynamics/tutorials/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 48745081e..32692438f 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -6,8 +6,8 @@ on: workflow_dispatch: env: - PETSC_VERSION: 3.18.1 - UW_VERSION: 2.14.0 + PETSC_VERSION: 3.19.4 + UW_VERSION: 2.15.0 OMPI_VERSION: 4.1.4 MPICH_VERSION: 3.4.3 diff --git a/CHANGES.md b/CHANGES.md index 7eec9a2aa..fa95f61a6 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,6 +1,21 @@ CHANGES: Underworld2 ======================= +Release 2.15.0 [2023-04-19] +--------------------------- +New: + * Move to Petsc-3.19.4 + * New 3D free surface implementation. (Not fully tested). + * new install guides for Gadi and setonix. + +Changes: + +Fixes: + * UWGeodynamics - add dynamic heating back into the advection diffusion solver, + https://github.com/underworldcode/underworld2/issues/669 + * Using updated Badlands-2.2.3 without license issue. + + Release 2.14 [2022-11-29] --------------------------- New: diff --git a/LICENSE.md b/LICENSE.md index 37132a214..d528e76d2 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -16,7 +16,7 @@ Underworld has been in development since 2003. It has always been released under ### Copyright holders -Copyright Australian National University, 2020-2022 +Copyright Australian National University, 2020-2023 Copyright Melbourne University, 2014-2021 Copyright Monash University, 2003-2021 Copyright VPAC, 2003-2009 diff --git a/docs/UWGeodynamics/examples/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb b/docs/UWGeodynamics/examples/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb deleted file mode 100644 index 6168b3bcc..000000000 --- a/docs/UWGeodynamics/examples/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb +++ /dev/null @@ -1,7675 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial 6: Simple Surface Processes" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loaded rc file /Users/benknight/Documents/Research/PyVenv/UW2/lib/python3.10/site-packages/underworld/UWGeodynamics/uwgeo-data/uwgeodynamicsrc\n" - ] - } - ], - "source": [ - "from underworld import UWGeodynamics as GEO\n", - "from underworld import visualisation as vis" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "u = GEO.UnitRegistry" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Characteristic values of the system\n", - "half_rate = 1.8 * u.centimeter / u.year\n", - "model_length = 360e3 * u.meter\n", - "model_height = 120e3 * u.meter\n", - "refViscosity = 1e24 * u.pascal * u.second\n", - "surfaceTemp = 273.15 * u.degK\n", - "baseModelTemp = 1603.15 * u.degK\n", - "bodyforce = 3300 * u.kilogram / u.metre**3 * 9.81 * u.meter / u.second**2\n", - "\n", - "KL = model_length\n", - "Kt = KL / half_rate\n", - "KM = bodyforce * KL**2 * Kt**2\n", - "KT = (baseModelTemp - surfaceTemp)\n", - "\n", - "GEO.scaling_coefficients[\"[length]\"] = KL\n", - "GEO.scaling_coefficients[\"[time]\"] = Kt\n", - "GEO.scaling_coefficients[\"[mass]\"]= KM\n", - "GEO.scaling_coefficients[\"[temperature]\"] = KT" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\tGlobal element size: 32x32x32\n", - "\tLocal offset of rank 0: 0x0x0\n", - "\tLocal range of rank 0: 32x32x32\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n" - ] - } - ], - "source": [ - "Model = GEO.Model(elementRes=(32, 32, 32), \n", - " minCoord=(0. * u.kilometer, 0. * u.kilometer, -110. * u.kilometer), \n", - " maxCoord=(120. * u.kilometer, 120. * u.kilometer, 10. * u.kilometer), \n", - " gravity=(0.0, 0.0, -9.81 * u.meter / u.second**2))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "Model.outputDir=\"outputs_tutorial6.3_velSP_3D\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "Model.diffusivity = 1e-6 * u.metre**2 / u.second \n", - "Model.capacity = 1000. * u.joule / (u.kelvin * u.kilogram)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "air = Model.add_material(name=\"Air\", shape=GEO.shapes.Layer3D(top=Model.top, bottom=0.0 * u.kilometer))\n", - "# stickyAir = Model.add_material(name=\"StickyAir\", shape=GEO.shapes.Layer2D(top=air.bottom, bottom= 0.0 * u.kilometer))\n", - "uppercrust = Model.add_material(name=\"UppperCrust\", shape=GEO.shapes.Layer3D(top=air.bottom, bottom=-35.0 * u.kilometer))\n", - "mantleLithosphere = Model.add_material(name=\"MantleLithosphere\", shape=GEO.shapes.Layer3D(top=uppercrust.bottom, bottom=-100.0 * u.kilometer))\n", - "mantle = Model.add_material(name=\"Mantle\", shape=GEO.shapes.Layer3D(top=mantleLithosphere.bottom, bottom=Model.bottom))\n", - "sediment = Model.add_material(name=\"Sediment\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "air.diffusivity = 1.0e-6 * u.metre**2 / u.second\n", - "air.capacity = 100. * u.joule / (u.kelvin * u.kilogram)\n", - "\n", - "# stickyAir.diffusivity = 1.0e-6 * u.metre**2 / u.second\n", - "# stickyAir.capacity = 100. * u.joule / (u.kelvin * u.kilogram)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "air.density = 1. * u.kilogram / u.metre**3\n", - "# stickyAir.density = 1. * u.kilogram / u.metre**3\n", - "uppercrust.density = GEO.LinearDensity(reference_density=2620. * u.kilogram / u.metre**3)\n", - "mantleLithosphere.density = GEO.LinearDensity(reference_density=3370. * u.kilogram / u.metre**3)\n", - "mantle.density = GEO.LinearDensity(reference_density=3370. * u.kilogram / u.metre**3)\n", - "sediment.density = GEO.LinearDensity(reference_density=2300. * u.kilogram / u.metre**3)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "uppercrust.radiogenicHeatProd = 0.7 * u.microwatt / u.meter**3\n", - "sediment.radiogenicHeatProd = 0.7 * u.microwatt / u.meter**3\n", - "mantleLithosphere.radiogenicHeatProd = 0.02 * u.microwatt / u.meter**3" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "rh = GEO.ViscousCreepRegistry()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "air.viscosity = 1e19 * u.pascal * u.second\n", - "# stickyAir.viscosity = 1e20 * u.pascal * u.second\n", - "uppercrust.viscosity = 1 * rh.Wet_Quartz_Dislocation_Gleason_and_Tullis_1995\n", - "mantleLithosphere.viscosity = rh.Dry_Olivine_Dislocation_Karato_and_Wu_1993\n", - "mantle.viscosity = 0.2 * rh.Dry_Olivine_Dislocation_Karato_and_Wu_1993\n", - "sediment.viscosity = rh.Wet_Quartz_Dislocation_Gleason_and_Tullis_1995" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "plasticity = GEO.DruckerPrager(cohesion=20.0 * u.megapascal,\n", - " cohesionAfterSoftening=20 * u.megapascal,\n", - " frictionCoefficient=0.12,\n", - " frictionAfterSoftening=0.02,\n", - " epsilon1=0.5,\n", - " epsilon2=1.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "uppercrust.plasticity = plasticity\n", - "mantleLithosphere.plasticity = plasticity\n", - "mantle.plasticity = plasticity\n", - "sediment.plasticity = plasticity" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Temperature Boundary Condition" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Model.set_temperatureBCs(top=293.15 * u.degK, \n", - " bottom=1603.15 * u.degK, \n", - " materials=[(mantle, 1603.15 * u.degK), (air, 293.15 * u.degK)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Velocity Boundary Conditions" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "1.9012852688417368 meter3/second" - ], - "text/latex": [ - "$1.9012852688417368\\ \\frac{\\mathrm{meter}^{3}}{\\mathrm{second}}$" - ], - "text/plain": [ - "1.9012852688417368 " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vel = 2.5 * u.centimeter / u.year\n", - "\n", - "\n", - "\n", - "vol_out = 2*(vel*(air.top - air.bottom)*Model.maxCoord[1]).to_base_units()\n", - "vol_out" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "1.320336992251206×10-10 meter/second" - ], - "text/latex": [ - "$1.320336992251206\\times 10^{-10}\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$" - ], - "text/plain": [ - "1.320336992251206e-10 " - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "### Velocity at surface to replace air that gets removed at sides\n", - "vel_in = vol_out / (Model.maxCoord[0] * Model.maxCoord[1])\n", - "vel_in.to_base_units()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Model.set_velocityBCs(left = [-vel, None, None],\n", - " right=[vel, None, None],\n", - " front=[None, 0.0, None], back=[None, 0.0, None],\n", - " top = [None, None, -1*vel_in],\n", - " bottom = [None, None, None])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initial Damage" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "def gaussian(xx, centre, width):\n", - " return ( np.exp( -(xx - centre)**2 / width ))\n", - "\n", - "maxDamage = 0.7\n", - "Model.plasticStrain.data[:] = 0.\n", - "Model.plasticStrain.data[:] = maxDamage * np.random.rand(*Model.plasticStrain.data.shape[:])\n", - "Model.plasticStrain.data[:,0] *= gaussian(Model.swarm.particleCoordinates.data[:,0], (GEO.nd(Model.maxCoord[0] - Model.minCoord[0])) / 2.0, GEO.nd(5.0 * u.kilometer))\n", - "Model.plasticStrain.data[:,0] *= gaussian(Model.swarm.particleCoordinates.data[:,2], GEO.nd(-35. * u.kilometer) , GEO.nd(5.0 * u.kilometer))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if GEO.nProcs == 1:\n", - " Fig = vis.Figure(resolution=(1200,600))\n", - " Fig.Surface(Model.mesh, Model.plasticStrain, cullface=False, opacity=0.5)\n", - " Fig.window()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if GEO.nProcs == 1:\n", - " Fig = vis.Figure(resolution=(1200,600))\n", - " Fig.Surface(Model.mesh, Model.materialField, cullface=False, opacity=0.5)\n", - " Fig.window()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### x and y coordinates for the surface" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/0v/6_65d96s7sgggt8x4wm2qqz40000gn/T/ipykernel_51205/2453750398.py:9: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " coords[:,0] = xi.flatten()\n", - "/var/folders/0v/6_65d96s7sgggt8x4wm2qqz40000gn/T/ipykernel_51205/2453750398.py:10: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " coords[:,1] = yi.flatten()\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "x = np.linspace(Model.minCoord[0], Model.maxCoord[0], 2*(Model.mesh.elementRes[0]+1))\n", - "y = np.linspace(Model.minCoord[1], Model.maxCoord[1], 2*(Model.mesh.elementRes[1]+1))\n", - "\n", - "xi, yi = np.meshgrid(x, y)\n", - "\n", - "coords = np.zeros(shape=(xi.flatten().shape[0], 3))\n", - "coords[:,0] = xi.flatten()\n", - "coords[:,1] = yi.flatten()\n", - "coords[:,2] = np.zeros_like(coords[:,0]) ### or any array with same shape as x and y coords with the initial height\n", - "\n", - "### add back in the dim\n", - "coords = coords * u.kilometer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Include erosion and sedimentation rates in model runs\n", - "\n", - "A branching condition is used to create erosion and sedimentation rates that can vary across the domain" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "ve_conditions = fn.branching.conditional([((Model.y >= GEO.nd(Model.maxCoord[1])/2.), GEO.nd(2.5 * u.millimeter/u.year)),\n", - " (True, GEO.nd(0.0 * u.millimeter/u.year))])\n", - "\n", - "vs_conditions = fn.branching.conditional([((Model.y >= GEO.nd(Model.maxCoord[1])/2.), GEO.nd(2.5 * u.millimeter/u.year)),\n", - " (True, GEO.nd(0.0 * u.millimeter/u.year))])\n", - "\n", - "Model.surfaceProcesses = GEO.surfaceProcesses.velocitySurface3D(airIndex = air.index,\n", - " sedimentIndex= sediment.index,\n", - " surfaceArray = coords, ### grid with surface points (x, y, z)\n", - " vs_condition = vs_conditions, ### sedimentation rate at each grid point\n", - " ve_condition = ve_conditions, ### erosion rate at each grid point\n", - " surfaceElevation=air.bottom)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UNSUPPORTED (log once): POSSIBLE ISSUE: unit 0 GLD_TEXTURE_INDEX_2D is unloadable and bound to sampler type (Float) - using zero texture because texture unloadable\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from underworld import visualisation as vis\n", - "Fig = vis.Figure(figsize=(1200,400))\n", - "Fig.Points(Model.surface_tracers, Model.surface_tracers.ve, fn_size=5)\n", - "Fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from underworld import visualisation as vis\n", - "Fig = vis.Figure(figsize=(1200,400))\n", - "Fig.Points(Model.surface_tracers, Model.surface_tracers.vs, fn_size=5)\n", - "Fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (ZZFERR92__system-execute) \n", - "Linear solver (ZZFERR92__system-execute), solution time 2.955800e-02 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (ZZFERR92__system-execute) \n", - "Linear solver (ZZFERR92__system-execute), solution time 2.844200e-02 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.002119 - Tolerance = 0.01\n", - "Non linear solver - Residual 2.11901424e-03; Tolerance 1.0000e-02 - Converged - 1.293648e+00 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n" - ] - } - ], - "source": [ - "Model.init_model(temperature=\"steady-state\", pressure=\"lithostatic\")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "GEO.rcParams['initial.nonlinear.min.iterations'] = 1\n", - "GEO.rcParams['nonlinear.min.iterations'] = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "Model.solver.set_inner_method(\"mumps\")\n", - "Model.solver.set_penalty(1e6)\n", - "GEO.rcParams[\"initial.nonlinear.tolerance\"] = 1e-2" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Options: -Q22_pc_type gkgdiag -force_correction True -ksp_type bsscr -pc_type none -ksp_k2_type NULL -rescale_equations False -remove_constant_pressure_null_space False -change_backsolve False -change_A11rhspresolve False -restore_K False -A11_ksp_type preonly -A11_pc_type lu -A11_pc_factor_mat_solver_type mumps -scr_ksp_type fgmres -scr_ksp_rtol 1e-05 -A11_mg_active False\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.171495 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.1027 secs / 1 its\n", - " Pressure Solve: = 0.2847 secs / 4 its\n", - " Final V Solve: = 0.068 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.718435 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.721294e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.168145 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09373 secs / 1 its\n", - " Pressure Solve: = 0.2802 secs / 4 its\n", - " Final V Solve: = 0.07041 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.529970 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.532689e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0012256 - Tolerance = 0.01\n", - "Non linear solver - Residual 1.22555406e-03; Tolerance 1.0000e-02 - Converged - 2.104792e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "Linear solver (8NOKC0TK__system-execute) \n", - "Linear solver (8NOKC0TK__system-execute), solution time 1.303713e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.1713 [min] / 2.1713 [max] (secs)\n", - "Time Integration - 2.17129 [min] / 2.17129 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35878.16795347183 year timestep: 11959.389317823941 year No. of its: 3\n", - "Step: 1 Model Time: 35878.2 year dt: 35878.2 year (2023-04-05 09:50:28)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166407 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2861 secs / 1 its\n", - " Pressure Solve: = 0.285 secs / 4 its\n", - " Final V Solve: = 0.06771 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.740254 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.743019e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166528 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.0694 secs / 1 its\n", - " Pressure Solve: = 0.2786 secs / 4 its\n", - " Final V Solve: = 0.06826 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.373084 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.375987e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0012828 - Tolerance = 0.01\n", - "Non linear solver - Residual 1.28280565e-03; Tolerance 1.0000e-02 - Converged - 2.268943e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (2H1P0HU4__system-execute) \n", - "Linear solver (2H1P0HU4__system-execute), solution time 1.337113e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2120 [min] / 2.2120 [max] (secs)\n", - "Time Integration - 2.212 [min] / 2.212 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35860.798892023544 year timestep: 11953.599630674516 year No. of its: 3\n", - "Step: 2 Model Time: 71739.0 year dt: 35860.8 year (2023-04-05 09:51:43)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166702 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.3102 secs / 1 its\n", - " Pressure Solve: = 0.3139 secs / 4 its\n", - " Final V Solve: = 0.09485 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 6.120426 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 6.123371e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.167393 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.08725 secs / 1 its\n", - " Pressure Solve: = 0.2792 secs / 4 its\n", - " Final V Solve: = 0.06928 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.446416 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.449036e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0013323 - Tolerance = 0.01\n", - "Non linear solver - Residual 1.33233913e-03; Tolerance 1.0000e-02 - Converged - 2.325578e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (W466UYST__system-execute) \n", - "Linear solver (W466UYST__system-execute), solution time 1.345633e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2142 [min] / 2.2142 [max] (secs)\n", - "Time Integration - 2.21424 [min] / 2.21424 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35826.61138573933 year timestep: 11942.203795246442 year No. of its: 3\n", - "Step: 3 Model Time: 107565.6 year dt: 35826.6 year (2023-04-05 09:52:59)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166597 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2513 secs / 1 its\n", - " Pressure Solve: = 0.2855 secs / 4 its\n", - " Final V Solve: = 0.06985 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.861868 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.864666e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166700 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.07991 secs / 1 its\n", - " Pressure Solve: = 0.2786 secs / 4 its\n", - " Final V Solve: = 0.06825 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.447676 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.450563e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0019589 - Tolerance = 0.01\n", - "Non linear solver - Residual 1.95888571e-03; Tolerance 1.0000e-02 - Converged - 2.299145e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (7E3WAWUH__system-execute) \n", - "Linear solver (7E3WAWUH__system-execute), solution time 1.332616e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2241 [min] / 2.2241 [max] (secs)\n", - "Time Integration - 2.22411 [min] / 2.22411 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35804.64927747243 year timestep: 11934.883092490812 year No. of its: 3\n", - "Step: 4 Model Time: 143370.2 year dt: 35804.6 year (2023-04-05 09:54:16)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.171201 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2233 secs / 1 its\n", - " Pressure Solve: = 0.2897 secs / 4 its\n", - " Final V Solve: = 0.07339 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.804313 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.806911e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.168559 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09938 secs / 1 its\n", - " Pressure Solve: = 0.2948 secs / 4 its\n", - " Final V Solve: = 0.07056 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.640584 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.643218e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0016503 - Tolerance = 0.01\n", - "Non linear solver - Residual 1.65033435e-03; Tolerance 1.0000e-02 - Converged - 2.310353e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (A977TEVV__system-execute) \n", - "Linear solver (A977TEVV__system-execute), solution time 1.332670e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2275 [min] / 2.2275 [max] (secs)\n", - "Time Integration - 2.22755 [min] / 2.22755 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35807.823832647286 year timestep: 11935.941277549096 year No. of its: 3\n", - "Step: 5 Model Time: 179178.1 year dt: 35807.8 year (2023-04-05 09:55:33)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166202 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09937 secs / 1 its\n", - " Pressure Solve: = 0.2779 secs / 4 its\n", - " Final V Solve: = 0.06776 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.497414 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.500058e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.167071 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09152 secs / 1 its\n", - " Pressure Solve: = 0.283 secs / 4 its\n", - " Final V Solve: = 0.06882 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.466163 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.468996e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0016409 - Tolerance = 0.01\n", - "Non linear solver - Residual 1.64085611e-03; Tolerance 1.0000e-02 - Converged - 2.258803e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (98YBMCQ2__system-execute) \n", - "Linear solver (98YBMCQ2__system-execute), solution time 1.333038e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2344 [min] / 2.2344 [max] (secs)\n", - "Time Integration - 2.23442 [min] / 2.23442 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35773.52683510561 year timestep: 11924.508945035202 year No. of its: 3\n", - "Step: 6 Model Time: 214951.6 year dt: 35773.5 year (2023-04-05 09:56:50)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166370 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09666 secs / 1 its\n", - " Pressure Solve: = 0.5688 secs / 4 its\n", - " Final V Solve: = 0.08457 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.847912 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.850526e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169129 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09621 secs / 1 its\n", - " Pressure Solve: = 0.2838 secs / 4 its\n", - " Final V Solve: = 0.07075 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.469030 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.472035e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0017643 - Tolerance = 0.01\n", - "Non linear solver - Residual 1.76429097e-03; Tolerance 1.0000e-02 - Converged - 2.297933e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (YN6U1UJ9__system-execute) \n", - "Linear solver (YN6U1UJ9__system-execute), solution time 1.329498e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2536 [min] / 2.2536 [max] (secs)\n", - "Time Integration - 2.25367 [min] / 2.25367 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35772.751745650734 year timestep: 11924.250581883576 year No. of its: 3\n", - "Step: 7 Model Time: 250724.3 year dt: 35772.8 year (2023-04-05 09:58:06)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166578 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2271 secs / 1 its\n", - " Pressure Solve: = 0.2803 secs / 4 its\n", - " Final V Solve: = 0.06978 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.692552 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.695178e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.167498 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.06985 secs / 1 its\n", - " Pressure Solve: = 0.2782 secs / 4 its\n", - " Final V Solve: = 0.06914 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.367407 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.370055e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0022809 - Tolerance = 0.01\n", - "Non linear solver - Residual 2.28093305e-03; Tolerance 1.0000e-02 - Converged - 2.272731e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (QVTLAC76__system-execute) \n", - "Linear solver (QVTLAC76__system-execute), solution time 1.134289e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2597 [min] / 2.2597 [max] (secs)\n", - "Time Integration - 2.2597 [min] / 2.2597 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35745.807921573156 year timestep: 11915.26930719105 year No. of its: 3\n", - "Step: 8 Model Time: 286470.1 year dt: 35745.8 year (2023-04-05 09:59:24)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169527 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2362 secs / 1 its\n", - " Pressure Solve: = 0.2808 secs / 4 its\n", - " Final V Solve: = 0.06812 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.790740 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.793443e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166745 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09754 secs / 1 its\n", - " Pressure Solve: = 0.2837 secs / 4 its\n", - " Final V Solve: = 0.06803 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.454708 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.457399e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0023624 - Tolerance = 0.01\n", - "Non linear solver - Residual 2.36242893e-03; Tolerance 1.0000e-02 - Converged - 2.292143e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (CVA0SWEA__system-execute) \n", - "Linear solver (CVA0SWEA__system-execute), solution time 1.323560e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2625 [min] / 2.2625 [max] (secs)\n", - "Time Integration - 2.26254 [min] / 2.26254 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35721.1248905711 year timestep: 11907.04163019037 year No. of its: 3\n", - "Step: 9 Model Time: 322191.3 year dt: 35721.1 year (2023-04-05 10:00:41)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169353 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2561 secs / 1 its\n", - " Pressure Solve: = 0.2788 secs / 4 its\n", - " Final V Solve: = 0.06896 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.836809 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.840051e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.171987 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.08977 secs / 1 its\n", - " Pressure Solve: = 0.2812 secs / 4 its\n", - " Final V Solve: = 0.06795 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.424167 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.426949e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0025853 - Tolerance = 0.01\n", - "Non linear solver - Residual 2.58533865e-03; Tolerance 1.0000e-02 - Converged - 2.293459e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (STSZ1LCL__system-execute) \n", - "Linear solver (STSZ1LCL__system-execute), solution time 1.315824e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2644 [min] / 2.2644 [max] (secs)\n", - "Time Integration - 2.26441 [min] / 2.26441 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35712.88468953164 year timestep: 11904.294896510548 year No. of its: 3\n", - "Step: 10 Model Time: 357904.1 year dt: 35712.9 year (2023-04-05 10:01:59)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169509 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2826 secs / 1 its\n", - " Pressure Solve: = 0.2782 secs / 4 its\n", - " Final V Solve: = 0.06851 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.995096 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.998521e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.165904 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.07079 secs / 1 its\n", - " Pressure Solve: = 0.2796 secs / 4 its\n", - " Final V Solve: = 0.06797 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.367260 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.369936e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0025289 - Tolerance = 0.01\n", - "Non linear solver - Residual 2.52890076e-03; Tolerance 1.0000e-02 - Converged - 2.303308e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (JXXJ56H3__system-execute) \n", - "Linear solver (JXXJ56H3__system-execute), solution time 1.313403e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2737 [min] / 2.2737 [max] (secs)\n", - "Time Integration - 2.2737 [min] / 2.2737 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35738.62678758263 year timestep: 11912.875595860878 year No. of its: 3\n", - "Step: 11 Model Time: 393642.8 year dt: 35738.6 year (2023-04-05 10:03:16)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166210 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2454 secs / 1 its\n", - " Pressure Solve: = 0.278 secs / 4 its\n", - " Final V Solve: = 0.06781 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.767819 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.771199e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166203 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09197 secs / 1 its\n", - " Pressure Solve: = 0.2779 secs / 4 its\n", - " Final V Solve: = 0.06822 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.489954 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.492759e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0032071 - Tolerance = 0.01\n", - "Non linear solver - Residual 3.20705531e-03; Tolerance 1.0000e-02 - Converged - 2.292220e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (PJAHIXRW__system-execute) \n", - "Linear solver (PJAHIXRW__system-execute), solution time 1.312330e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2939 [min] / 2.2939 [max] (secs)\n", - "Time Integration - 2.29392 [min] / 2.29392 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35685.55182019167 year timestep: 11895.183940063891 year No. of its: 3\n", - "Step: 12 Model Time: 429328.3 year dt: 35685.6 year (2023-04-05 10:04:34)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166406 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09857 secs / 1 its\n", - " Pressure Solve: = 0.3345 secs / 4 its\n", - " Final V Solve: = 0.06774 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.584797 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.587599e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.171990 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.1619 secs / 1 its\n", - " Pressure Solve: = 0.2909 secs / 4 its\n", - " Final V Solve: = 0.06857 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.792348 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.795498e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0043815 - Tolerance = 0.01\n", - "Non linear solver - Residual 4.38146515e-03; Tolerance 1.0000e-02 - Converged - 2.303248e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (8CYB2ADJ__system-execute) \n", - "Linear solver (8CYB2ADJ__system-execute), solution time 1.311725e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.2918 [min] / 2.2918 [max] (secs)\n", - "Time Integration - 2.29178 [min] / 2.29178 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35684.01274095162 year timestep: 11894.67091365054 year No. of its: 3\n", - "Step: 13 Model Time: 465012.3 year dt: 35684.0 year (2023-04-05 10:05:52)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169022 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2671 secs / 1 its\n", - " Pressure Solve: = 0.2784 secs / 4 its\n", - " Final V Solve: = 0.06849 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.848658 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.851517e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.170198 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.0753 secs / 1 its\n", - " Pressure Solve: = 0.2794 secs / 4 its\n", - " Final V Solve: = 0.06778 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.453882 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.456862e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0034359 - Tolerance = 0.01\n", - "Non linear solver - Residual 3.43585901e-03; Tolerance 1.0000e-02 - Converged - 2.295679e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (2Z4SNU0G__system-execute) \n", - "Linear solver (2Z4SNU0G__system-execute), solution time 1.121836e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.4650 [min] / 2.4650 [max] (secs)\n", - "Time Integration - 2.46502 [min] / 2.46502 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 34987.66122748742 year timestep: 11662.553742495806 year No. of its: 3\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Step: 14 Model Time: 500000.0 year dt: 34987.7 year (2023-04-05 10:08:22)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169989 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.1569 secs / 1 its\n", - " Pressure Solve: = 0.279 secs / 4 its\n", - " Final V Solve: = 0.06776 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.657251 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.660377e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.167942 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.115 secs / 1 its\n", - " Pressure Solve: = 0.2803 secs / 4 its\n", - " Final V Solve: = 0.06823 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.535181 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.538108e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0033838 - Tolerance = 0.01\n", - "Non linear solver - Residual 3.38377657e-03; Tolerance 1.0000e-02 - Converged - 2.288806e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "Linear solver (LVIH5KWG__system-execute) \n", - "Linear solver (LVIH5KWG__system-execute), solution time 1.306584e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3039 [min] / 2.3039 [max] (secs)\n", - "Time Integration - 2.30387 [min] / 2.30387 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35660.719828067195 year timestep: 11886.906609355732 year No. of its: 3\n", - "Step: 15 Model Time: 535660.7 year dt: 35660.7 year (2023-04-05 10:09:34)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169874 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.1005 secs / 1 its\n", - " Pressure Solve: = 0.2793 secs / 4 its\n", - " Final V Solve: = 0.06819 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.537713 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.540791e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169425 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.07801 secs / 1 its\n", - " Pressure Solve: = 0.2781 secs / 4 its\n", - " Final V Solve: = 0.06799 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.473730 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.476558e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0044451 - Tolerance = 0.01\n", - "Non linear solver - Residual 4.44513803e-03; Tolerance 1.0000e-02 - Converged - 2.268810e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (CMPT0031__system-execute) \n", - "Linear solver (CMPT0031__system-execute), solution time 1.306552e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3102 [min] / 2.3102 [max] (secs)\n", - "Time Integration - 2.31024 [min] / 2.31024 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35750.72216187863 year timestep: 11916.907387292878 year No. of its: 3\n", - "Step: 16 Model Time: 571411.4 year dt: 35750.7 year (2023-04-05 10:10:52)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.166145 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.1014 secs / 1 its\n", - " Pressure Solve: = 0.2794 secs / 4 its\n", - " Final V Solve: = 0.06788 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.528290 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.531231e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169412 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.07648 secs / 1 its\n", - " Pressure Solve: = 0.2786 secs / 4 its\n", - " Final V Solve: = 0.06815 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.463065 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.465980e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0041371 - Tolerance = 0.01\n", - "Non linear solver - Residual 4.13705533e-03; Tolerance 1.0000e-02 - Converged - 2.265934e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (IBD8R01V__system-execute) \n", - "Linear solver (IBD8R01V__system-execute), solution time 1.104455e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3139 [min] / 2.3139 [max] (secs)\n", - "Time Integration - 2.3139 [min] / 2.3139 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35927.741271497325 year timestep: 11975.913757165774 year No. of its: 3\n", - "Step: 17 Model Time: 607339.2 year dt: 35927.7 year (2023-04-05 10:12:10)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169220 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.08978 secs / 1 its\n", - " Pressure Solve: = 0.2798 secs / 4 its\n", - " Final V Solve: = 0.06787 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.464635 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.467176e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.167692 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.1016 secs / 1 its\n", - " Pressure Solve: = 0.2782 secs / 4 its\n", - " Final V Solve: = 0.06799 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.496302 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.499325e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0056713 - Tolerance = 0.01\n", - "Non linear solver - Residual 5.67130155e-03; Tolerance 1.0000e-02 - Converged - 2.265796e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (XIIZHSOW__system-execute) \n", - "Linear solver (XIIZHSOW__system-execute), solution time 1.300207e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3241 [min] / 2.3241 [max] (secs)\n", - "Time Integration - 2.32411 [min] / 2.32411 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35790.000489788785 year timestep: 11930.000163262926 year No. of its: 3\n", - "Step: 18 Model Time: 643129.2 year dt: 35790.0 year (2023-04-05 10:13:28)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169027 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2018 secs / 1 its\n", - " Pressure Solve: = 0.2852 secs / 4 its\n", - " Final V Solve: = 0.06974 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.771793 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.774760e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.167600 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2937 secs / 1 its\n", - " Pressure Solve: = 0.2943 secs / 4 its\n", - " Final V Solve: = 0.0694 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 6.703426 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 6.706127e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0053134 - Tolerance = 0.01\n", - "Non linear solver - Residual 5.31335874e-03; Tolerance 1.0000e-02 - Converged - 2.420875e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (0CA6SJ1C__system-execute) \n", - "Linear solver (0CA6SJ1C__system-execute), solution time 1.298870e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3321 [min] / 2.3321 [max] (secs)\n", - "Time Integration - 2.33214 [min] / 2.33214 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35862.55843736162 year timestep: 11954.186145787206 year No. of its: 3\n", - "Step: 19 Model Time: 678991.7 year dt: 35862.6 year (2023-04-05 10:14:49)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169530 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.153 secs / 1 its\n", - " Pressure Solve: = 0.6394 secs / 4 its\n", - " Final V Solve: = 0.08154 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 6.088947 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 6.092151e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.182620 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.07339 secs / 1 its\n", - " Pressure Solve: = 0.2801 secs / 4 its\n", - " Final V Solve: = 0.07465 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.565686 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.568381e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0039977 - Tolerance = 0.01\n", - "Non linear solver - Residual 3.99772688e-03; Tolerance 1.0000e-02 - Converged - 2.353369e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (MWWWF1MK__system-execute) \n", - "Linear solver (MWWWF1MK__system-execute), solution time 1.296625e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.4113 [min] / 2.4113 [max] (secs)\n", - "Time Integration - 2.41129 [min] / 2.41129 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 36040.01899553787 year timestep: 12013.339665179288 year No. of its: 3\n", - "Step: 20 Model Time: 715031.8 year dt: 36040.0 year (2023-04-05 10:16:09)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169451 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.1562 secs / 1 its\n", - " Pressure Solve: = 0.283 secs / 4 its\n", - " Final V Solve: = 0.06869 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.905357 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.908152e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.171654 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.1078 secs / 1 its\n", - " Pressure Solve: = 0.3201 secs / 4 its\n", - " Final V Solve: = 0.06849 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.608238 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.610985e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.004129 - Tolerance = 0.01\n", - "Non linear solver - Residual 4.12898231e-03; Tolerance 1.0000e-02 - Converged - 2.334093e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (Z57E4Y8Q__system-execute) \n", - "Linear solver (Z57E4Y8Q__system-execute), solution time 1.096846e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3411 [min] / 2.3411 [max] (secs)\n", - "Time Integration - 2.34115 [min] / 2.34115 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35974.39888171994 year timestep: 11991.466293906646 year No. of its: 3\n", - "Step: 21 Model Time: 751006.2 year dt: 35974.4 year (2023-04-05 10:17:29)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169429 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2336 secs / 1 its\n", - " Pressure Solve: = 0.2797 secs / 4 its\n", - " Final V Solve: = 0.06844 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.701731 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.704809e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.170179 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.07069 secs / 1 its\n", - " Pressure Solve: = 0.295 secs / 4 its\n", - " Final V Solve: = 0.0684 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.413703 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.416049e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0086994 - Tolerance = 0.01\n", - "Non linear solver - Residual 8.69939897e-03; Tolerance 1.0000e-02 - Converged - 2.283755e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (KKYUU07W__system-execute) \n", - "Linear solver (KKYUU07W__system-execute), solution time 1.292824e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3538 [min] / 2.3538 [max] (secs)\n", - "Time Integration - 2.35383 [min] / 2.35383 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35885.566653082875 year timestep: 11961.855551027624 year No. of its: 3\n", - "Step: 22 Model Time: 786891.7 year dt: 35885.6 year (2023-04-05 10:18:48)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169815 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2661 secs / 1 its\n", - " Pressure Solve: = 0.2872 secs / 4 its\n", - " Final V Solve: = 0.06992 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.773679 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.776657e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.168849 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.07072 secs / 1 its\n", - " Pressure Solve: = 0.2788 secs / 4 its\n", - " Final V Solve: = 0.0697 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.469585 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.472319e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0061704 - Tolerance = 0.01\n", - "Non linear solver - Residual 6.17036525e-03; Tolerance 1.0000e-02 - Converged - 2.296337e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (3SCDWV6H__system-execute) \n", - "Linear solver (3SCDWV6H__system-execute), solution time 1.288559e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3557 [min] / 2.3557 [max] (secs)\n", - "Time Integration - 2.3557 [min] / 2.3557 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 35973.67371639627 year timestep: 11991.224572132089 year No. of its: 3\n", - "Step: 23 Model Time: 822865.4 year dt: 35973.7 year (2023-04-05 10:20:07)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169866 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.1177 secs / 1 its\n", - " Pressure Solve: = 0.2803 secs / 4 its\n", - " Final V Solve: = 0.06879 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.555409 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.558369e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.181931 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2717 secs / 1 its\n", - " Pressure Solve: = 0.2907 secs / 4 its\n", - " Final V Solve: = 0.07527 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.995246 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.998179e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0054739 - Tolerance = 0.01\n", - "Non linear solver - Residual 5.47387544e-03; Tolerance 1.0000e-02 - Converged - 2.341524e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (FMV6EHQA__system-execute) \n", - "Linear solver (FMV6EHQA__system-execute), solution time 1.297947e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3673 [min] / 2.3673 [max] (secs)\n", - "Time Integration - 2.36727 [min] / 2.36727 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 36188.96769893627 year timestep: 12062.989232978758 year No. of its: 3\n", - "Step: 24 Model Time: 859054.4 year dt: 36189.0 year (2023-04-05 10:21:27)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169396 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09842 secs / 1 its\n", - " Pressure Solve: = 0.2815 secs / 4 its\n", - " Final V Solve: = 0.06902 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.537754 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.540452e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169552 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09944 secs / 1 its\n", - " Pressure Solve: = 0.2796 secs / 4 its\n", - " Final V Solve: = 0.06916 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.473611 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.476382e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0046006 - Tolerance = 0.01\n", - "Non linear solver - Residual 4.60057741e-03; Tolerance 1.0000e-02 - Converged - 2.274588e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (CQXNBXS0__system-execute) \n", - "Linear solver (CQXNBXS0__system-execute), solution time 1.283669e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3692 [min] / 2.3692 [max] (secs)\n", - "Time Integration - 2.3692 [min] / 2.3692 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 36034.62576069727 year timestep: 12011.541920232423 year No. of its: 3\n", - "Step: 25 Model Time: 895089.0 year dt: 36034.6 year (2023-04-05 10:22:47)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169314 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.09969 secs / 1 its\n", - " Pressure Solve: = 0.2796 secs / 4 its\n", - " Final V Solve: = 0.06806 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.539354 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.542118e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169263 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.08544 secs / 1 its\n", - " Pressure Solve: = 0.2776 secs / 4 its\n", - " Final V Solve: = 0.06814 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.467981 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.470691e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0057267 - Tolerance = 0.01\n", - "Non linear solver - Residual 5.72670513e-03; Tolerance 1.0000e-02 - Converged - 2.277180e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (SC2V10YO__system-execute) \n", - "Linear solver (SC2V10YO__system-execute), solution time 1.285613e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3749 [min] / 2.3749 [max] (secs)\n", - "Time Integration - 2.37496 [min] / 2.37496 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 36203.03147596533 year timestep: 12067.677158655113 year No. of its: 3\n", - "Step: 26 Model Time: 931292.0 year dt: 36203.0 year (2023-04-05 10:24:07)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169353 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.2446 secs / 1 its\n", - " Pressure Solve: = 0.2787 secs / 4 its\n", - " Final V Solve: = 0.06793 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.730627 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.733745e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169365 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.06872 secs / 1 its\n", - " Pressure Solve: = 0.2787 secs / 4 its\n", - " Final V Solve: = 0.06886 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.392642 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.395354e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0079067 - Tolerance = 0.01\n", - "Non linear solver - Residual 7.90673240e-03; Tolerance 1.0000e-02 - Converged - 2.288925e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (OKP8ABUG__system-execute) \n", - "Linear solver (OKP8ABUG__system-execute), solution time 1.083139e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3784 [min] / 2.3784 [max] (secs)\n", - "Time Integration - 2.37839 [min] / 2.37839 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 36461.86287508795 year timestep: 12153.954291695984 year No. of its: 3\n", - "Step: 27 Model Time: 967753.9 year dt: 36461.9 year (2023-04-05 10:25:27)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.169014 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.3 secs / 1 its\n", - " Pressure Solve: = 0.2786 secs / 4 its\n", - " Final V Solve: = 0.06831 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.861719 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.864796e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.168154 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.07256 secs / 1 its\n", - " Pressure Solve: = 0.2781 secs / 4 its\n", - " Final V Solve: = 0.06797 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.398905 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.401686e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0082829 - Tolerance = 0.01\n", - "Non linear solver - Residual 8.28286224e-03; Tolerance 1.0000e-02 - Converged - 2.300758e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Linear solver (72W1CMI2__system-execute) \n", - "Linear solver (72W1CMI2__system-execute), solution time 1.281955e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3831 [min] / 2.3831 [max] (secs)\n", - "Time Integration - 2.38314 [min] / 2.38314 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 32246.11175398267 year timestep: 10748.703917994224 year No. of its: 3\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "Step: 28 Model Time: 1.0 megayear dt: 32246.1 year (2023-04-05 10:28:02)\n", - "In SystemLinearEquations_NonLinearExecute\n", - "\n", - "Non linear solver - iteration 0\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.168851 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.1985 secs / 1 its\n", - " Pressure Solve: = 0.2792 secs / 4 its\n", - " Final V Solve: = 0.06802 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.680149 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.682863e+00 (secs)\n", - "Non linear solver - iteration 1\n", - "Linear solver (WG3GI2FG__system-execute) \n", - "\n", - "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", - "\n", - "\n", - "----- K2_GMG ------\n", - "\n", - "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 1000000.000000\n", - "\n", - "\n", - "\t* K+p*K2 in time: 0.168393 seconds\n", - "\n", - " Setting schur_pc to \"gkgdiag\" \n", - "\n", - "\n", - "SCR Solver Summary:\n", - "\n", - " RHS V Solve: = 0.07429 secs / 1 its\n", - " Pressure Solve: = 0.2793 secs / 4 its\n", - " Final V Solve: = 0.06832 secs / 1 its\n", - "\n", - " Total BSSCR Linear solve time: 5.412282 seconds\n", - "\n", - "Linear solver (WG3GI2FG__system-execute), solution time 5.414968e+00 (secs)\n", - "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.0064635 - Tolerance = 0.01\n", - "Non linear solver - Residual 6.46345976e-03; Tolerance 1.0000e-02 - Converged - 2.286605e+01 (secs)\n", - "\n", - "In func SystemLinearEquations_NonLinearExecute: Converged after 1 iterations.\n", - "Linear solver (99A762O3__system-execute) \n", - "Linear solver (99A762O3__system-execute), solution time 1.275735e+00 (secs)\n", - "Time Integration\n", - "\t2nd order: 3XI2C93P__integrand - 2.3716 [min] / 2.3716 [max] (secs)\n", - "Time Integration - 2.37161 [min] / 2.37161 [max] (secs)\n", - "In func WeightsCalculator_CalculateAll(): for swarm \"JM5Y7ZPD__swarm\"\n", - "\tdone 33% (10923 cells)...\n", - "\tdone 67% (21846 cells)...\n", - "\tdone 100% (32768 cells)...\n", - "WeightsCalculator_CalculateAll(): finished update of weights for swarm \"JM5Y7ZPD__swarm\"\n", - "SP total time: 9999.999999999869 year timestep: 9999.999999999869 year No. of its: 1\n", - "Step: 29 Model Time: 1.0 megayear dt: 10000.0 year (2023-04-05 10:29:15)\n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Model.run_for(1.01 * u.megayear, checkpoint_interval=0.5*u.megayears)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/benknight/Documents/Research/PyVenv/UW2/lib/python3.10/site-packages/numpy/ma/core.py:2826: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " _data = np.array(data, dtype=dtype, copy=copy,\n", - "/Users/benknight/Documents/Research/PyVenv/UW2/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4339: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " c = np.asanyarray(c, dtype=float)\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if uw.mpi.rank == 0 :\n", - " import matplotlib.pyplot as plt\n", - " \n", - " \n", - " surface = GEO.dim(Model.surface_tracers.data, u.kilometer)\n", - "\n", - " scatter = plt.scatter(surface[:,0], surface[:,1], c=surface[:,2], s=10)\n", - "\n", - " cbar = plt.colorbar(scatter)\n", - " \n", - " cbar.set_label('Topo [km]')\n", - " \n", - " \n", - " plt.xlabel('x [km]')\n", - " plt.xlabel('y [km]')\n", - " \n", - " plt.show()\n", - " \n", - " \n", - " plt.plot()\n", - " \n", - " profile1 = surface[surface[:,1].m == 24]\n", - " profile2 = surface[surface[:,1].m == 96]\n", - " \n", - " plt.plot(profile1[:,0], profile1[:,2], label = 'No SP') \n", - " \n", - " plt.plot(profile2[:,0], profile2[:,2], label = 'SP')\n", - " \n", - " plt.xlabel('x [km]')\n", - " \n", - " plt.ylabel('Topo [km]')\n", - " \n", - " plt.legend()\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/UWGeodynamics/tutorials/Tutorial_6_1_sedimentation_erosion_rates.ipynb b/docs/UWGeodynamics/tutorials/Tutorial_6_1_sedimentation_erosion_rates.ipynb index 2462d6615..a5745b8f7 100644 --- a/docs/UWGeodynamics/tutorials/Tutorial_6_1_sedimentation_erosion_rates.ipynb +++ b/docs/UWGeodynamics/tutorials/Tutorial_6_1_sedimentation_erosion_rates.ipynb @@ -9,17 +9,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loaded rc file /opt/venv/lib/python3.7/site-packages/UWGeodynamics/uwgeo-data/uwgeodynamicsrc\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from underworld import UWGeodynamics as GEO\n", "from underworld import visualisation as vis" @@ -27,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -145,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -168,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -182,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -194,22 +186,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from underworld import visualisation as vis\n", "Fig = vis.Figure(figsize=(1200,400))\n", @@ -226,20 +205,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Model.set_temperatureBCs(top=293.15 * u.degK, \n", " bottom=1603.15 * u.degK, \n", @@ -255,20 +223,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Model.set_velocityBCs(left=[-2.5 * u.centimeter / u.year, None],\n", " right=[2.5 * u.centimeter / u.year, None],\n", @@ -285,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -309,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -332,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -352,22 +309,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.projPlasticStrain)\n", @@ -376,22 +320,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Points(Model.swarm, Model.materialField, fn_size=3.0)\n", @@ -400,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -409,22 +340,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.projViscosityField, logScale=True)\n", @@ -433,155 +351,24 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { - "scrolled": false + "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running with UWGeodynamics version 2.10.2\n", - "Options: -Q22_pc_type uw -ksp_type bsscr -pc_type none -ksp_k2_type NULL -rescale_equations False -remove_constant_pressure_null_space False -change_backsolve False -change_A11rhspresolve False -restore_K False -A11_ksp_type fgmres -A11_ksp_rtol 1e-06 -scr_ksp_type fgmres -scr_ksp_rtol 1e-05\n", - "SP total time: 41551.76 years, timestep: 6925.29 years, No. of its: 6\n", - "Step: 1 Model Time: 41551.8 year dt: 41551.8 year (2021-09-20 05:16:09)\n", - "SP total time: 41769.64 years, timestep: 6961.61 years, No. of its: 6\n", - "Step: 2 Model Time: 83321.4 year dt: 41769.6 year (2021-09-20 05:16:22)\n", - "SP total time: 41794.6 years, timestep: 6965.77 years, No. of its: 6\n", - "Step: 3 Model Time: 125116.0 year dt: 41794.6 year (2021-09-20 05:16:36)\n", - "SP total time: 41647.84 years, timestep: 6941.31 years, No. of its: 6\n", - "Step: 4 Model Time: 166763.8 year dt: 41647.8 year (2021-09-20 05:16:50)\n", - "SP total time: 41734.73 years, timestep: 6955.79 years, No. of its: 6\n", - "Step: 5 Model Time: 208498.6 year dt: 41734.7 year (2021-09-20 05:17:04)\n", - "SP total time: 41313.64 years, timestep: 6885.61 years, No. of its: 6\n", - "Step: 6 Model Time: 249812.2 year dt: 41313.6 year (2021-09-20 05:17:17)\n", - "SP total time: 41635.16 years, timestep: 6939.19 years, No. of its: 6\n", - "Step: 7 Model Time: 291447.4 year dt: 41635.2 year (2021-09-20 05:17:31)\n", - "SP total time: 41652.58 years, timestep: 6942.1 years, No. of its: 6\n", - "Step: 8 Model Time: 333100.0 year dt: 41652.6 year (2021-09-20 05:17:46)\n", - "SP total time: 41638.11 years, timestep: 6939.68 years, No. of its: 6\n", - "Step: 9 Model Time: 374738.1 year dt: 41638.1 year (2021-09-20 05:18:00)\n", - "SP total time: 41716.94 years, timestep: 6952.82 years, No. of its: 6\n", - "Step: 10 Model Time: 416455.0 year dt: 41716.9 year (2021-09-20 05:18:14)\n", - "SP total time: 41643.61 years, timestep: 6940.6 years, No. of its: 6\n", - "Step: 11 Model Time: 458098.6 year dt: 41643.6 year (2021-09-20 05:18:28)\n", - "SP total time: 41912.97 years, timestep: 6985.49 years, No. of its: 6\n", - "Step: 12 Model Time: 500011.6 year dt: 41913.0 year (2021-09-20 05:18:43)\n", - "SP total time: 41816.75 years, timestep: 6969.46 years, No. of its: 6\n", - "Step: 13 Model Time: 541828.3 year dt: 41816.8 year (2021-09-20 05:18:57)\n", - "SP total time: 42059.28 years, timestep: 7009.88 years, No. of its: 6\n", - "Step: 14 Model Time: 583887.6 year dt: 42059.3 year (2021-09-20 05:19:12)\n", - "SP total time: 41912.71 years, timestep: 6985.45 years, No. of its: 6\n", - "Step: 15 Model Time: 625800.3 year dt: 41912.7 year (2021-09-20 05:19:26)\n", - "SP total time: 41941.67 years, timestep: 6990.28 years, No. of its: 6\n", - "Step: 16 Model Time: 667742.0 year dt: 41941.7 year (2021-09-20 05:19:41)\n", - "SP total time: 41876.74 years, timestep: 6979.46 years, No. of its: 6\n", - "Step: 17 Model Time: 709618.7 year dt: 41876.7 year (2021-09-20 05:19:56)\n", - "SP total time: 42120.81 years, timestep: 7020.13 years, No. of its: 6\n", - "Step: 18 Model Time: 751739.5 year dt: 42120.8 year (2021-09-20 05:20:10)\n", - "SP total time: 42163.63 years, timestep: 7027.27 years, No. of its: 6\n", - "Step: 19 Model Time: 793903.2 year dt: 42163.6 year (2021-09-20 05:20:24)\n", - "SP total time: 41429.42 years, timestep: 6904.9 years, No. of its: 6\n", - "Step: 20 Model Time: 835332.6 year dt: 41429.4 year (2021-09-20 05:20:48)\n", - "SP total time: 41663.61 years, timestep: 6943.94 years, No. of its: 6\n", - "Step: 21 Model Time: 876996.2 year dt: 41663.6 year (2021-09-20 05:21:03)\n", - "SP total time: 41913.18 years, timestep: 6985.53 years, No. of its: 6\n", - "Step: 22 Model Time: 918909.4 year dt: 41913.2 year (2021-09-20 05:21:17)\n", - "SP total time: 41909.0 years, timestep: 6984.83 years, No. of its: 6\n", - "Step: 23 Model Time: 960818.4 year dt: 41909.0 year (2021-09-20 05:21:31)\n", - "SP total time: 39181.62 years, timestep: 7836.32 years, No. of its: 5\n", - "Step: 24 Model Time: 1.0 megayear dt: 39181.6 year (2021-09-20 05:22:16)\n", - "SP total time: 42258.84 years, timestep: 7043.14 years, No. of its: 6\n", - "Step: 25 Model Time: 1.0 megayear dt: 42258.8 year (2021-09-20 05:22:30)\n", - "SP total time: 41625.9 years, timestep: 6937.65 years, No. of its: 6\n", - "Step: 26 Model Time: 1.1 megayear dt: 41625.9 year (2021-09-20 05:22:59)\n", - "SP total time: 41690.95 years, timestep: 6948.49 years, No. of its: 6\n", - "Step: 27 Model Time: 1.1 megayear dt: 41690.9 year (2021-09-20 05:23:14)\n", - "SP total time: 42427.25 years, timestep: 7071.21 years, No. of its: 6\n", - "Step: 28 Model Time: 1.2 megayear dt: 42427.2 year (2021-09-20 05:23:31)\n", - "SP total time: 42650.37 years, timestep: 7108.39 years, No. of its: 6\n", - "Step: 29 Model Time: 1.2 megayear dt: 42650.4 year (2021-09-20 05:23:46)\n", - "SP total time: 42651.8 years, timestep: 7108.63 years, No. of its: 6\n", - "Step: 30 Model Time: 1.3 megayear dt: 42651.8 year (2021-09-20 05:24:01)\n", - "SP total time: 42329.86 years, timestep: 7054.98 years, No. of its: 6\n", - "Step: 31 Model Time: 1.3 megayear dt: 42329.9 year (2021-09-20 05:24:16)\n", - "SP total time: 42385.22 years, timestep: 7064.2 years, No. of its: 6\n", - "Step: 32 Model Time: 1.3 megayear dt: 42385.2 year (2021-09-20 05:24:31)\n", - "SP total time: 42458.07 years, timestep: 7076.34 years, No. of its: 6\n", - "Step: 33 Model Time: 1.4 megayear dt: 42458.1 year (2021-09-20 05:24:46)\n", - "SP total time: 42677.27 years, timestep: 7112.88 years, No. of its: 6\n", - "Step: 34 Model Time: 1.4 megayear dt: 42677.3 year (2021-09-20 05:25:01)\n", - "SP total time: 42919.47 years, timestep: 7153.25 years, No. of its: 6\n", - "Step: 35 Model Time: 1.5 megayear dt: 42919.5 year (2021-09-20 05:25:16)\n", - "SP total time: 43317.52 years, timestep: 7219.59 years, No. of its: 6\n", - "Step: 36 Model Time: 1.5 megayear dt: 43317.5 year (2021-09-20 05:25:31)\n", - "SP total time: 43134.31 years, timestep: 7189.05 years, No. of its: 6\n", - "Step: 37 Model Time: 1.6 megayear dt: 43134.3 year (2021-09-20 05:25:46)\n", - "SP total time: 42758.75 years, timestep: 7126.46 years, No. of its: 6\n", - "Step: 38 Model Time: 1.6 megayear dt: 42758.7 year (2021-09-20 05:26:02)\n", - "SP total time: 42871.85 years, timestep: 7145.31 years, No. of its: 6\n", - "Step: 39 Model Time: 1.6 megayear dt: 42871.9 year (2021-09-20 05:26:21)\n", - "SP total time: 44194.59 years, timestep: 7365.77 years, No. of its: 6\n", - "Step: 40 Model Time: 1.7 megayear dt: 44194.6 year (2021-09-20 05:26:43)\n", - "SP total time: 44520.91 years, timestep: 7420.15 years, No. of its: 6\n", - "Step: 41 Model Time: 1.7 megayear dt: 44520.9 year (2021-09-20 05:26:59)\n", - "SP total time: 44389.94 years, timestep: 7398.32 years, No. of its: 6\n", - "Step: 42 Model Time: 1.8 megayear dt: 44389.9 year (2021-09-20 05:27:15)\n", - "SP total time: 44142.17 years, timestep: 7357.03 years, No. of its: 6\n", - "Step: 43 Model Time: 1.8 megayear dt: 44142.2 year (2021-09-20 05:27:32)\n", - "SP total time: 44062.9 years, timestep: 7343.82 years, No. of its: 6\n", - "Step: 44 Model Time: 1.9 megayear dt: 44062.9 year (2021-09-20 05:27:49)\n", - "SP total time: 44105.35 years, timestep: 7350.89 years, No. of its: 6\n", - "Step: 45 Model Time: 1.9 megayear dt: 44105.3 year (2021-09-20 05:28:05)\n", - "SP total time: 44375.27 years, timestep: 7395.88 years, No. of its: 6\n", - "Step: 46 Model Time: 1.9 megayear dt: 44375.3 year (2021-09-20 05:28:22)\n", - "SP total time: 45323.12 years, timestep: 6474.73 years, No. of its: 7\n", - "Step: 47 Model Time: 2.0 megayear dt: 45323.1 year (2021-09-20 05:29:05)\n", - "SP total time: 6728.33 years, timestep: 6728.33 years, No. of its: 1\n", - "Step: 48 Model Time: 2.0 megayear dt: 6728.3 year (2021-09-20 05:29:52)\n", - "SP total time: 10000.0 years, timestep: 5000.0 years, No. of its: 2\n", - "Step: 49 Model Time: 2.0 megayear dt: 10000.0 year (2021-09-20 05:30:10)\n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Model.run_for(2.01 * u.megayear, checkpoint_interval=1.*u.megayears)" + "outputs": [], + "source": [ + "Model.run_for(duration=2.01 * u.megayear, checkpoint_interval=0.1*u.megayears)" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "from underworld import visualisation as vis\n", "Fig = vis.Figure(figsize=(1200,400))\n", "### output tracers\n", - "Fig.Points(Model.surfacetracers_tracers, pointSize=1.0)\n", + "Fig.Points(Model.surface_tracers, pointSize=1.0)\n", "\n", "# for line in lines:\n", "# Fig.Points(line, pointSize=2.0)\n", @@ -591,22 +378,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.strainRate_2ndInvariant, logScale=True)\n", @@ -623,7 +397,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -637,9 +411,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.2" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/UWGeodynamics/tutorials/Tutorial_6_2_diffusive_surface.ipynb b/docs/UWGeodynamics/tutorials/Tutorial_6_2_diffusive_surface.ipynb index 8a121f68a..63bd50eee 100644 --- a/docs/UWGeodynamics/tutorials/Tutorial_6_2_diffusive_surface.ipynb +++ b/docs/UWGeodynamics/tutorials/Tutorial_6_2_diffusive_surface.ipynb @@ -9,17 +9,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loaded rc file /opt/venv/lib/python3.7/site-packages/UWGeodynamics/uwgeo-data/uwgeodynamicsrc\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from underworld import UWGeodynamics as GEO\n", "from underworld import visualisation as vis" @@ -27,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -145,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -168,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -182,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -194,22 +186,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from underworld import visualisation as vis\n", "Fig = vis.Figure(figsize=(1200,400))\n", @@ -226,20 +205,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Model.set_temperatureBCs(top=293.15 * u.degK, \n", " bottom=1603.15 * u.degK, \n", @@ -255,20 +223,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Model.set_velocityBCs(left=[-2.5 * u.centimeter / u.year, None],\n", " right=[2.5 * u.centimeter / u.year, None],\n", @@ -285,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -309,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -332,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -351,22 +308,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.projPlasticStrain)\n", @@ -375,22 +319,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Points(Model.swarm, Model.materialField, fn_size=3.0)\n", @@ -399,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -408,22 +339,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.projViscosityField, logScale=True)\n", @@ -432,155 +350,25 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { - "scrolled": false + "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running with UWGeodynamics version 2.10.2\n", - "Options: -Q22_pc_type uw -ksp_type bsscr -pc_type none -ksp_k2_type NULL -rescale_equations False -remove_constant_pressure_null_space False -change_backsolve False -change_A11rhspresolve False -restore_K False -A11_ksp_type fgmres -A11_ksp_rtol 1e-06 -scr_ksp_type fgmres -scr_ksp_rtol 1e-05\n", - "SP total time: 41606.92 years, timestep: 201.0 years, No. of its: 207\n", - "Step: 1 Model Time: 41606.9 year dt: 41606.9 year (2021-09-20 04:56:45)\n", - "SP total time: 41652.4 years, timestep: 200.25 years, No. of its: 208\n", - "Step: 2 Model Time: 83259.3 year dt: 41652.4 year (2021-09-20 04:56:59)\n", - "SP total time: 41665.53 years, timestep: 200.32 years, No. of its: 208\n", - "Step: 3 Model Time: 124924.8 year dt: 41665.5 year (2021-09-20 04:57:14)\n", - "SP total time: 41598.51 years, timestep: 200.96 years, No. of its: 207\n", - "Step: 4 Model Time: 166523.4 year dt: 41598.5 year (2021-09-20 04:57:30)\n", - "SP total time: 41589.54 years, timestep: 200.92 years, No. of its: 207\n", - "Step: 5 Model Time: 208112.9 year dt: 41589.5 year (2021-09-20 04:57:45)\n", - "SP total time: 41514.71 years, timestep: 200.55 years, No. of its: 207\n", - "Step: 6 Model Time: 249627.6 year dt: 41514.7 year (2021-09-20 04:58:00)\n", - "SP total time: 41767.65 years, timestep: 200.81 years, No. of its: 208\n", - "Step: 7 Model Time: 291395.3 year dt: 41767.7 year (2021-09-20 04:58:15)\n", - "SP total time: 41699.84 years, timestep: 200.48 years, No. of its: 208\n", - "Step: 8 Model Time: 333095.1 year dt: 41699.8 year (2021-09-20 04:58:29)\n", - "SP total time: 41633.23 years, timestep: 200.16 years, No. of its: 208\n", - "Step: 9 Model Time: 374728.3 year dt: 41633.2 year (2021-09-20 04:58:42)\n", - "SP total time: 41609.6 years, timestep: 201.01 years, No. of its: 207\n", - "Step: 10 Model Time: 416337.9 year dt: 41609.6 year (2021-09-20 04:58:56)\n", - "SP total time: 41642.12 years, timestep: 200.2 years, No. of its: 208\n", - "Step: 11 Model Time: 457980.0 year dt: 41642.1 year (2021-09-20 04:59:09)\n", - "SP total time: 41849.32 years, timestep: 200.24 years, No. of its: 209\n", - "Step: 12 Model Time: 499829.4 year dt: 41849.3 year (2021-09-20 04:59:23)\n", - "SP total time: 41818.0 years, timestep: 201.05 years, No. of its: 208\n", - "Step: 13 Model Time: 541647.4 year dt: 41818.0 year (2021-09-20 04:59:37)\n", - "SP total time: 42021.98 years, timestep: 201.06 years, No. of its: 209\n", - "Step: 14 Model Time: 583669.3 year dt: 42022.0 year (2021-09-20 04:59:59)\n", - "SP total time: 42020.64 years, timestep: 201.06 years, No. of its: 209\n", - "Step: 15 Model Time: 625690.0 year dt: 42020.6 year (2021-09-20 05:00:14)\n", - "SP total time: 41600.45 years, timestep: 200.97 years, No. of its: 207\n", - "Step: 16 Model Time: 667290.4 year dt: 41600.4 year (2021-09-20 05:00:30)\n", - "SP total time: 41641.41 years, timestep: 200.2 years, No. of its: 208\n", - "Step: 17 Model Time: 708931.8 year dt: 41641.4 year (2021-09-20 05:00:44)\n", - "SP total time: 41740.4 years, timestep: 200.67 years, No. of its: 208\n", - "Step: 18 Model Time: 750672.2 year dt: 41740.4 year (2021-09-20 05:00:59)\n", - "SP total time: 41974.9 years, timestep: 200.84 years, No. of its: 209\n", - "Step: 19 Model Time: 792647.1 year dt: 41974.9 year (2021-09-20 05:01:13)\n", - "SP total time: 41639.26 years, timestep: 200.19 years, No. of its: 208\n", - "Step: 20 Model Time: 834286.4 year dt: 41639.3 year (2021-09-20 05:01:29)\n", - "SP total time: 41839.98 years, timestep: 200.19 years, No. of its: 209\n", - "Step: 21 Model Time: 876126.4 year dt: 41840.0 year (2021-09-20 05:01:43)\n", - "SP total time: 41826.91 years, timestep: 201.09 years, No. of its: 208\n", - "Step: 22 Model Time: 917953.3 year dt: 41826.9 year (2021-09-20 05:01:59)\n", - "SP total time: 41709.76 years, timestep: 200.53 years, No. of its: 208\n", - "Step: 23 Model Time: 959663.0 year dt: 41709.8 year (2021-09-20 05:02:14)\n", - "SP total time: 40336.95 years, timestep: 200.68 years, No. of its: 201\n", - "Step: 24 Model Time: 1.0 megayear dt: 40337.0 year (2021-09-20 05:03:00)\n", - "SP total time: 42574.8 years, timestep: 200.82 years, No. of its: 212\n", - "Step: 25 Model Time: 1.0 megayear dt: 42574.8 year (2021-09-20 05:03:19)\n", - "SP total time: 41958.72 years, timestep: 200.76 years, No. of its: 209\n", - "Step: 26 Model Time: 1.1 megayear dt: 41958.7 year (2021-09-20 05:03:35)\n", - "SP total time: 42161.09 years, timestep: 200.77 years, No. of its: 210\n", - "Step: 27 Model Time: 1.1 megayear dt: 42161.1 year (2021-09-20 05:03:49)\n", - "SP total time: 42592.15 years, timestep: 200.91 years, No. of its: 212\n", - "Step: 28 Model Time: 1.2 megayear dt: 42592.2 year (2021-09-20 05:04:08)\n", - "SP total time: 42948.93 years, timestep: 200.7 years, No. of its: 214\n", - "Step: 29 Model Time: 1.2 megayear dt: 42948.9 year (2021-09-20 05:04:27)\n", - "SP total time: 42689.88 years, timestep: 200.42 years, No. of its: 213\n", - "Step: 30 Model Time: 1.3 megayear dt: 42689.9 year (2021-09-20 05:04:41)\n", - "SP total time: 42439.84 years, timestep: 200.19 years, No. of its: 212\n", - "Step: 31 Model Time: 1.3 megayear dt: 42439.8 year (2021-09-20 05:04:58)\n", - "SP total time: 42223.57 years, timestep: 201.06 years, No. of its: 210\n", - "Step: 32 Model Time: 1.3 megayear dt: 42223.6 year (2021-09-20 05:05:13)\n", - "SP total time: 42445.07 years, timestep: 200.21 years, No. of its: 212\n", - "Step: 33 Model Time: 1.4 megayear dt: 42445.1 year (2021-09-20 05:05:29)\n", - "SP total time: 42402.16 years, timestep: 200.96 years, No. of its: 211\n", - "Step: 34 Model Time: 1.4 megayear dt: 42402.2 year (2021-09-20 05:05:47)\n", - "SP total time: 42636.96 years, timestep: 200.17 years, No. of its: 213\n", - "Step: 35 Model Time: 1.5 megayear dt: 42637.0 year (2021-09-20 05:06:02)\n", - "SP total time: 42715.45 years, timestep: 200.54 years, No. of its: 213\n", - "Step: 36 Model Time: 1.5 megayear dt: 42715.4 year (2021-09-20 05:06:16)\n", - "SP total time: 42415.54 years, timestep: 201.02 years, No. of its: 211\n", - "Step: 37 Model Time: 1.6 megayear dt: 42415.5 year (2021-09-20 05:06:30)\n", - "SP total time: 42029.29 years, timestep: 201.1 years, No. of its: 209\n", - "Step: 38 Model Time: 1.6 megayear dt: 42029.3 year (2021-09-20 05:06:57)\n", - "SP total time: 42709.76 years, timestep: 200.52 years, No. of its: 213\n", - "Step: 39 Model Time: 1.6 megayear dt: 42709.8 year (2021-09-20 05:07:19)\n", - "SP total time: 43780.35 years, timestep: 200.83 years, No. of its: 218\n", - "Step: 40 Model Time: 1.7 megayear dt: 43780.3 year (2021-09-20 05:07:42)\n", - "SP total time: 43875.98 years, timestep: 200.35 years, No. of its: 219\n", - "Step: 41 Model Time: 1.7 megayear dt: 43876.0 year (2021-09-20 05:07:59)\n", - "SP total time: 43888.99 years, timestep: 200.41 years, No. of its: 219\n", - "Step: 42 Model Time: 1.8 megayear dt: 43889.0 year (2021-09-20 05:08:14)\n", - "SP total time: 43810.35 years, timestep: 200.96 years, No. of its: 218\n", - "Step: 43 Model Time: 1.8 megayear dt: 43810.4 year (2021-09-20 05:08:29)\n", - "SP total time: 44702.28 years, timestep: 200.46 years, No. of its: 223\n", - "Step: 44 Model Time: 1.9 megayear dt: 44702.3 year (2021-09-20 05:08:58)\n", - "SP total time: 44941.05 years, timestep: 200.63 years, No. of its: 224\n", - "Step: 45 Model Time: 1.9 megayear dt: 44941.0 year (2021-09-20 05:09:13)\n", - "SP total time: 45042.24 years, timestep: 201.08 years, No. of its: 224\n", - "Step: 46 Model Time: 1.9 megayear dt: 45042.2 year (2021-09-20 05:09:28)\n", - "SP total time: 45250.95 years, timestep: 201.12 years, No. of its: 225\n", - "Step: 47 Model Time: 2.0 megayear dt: 45250.9 year (2021-09-20 05:09:45)\n", - "SP total time: 7764.61 years, timestep: 199.09 years, No. of its: 39\n", - "Step: 48 Model Time: 2.0 megayear dt: 7764.6 year (2021-09-20 05:10:28)\n", - "SP total time: 10000.0 years, timestep: 200.0 years, No. of its: 50\n", - "Step: 49 Model Time: 2.0 megayear dt: 10000.0 year (2021-09-20 05:10:45)\n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Model.run_for(2.01 * u.megayear, checkpoint_interval=1.*u.megayears)" + "outputs": [], + "source": [ + "Model.run_for(duration=2.01 * u.megayear, checkpoint_interval=0.1*u.megayears)" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from underworld import visualisation as vis\n", "Fig = vis.Figure(figsize=(1200,400))\n", "# ### output tracers\n", - "Fig.Points(Model.surfacetracers_tracers, pointSize=3.0)\n", + "Fig.Points(Model.surface_tracers, pointSize=3.0)\n", "\n", "# for line in lines:\n", "# Fig.Points(line, pointSize=2.0)\n", @@ -590,22 +378,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.strainRate_2ndInvariant, logScale=True)\n", @@ -622,7 +397,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -636,9 +411,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.2" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/UWGeodynamics/tutorials/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb b/docs/UWGeodynamics/tutorials/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb new file mode 100644 index 000000000..9212b5850 --- /dev/null +++ b/docs/UWGeodynamics/tutorials/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb @@ -0,0 +1,517 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 6: Simple Surface Processes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from underworld import UWGeodynamics as GEO\n", + "from underworld import visualisation as vis\n", + "\n", + "import underworld.function as fn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "u = GEO.UnitRegistry" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Characteristic values of the system\n", + "half_rate = 1.8 * u.centimeter / u.year\n", + "model_length = 360e3 * u.meter\n", + "model_height = 120e3 * u.meter\n", + "refViscosity = 1e24 * u.pascal * u.second\n", + "surfaceTemp = 273.15 * u.degK\n", + "baseModelTemp = 1603.15 * u.degK\n", + "bodyforce = 3300 * u.kilogram / u.metre**3 * 9.81 * u.meter / u.second**2\n", + "\n", + "KL = model_length\n", + "Kt = KL / half_rate\n", + "KM = bodyforce * KL**2 * Kt**2\n", + "KT = (baseModelTemp - surfaceTemp)\n", + "\n", + "GEO.scaling_coefficients[\"[length]\"] = KL\n", + "GEO.scaling_coefficients[\"[time]\"] = Kt\n", + "GEO.scaling_coefficients[\"[mass]\"]= KM\n", + "GEO.scaling_coefficients[\"[temperature]\"] = KT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "Model = GEO.Model(elementRes=(16, 16, 16), \n", + " minCoord=(0. * u.kilometer, 0. * u.kilometer, -110. * u.kilometer), \n", + " maxCoord=(120. * u.kilometer, 120. * u.kilometer, 10. * u.kilometer), \n", + " gravity=(0.0, 0.0, -9.81 * u.meter / u.second**2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "Model.outputDir=\"outputs_tutorial6.3_velSP_3D\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Model.diffusivity = 1e-6 * u.metre**2 / u.second \n", + "Model.capacity = 1000. * u.joule / (u.kelvin * u.kilogram)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "air = Model.add_material(name=\"Air\", shape=GEO.shapes.Layer3D(top=Model.top, bottom=0.0 * u.kilometer))\n", + "# stickyAir = Model.add_material(name=\"StickyAir\", shape=GEO.shapes.Layer2D(top=air.bottom, bottom= 0.0 * u.kilometer))\n", + "uppercrust = Model.add_material(name=\"UppperCrust\", shape=GEO.shapes.Layer3D(top=air.bottom, bottom=-35.0 * u.kilometer))\n", + "mantleLithosphere = Model.add_material(name=\"MantleLithosphere\", shape=GEO.shapes.Layer3D(top=uppercrust.bottom, bottom=-100.0 * u.kilometer))\n", + "mantle = Model.add_material(name=\"Mantle\", shape=GEO.shapes.Layer3D(top=mantleLithosphere.bottom, bottom=Model.bottom))\n", + "sediment = Model.add_material(name=\"Sediment\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "air.diffusivity = 1.0e-6 * u.metre**2 / u.second\n", + "air.capacity = 100. * u.joule / (u.kelvin * u.kilogram)\n", + "\n", + "# stickyAir.diffusivity = 1.0e-6 * u.metre**2 / u.second\n", + "# stickyAir.capacity = 100. * u.joule / (u.kelvin * u.kilogram)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "air.density = 1. * u.kilogram / u.metre**3\n", + "# stickyAir.density = 1. * u.kilogram / u.metre**3\n", + "uppercrust.density = GEO.LinearDensity(reference_density=2620. * u.kilogram / u.metre**3)\n", + "mantleLithosphere.density = GEO.LinearDensity(reference_density=3370. * u.kilogram / u.metre**3)\n", + "mantle.density = GEO.LinearDensity(reference_density=3370. * u.kilogram / u.metre**3)\n", + "sediment.density = GEO.LinearDensity(reference_density=2300. * u.kilogram / u.metre**3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uppercrust.radiogenicHeatProd = 0.7 * u.microwatt / u.meter**3\n", + "sediment.radiogenicHeatProd = 0.7 * u.microwatt / u.meter**3\n", + "mantleLithosphere.radiogenicHeatProd = 0.02 * u.microwatt / u.meter**3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rh = GEO.ViscousCreepRegistry()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "air.viscosity = 1e19 * u.pascal * u.second\n", + "# stickyAir.viscosity = 1e20 * u.pascal * u.second\n", + "uppercrust.viscosity = 1 * rh.Wet_Quartz_Dislocation_Gleason_and_Tullis_1995\n", + "mantleLithosphere.viscosity = rh.Dry_Olivine_Dislocation_Karato_and_Wu_1993\n", + "mantle.viscosity = 0.2 * rh.Dry_Olivine_Dislocation_Karato_and_Wu_1993\n", + "sediment.viscosity = rh.Wet_Quartz_Dislocation_Gleason_and_Tullis_1995" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plasticity = GEO.DruckerPrager(cohesion=20.0 * u.megapascal,\n", + " cohesionAfterSoftening=20 * u.megapascal,\n", + " frictionCoefficient=0.12,\n", + " frictionAfterSoftening=0.02,\n", + " epsilon1=0.5,\n", + " epsilon2=1.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uppercrust.plasticity = plasticity\n", + "mantleLithosphere.plasticity = plasticity\n", + "mantle.plasticity = plasticity\n", + "sediment.plasticity = plasticity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Temperature Boundary Condition" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Model.set_temperatureBCs(top=293.15 * u.degK, \n", + " bottom=1603.15 * u.degK, \n", + " materials=[(mantle, 1603.15 * u.degK), (air, 293.15 * u.degK)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Velocity Boundary Conditions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vel = 2.5 * u.centimeter / u.year\n", + "\n", + "\n", + "\n", + "vol_out = 2*(vel*(air.top - air.bottom)*Model.maxCoord[1]).to_base_units()\n", + "vol_out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Velocity at surface to replace air that gets removed at sides\n", + "vel_in = vol_out / (Model.maxCoord[0] * Model.maxCoord[1])\n", + "vel_in.to_base_units()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Model.set_velocityBCs(left = [-vel, None, None],\n", + " right=[vel, None, None],\n", + " front=[None, 0.0, None], back=[None, 0.0, None],\n", + " top = [None, None, -1*vel_in],\n", + " bottom = [None, None, None])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initial Damage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def gaussian(xx, centre, width):\n", + " return ( np.exp( -(xx - centre)**2 / width ))\n", + "\n", + "maxDamage = 0.7\n", + "Model.plasticStrain.data[:] = 0.\n", + "Model.plasticStrain.data[:] = maxDamage * np.random.rand(*Model.plasticStrain.data.shape[:])\n", + "Model.plasticStrain.data[:,0] *= gaussian(Model.swarm.particleCoordinates.data[:,0], (GEO.nd(Model.maxCoord[0] - Model.minCoord[0])) / 2.0, GEO.nd(5.0 * u.kilometer))\n", + "Model.plasticStrain.data[:,0] *= gaussian(Model.swarm.particleCoordinates.data[:,2], GEO.nd(-35. * u.kilometer) , GEO.nd(5.0 * u.kilometer))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if GEO.nProcs == 1:\n", + " Fig = vis.Figure(resolution=(1200,600))\n", + " Fig.Surface(Model.mesh, Model.plasticStrain, cullface=False, opacity=0.5)\n", + " Fig.window()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if GEO.nProcs == 1:\n", + " Fig = vis.Figure(resolution=(1200,600))\n", + " Fig.Surface(Model.mesh, Model.materialField, cullface=False, opacity=0.5)\n", + " Fig.window()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### x and y coordinates for the surface" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "x = np.linspace(Model.minCoord[0], Model.maxCoord[0], 4*(Model.mesh.elementRes[0]+1))\n", + "y = np.linspace(Model.minCoord[1], Model.maxCoord[1], 4*(Model.mesh.elementRes[1]+1))\n", + "\n", + "xi, yi = np.meshgrid(x, y)\n", + "\n", + "coords = np.zeros(shape=(xi.flatten().shape[0], 3))\n", + "coords[:,0] = xi.flatten()\n", + "coords[:,1] = yi.flatten()\n", + "coords[:,2] = np.zeros_like(coords[:,0]) ### or any array with same shape as x and y coords with the initial height\n", + "\n", + "### add back in the dim\n", + "coords = coords * u.kilometer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Include erosion and sedimentation rates in model runs\n", + "\n", + "A branching condition is used to create erosion and sedimentation rates that can vary across the domain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ve_conditions = fn.branching.conditional([((Model.y >= GEO.nd(Model.maxCoord[1])/2.), GEO.nd(2.5 * u.millimeter/u.year)),\n", + " (True, GEO.nd(0.0 * u.millimeter/u.year))])\n", + "\n", + "vs_conditions = fn.branching.conditional([((Model.y >= GEO.nd(Model.maxCoord[1])/2.), GEO.nd(2.5 * u.millimeter/u.year)),\n", + " (True, GEO.nd(0.0 * u.millimeter/u.year))])\n", + "\n", + "Model.surfaceProcesses = GEO.surfaceProcesses.velocitySurface_3D(airIndex = air.index,\n", + " sedimentIndex= sediment.index,\n", + " surfaceArray = coords, ### grid with surface points (x, y, z)\n", + " vs_condition = vs_conditions, ### sedimentation rate at each grid point\n", + " ve_condition = ve_conditions, ### erosion rate at each grid point\n", + " surfaceElevation=air.bottom)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from underworld import visualisation as vis\n", + "Fig = vis.Figure(figsize=(1200,400))\n", + "Fig.Points(Model.surface_tracers, Model.surface_tracers.ve, fn_size=5)\n", + "Fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from underworld import visualisation as vis\n", + "Fig = vis.Figure(figsize=(1200,400))\n", + "Fig.Points(Model.surface_tracers, Model.surface_tracers.vs, fn_size=5)\n", + "Fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Model.init_model(temperature=\"steady-state\", pressure=\"lithostatic\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "GEO.rcParams['initial.nonlinear.min.iterations'] = 1\n", + "GEO.rcParams['nonlinear.min.iterations'] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Model.solver.set_inner_method(\"mumps\")\n", + "# Model.solver.set_penalty(1e6)\n", + "GEO.rcParams[\"initial.nonlinear.tolerance\"] = 1e-2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "Model.run_for(duration=0.51 * u.megayear, checkpoint_interval=0.5*u.megayears)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if GEO.size == 1:\n", + " import matplotlib.pyplot as plt\n", + " \n", + " \n", + " surface = GEO.dim(Model.surface_tracers.data, u.kilometer)\n", + "\n", + " scatter = plt.scatter(surface[:,0], surface[:,1], c=surface[:,2], s=10)\n", + "\n", + " cbar = plt.colorbar(scatter)\n", + " \n", + " cbar.set_label('Topo [km]')\n", + " \n", + " \n", + " plt.xlabel('x [km]')\n", + " plt.xlabel('y [km]')\n", + " \n", + " plt.show()\n", + " \n", + " \n", + " plt.plot()\n", + " \n", + " profile1 = surface[surface[:,1].m == np.unique(surface.m[:,0])[20]]\n", + " profile2 = surface[surface[:,1].m == np.unique(surface.m[:,0])[-20]]\n", + " \n", + " plt.plot(profile1[:,0], profile1[:,2], label = 'No SP') \n", + " \n", + " plt.plot(profile2[:,0], profile2[:,2], label = 'SP')\n", + " \n", + " plt.xlabel('x [km]')\n", + " \n", + " plt.ylabel('Topo [km]')\n", + " \n", + " plt.legend()\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/development/docker/docker-builder.sh b/docs/development/docker/docker-builder.sh index 0a15e8f47..2e2f01e7f 100755 --- a/docs/development/docker/docker-builder.sh +++ b/docs/development/docker/docker-builder.sh @@ -13,24 +13,27 @@ ARCH=$(uname -m) echo "Will build docker image locally for architecture type: $ARCH" echo "************************************************************\n" +# Get the ubuntu image +docker pull ubuntu:22.04 + ## The mpi and lavavu images should be automatically made via github actions -#docker build . --pull -f ./docs/development/docker/mpi/Dockerfile.openmpi -t underworldcode/openmpi:4.1.4-$ARCH -#docker build . --pull -f ./docs/development/docker/lavavu/Dockerfile -t underworldcode/lavavu:$ARCH +docker build . -f ./docs/development/docker/mpi/Dockerfile.openmpi -t underworldcode/openmpi:4.1.4-$ARCH +docker build . -f ./docs/development/docker/lavavu/Dockerfile -t underworldcode/lavavu:$ARCH -docker build . --pull \ +docker build . \ -f ./docs/development/docker/petsc/Dockerfile \ - --build-arg MPI_IMAGE="underworldcode/openmpi:4.1.4" \ - -t underworldcode/petsc:3.18.1-$ARCH + --build-arg MPI_IMAGE="underworldcode/openmpi:4.1.4-$ARCH" \ + -t underworldcode/petsc:3.19.4-$ARCH -# don't use pull here as we want the petsc image above +## don't use pull here as we want the petsc image above docker build . \ - --build-arg PETSC_IMAGE="underworldcode/petsc:3.18.1-$ARCH" \ + --build-arg PETSC_IMAGE="underworldcode/petsc:3.19.4-$ARCH" \ -f ./docs/development/docker/underworld2/Dockerfile \ - -t underworldcode/underworld2:2.14.0b-$ARCH + -t underworldcode/underworld2:2.15.0b-$ARCH -docker push underworldcode/petsc:3.18.1-$ARCH -docker push underworldcode/underworld2:2.14.0b-$ARCH +#docker push underworldcode/petsc:3.19.4-$ARCH +#docker push underworldcode/underworld2:2.15.0b-$ARCH #### if updates for both arm64 and x86_64 build manifest, ie # docker manifest create underworldcode/petsc:3.18.1 \ diff --git a/docs/development/docker/petsc/Dockerfile b/docs/development/docker/petsc/Dockerfile index cd96300ab..ff827a91b 100644 --- a/docs/development/docker/petsc/Dockerfile +++ b/docs/development/docker/petsc/Dockerfile @@ -80,12 +80,12 @@ RUN apt-get update -qq \ && rm -rf /var/lib/apt/lists/* RUN pip3 install --no-cache-dir \ - cython \ + cython==0.29.36 \ numpy \ mpi4py # get petsc -ARG PETSC_VERSION="3.18.1" +ARG PETSC_VERSION="3.19.4" RUN mkdir -p /tmp/src WORKDIR /tmp/src RUN wget http://ftp.mcs.anl.gov/pub/petsc/release-snapshots/petsc-lite-${PETSC_VERSION}.tar.gz --no-check-certificate \ diff --git a/docs/development/docker/underworld2/Dockerfile b/docs/development/docker/underworld2/Dockerfile index 42f5e1c70..38533ddc1 100644 --- a/docs/development/docker/underworld2/Dockerfile +++ b/docs/development/docker/underworld2/Dockerfile @@ -17,7 +17,7 @@ # Used for github actions on the underworld repo # Must go before the 1st FROM see # https://docs.docker.com/engine/reference/builder/#understand-how-arg-and-from-interact -ARG PETSC_IMAGE="underworldcode/petsc:3.18.1" +ARG PETSC_IMAGE="underworldcode/petsc:3.19.4" # 'petsc-image' will be used later on in build stage COPY command FROM ${PETSC_IMAGE} as petsc-image @@ -94,7 +94,7 @@ RUN apt-get update -qq \ # Remove this for future versions # setuptools=65.6.0 has a unfixed error, so forcing version -RUN pip3 install setuptools==65.5.1 --force-reinstall --no-cache \ +RUN pip3 install setuptools --force-reinstall --no-cache \ && pip3 install --no-cache-dir \ matplotlib \ scipy \ @@ -127,10 +127,11 @@ WORKDIR /tmp COPY --chown=$NB_USER:users . /tmp/underworld2 WORKDIR /tmp/underworld2 RUN pip3 install -vvv . -RUN pip3 install setuptools==65.5.1 --force-reinstall --no-cache-dir \ +RUN pip3 install setuptools --force-reinstall --no-cache-dir \ && pip3 install --no-cache-dir \ git+https://github.com/drufat/triangle.git \ - badlands + badlands==2.2.4 \ + jupyter_contrib_nbextensions RUN pip3 freeze >/opt/requirements.txt # Record manually install apt packages. @@ -161,4 +162,7 @@ EXPOSE 8888 WORKDIR $NB_HOME USER $NB_USER +# Declare a volume space +VOLUME $NB_HOME/workspace + CMD ["jupyter-lab", "--no-browser", "--ip='0.0.0.0'"] diff --git a/docs/install_guides/setonix_baremetal.sh b/docs/install_guides/setonix_baremetal.sh index a14adf5aa..28c09a8cf 100644 --- a/docs/install_guides/setonix_baremetal.sh +++ b/docs/install_guides/setonix_baremetal.sh @@ -1,19 +1,30 @@ #!/bin/bash -l + +## User required input +#SBATCH --account=pawsey0407 +#SBATCH --job-name=bobthejob +#SBATCH --ntasks=3 +#SBATCH --time=00:20:00 -# The following assumes read access to -# /software/projects/pawsey0407/setonix/ +## Setup job conditions and run environment +#SBATCH --ntasks-per-node=64 # found this is needed ~Apr2023 +#SBATCH --cpus-per-task=1 # OMP_NUM_THREADS equivalent -module load python/3.9.15 py-mpi4py/3.1.2-py3.9.15 py-numpy/1.20.3 py-h5py/3.4.0 py-cython/0.29.24 +# Note we avoid any inadvertent OpenMP threading by setting +export OMP_NUM_THREADS=1 -export OPT_DIR=/software/projects/pawsey0407/setonix/ +# load system packages: py39, mpi, hdf5 +module load python/3.9.15 py-mpi4py/3.1.2-py3.9.15 py-numpy/1.20.3 py-h5py/3.4.0 py-cython/0.29.24 cmake/3.21.4 -## for modifying the venv -# source $OPT_DIR/py39/bin/activate +# add custom virtual environment and underworld +export OPT_DIR=/software/projects/pawsey0407/setonix/ +export PYTHONPATH=$OPT_DIR/py39/lib/python3.9/site-packages/:$PYTHONPATH +export PYTHONPATH=$OPT_DIR/underworld/2.14.2/lib/python3.9/site-packages:$PYTHONPATH -## For development only -# export PETSC_DIR=$OPT_DIR/petsc-3.18.1 -# export PYTHONPATH=$PETSC_DIR/:$PYTHONPATH +# load custom petsc +export PETSC_DIR=$OPT_DIR/petsc-3.19.0 +export PYTHONPATH=$PETSC_DIR/lib:$PYTHONPATH -# for using the venv -export PYTHONPATH=$OPT_DIR/py39/lib/python3.9/site-packages:$PYTHONPATH -export PYTHONPATH=$OPT_DIR/underworld/2.14.2/lib/python3.9/site-packages/:$PYTHONPATH +## model name and execution +export model="mymod.py" +srun -n ${SLURM_NTASKS} python3 $model diff --git a/docs/test/UWGeodynamics/image_tests.py b/docs/test/UWGeodynamics/image_tests.py index d6f5bef4d..2ebdb8bf3 100644 --- a/docs/test/UWGeodynamics/image_tests.py +++ b/docs/test/UWGeodynamics/image_tests.py @@ -4,8 +4,8 @@ path = os.path.abspath('./image_tests/') #Check if the viewer is working -import glucifer -if not glucifer.lavavu: +import underworld.visualisation as vis +if not vis.lavavu: print("Image tests skipped, Viewer disabled") exit() diff --git a/underworld/UWGeodynamics/_melt.py b/underworld/UWGeodynamics/_melt.py index b431c8cb7..a9920aebd 100644 --- a/underworld/UWGeodynamics/_melt.py +++ b/underworld/UWGeodynamics/_melt.py @@ -24,8 +24,8 @@ def temperature(self, pressure): def plot(self, pressure): import pylab as plt - temperature = dimensionalise(self.temperature(pressure), u.kelvin) - pressure = dimensionalise(pressure, u.pascal) + temperature = dimensionalise(self.temperature(pressure), u.kelvin).m + pressure = dimensionalise(pressure, u.pascal).m plt.plot(temperature, pressure) plt.gca().invert_yaxis() plt.show() diff --git a/underworld/UWGeodynamics/_model.py b/underworld/UWGeodynamics/_model.py index 2b41a4a12..0d1574cf4 100644 --- a/underworld/UWGeodynamics/_model.py +++ b/underworld/UWGeodynamics/_model.py @@ -839,6 +839,12 @@ def _advdiffSystem(self): else: HeatProdMap[material.index] = 0. + # Melt heating effects if enabled + if material.latentHeatFusion and self.dt.value: + dynamicHeating = self._get_dynamic_heating(material) + HeatProdMap[material.index] += dynamicHeating + + self.HeatProdFn = fn.branching.map(fn_key=self.materialField, mapping=HeatProdMap) diff --git a/underworld/UWGeodynamics/surfaceProcesses.py b/underworld/UWGeodynamics/surfaceProcesses.py index 50b60ceb3..507122e79 100644 --- a/underworld/UWGeodynamics/surfaceProcesses.py +++ b/underworld/UWGeodynamics/surfaceProcesses.py @@ -523,6 +523,7 @@ def __init__(self, airIndex, sedimentIndex, D, surfaceArray, updateSurfaceLB=0.* updateSurfaceRB : Distance to update surface from right boundary, default is 0 km which results in a free slip boundary + '''updated''' ***All units are converted under the hood*** @@ -536,200 +537,256 @@ def __init__(self, airIndex, sedimentIndex, D, surfaceArray, updateSurfaceLB=0.* surfaceArray = coords ) *** - """ + """ self.airIndex = airIndex self.sedimentIndex = sedimentIndex self.timeField = timeField - self.updateSurfaceLB = updateSurfaceLB.to(u.kilometer).magnitude - self.updateSurfaceRB = updateSurfaceRB.to(u.kilometer).magnitude - # self.surfaceElevation = surfaceElevation.to(u.kilometer).magnitude - self.D = D.to(u.kilometer**2 / u.year).magnitude + + self.D = D.to(u.kilometer**2 / u.year) + + + ### a conversion, will throw an error if units are neglected + self.surfaceArray = surfaceArray + self.updateSurfaceLB = updateSurfaceLB.to(u.kilometer) + self.updateSurfaceRB = updateSurfaceRB.to(u.kilometer) + + self.Model = Model - self.surfaceArray = surfaceArray - self.surface_dt_diffusion = None + + self.originalZ = None + self.min_dist = None + self.nd_coords = None self.dx = None - self.surface_data_local = None - self.original_surface = None + + ''' function to create grid for surface ''' + def _init_model(self): + ''' creates a PT output ''' + ### automatically non-dimensionalises the imput coords if they have a dim + self.Model.add_passive_tracers(name="surface", vertices=nd(self.surfaceArray), advect=False) - self.Model.add_passive_tracers(name="surface", vertices=self.surfaceArray, advect=False) + self.Model.surface_tracers.allow_parallel_nn = True - self.dx = dimensionalise((self.surfaceArray[:,0][1] - self.surfaceArray[:,0][0]), u.kilometer).magnitude + self.nd_coords = nd(self.surfaceArray) - '''set up custom tracers for surface''' - x_min_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,0].min() - x_max_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,0].max() + ### get distance between 1st and 2nd x coord and y coords to determine min distance between grid points + x = np.sort(np.unique(self.nd_coords[:,0])) - ''' create surface tracers to advect on each node''' - self.surface_data_local = np.zeros_like(self.surfaceArray[(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)]) + self.min_dist = np.diff(x).min() - self.surface_data_local[:,0] = self.surfaceArray[:,0][(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)] - self.surface_data_local[:,1] = self.surfaceArray[:,1][(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)] + # self.dx = np.diff(x).min() - # # Spline original surface for no slip condition near boundary - self.original_surface = interp1d(self.surfaceArray[:,0], self.surfaceArray[:,1], kind='cubic', fill_value='extrapolate') + ### create copy of original surface + self.originalZ = self.nd_coords[:,1] + #### add variables for tracking that aren't included in UWGeo + self.Model.surface_tracers.z_coord = self.Model.surface_tracers.add_variable( dataType="double", count=1 ) + + self.Model.surface_tracers.D = self.Model.surface_tracers.add_variable( dataType="double", count=1 ) - self.surface_dt_diffusion = (0.2 * (self.dx * self.dx / self.D)) + if self.Model.surface_tracers.data.size != 0: + ### erosion is downward (negative) + self.Model.surface_tracers.D.data[:,0] = abs( np.repeat( nd(self.D), self.Model.surface_tracers.data.shape[0] ) ) + self.Model.surface_tracers.z_coord.data[:,0] = self.Model.surface_tracers.data[:,1] - def solve(self, dt): + comm.barrier() - root_proc = 0 - z_max_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,1].max() - z_min_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,1].min() + ### add fields to track - ### collect surface data on each node - if z_max_local >= self.surface_data_local[:,1].min(): - ### gets the x and z data of the surface tracers - x_data = np.ascontiguousarray(self.surface_data_local[:,0].copy()) - z_data = np.ascontiguousarray(self.surface_data_local[:,1].copy()) + ### track velocity field on tracers + self.Model.surface_tracers.add_tracked_field(self.Model.velocityField, + name="surface_vel", + units=u.centimeter/u.year, + dataType="float", count=self.Model.mesh.dim) - ### Get the velocity of the surface tracers - tracer_velocity = self.Model.velocityField.evaluate(self.surface_data_local) - vx = np.ascontiguousarray(tracer_velocity[:,0]) - vz = np.ascontiguousarray(tracer_velocity[:,1]) + self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.particleCoordinates, + name="coords", + units=u.centimeter/u.year, + dataType="float", count=self.Model.mesh.dim) + + ## track the surface coordinates (could change to only show the height) + self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.z_coord, + name="topo_height", + units=u.kilometer, + dataType="float", count=1) + + ### track the diffusive surface rate + self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.D, + name="Diffusive rate", + units=u.meter**2/u.year, + dataType="float", count=1) + + + comm.barrier() + + def solve(self, dt): + + + if self.Model.surface_tracers.data.shape[0] > 0: + ### evaluate on all nodes and get the tracer velocity on root proc + tracer_velocity_local = self.Model.velocityField.evaluate(self.Model.surface_tracers.data) + x_local = nd(self.Model.x.evaluate(self.Model.surface_tracers.data)) + y_local = nd(self.Model.y.evaluate(self.Model.surface_tracers.data)) + + x = np.ascontiguousarray(x_local) + y = np.ascontiguousarray(y_local) + vx = np.ascontiguousarray(tracer_velocity_local[:,0]) + vy = np.ascontiguousarray(tracer_velocity_local[:,1]) else: - ### creates dummy data on nodes without the surface - x_data = np.array([None], dtype='float64') - z_data = np.array([None], dtype='float64') + x = np.array([None], dtype='float64') + y = np.array([None], dtype='float64') vx = np.array([None], dtype='float64') - vz = np.array([None], dtype='float64') + vy = np.array([None], dtype='float64') + comm.barrier() ### Collect local array sizes using the high-level mpi4py gather - sendcounts = np.array(comm.gather(len(x_data), root=root_proc)) + sendcounts = np.array(comm.gather(len(x), root=0)) comm.barrier() - if rank == root_proc: + if rank == 0: ### creates dummy data on all nodes to store the surface # surface_data = np.zeros((npoints,2)) - x_surface_data = np.zeros((sum(sendcounts)), dtype='float64') - z_surface_data = np.zeros((sum(sendcounts)), dtype='float64') + x_data = np.zeros((sum(sendcounts)), dtype='float64') + y_data = np.zeros((sum(sendcounts)), dtype='float64') vx_data = np.zeros((sum(sendcounts)), dtype='float64') - vz_data = np.zeros((sum(sendcounts)), dtype='float64') - surface_data = np.zeros((sum(sendcounts), 4), dtype='float64') + vy_data = np.zeros((sum(sendcounts)), dtype='float64') else: - x_surface_data = None - z_surface_data = None + x_data = None + y_data = None vx_data = None - vz_data = None - surface_data = None + vy_data = None ### store the surface spline on each node f1 = None - comm.barrier() + comm.Gatherv(sendbuf=x, recvbuf=(x_data, sendcounts), root=0) - ## gather x values, can't do them together - comm.Gatherv(sendbuf=x_data, recvbuf=(x_surface_data, sendcounts), root=root_proc) - ## gather z values - comm.Gatherv(sendbuf=z_data, recvbuf=(z_surface_data, sendcounts), root=root_proc) + comm.Gatherv(sendbuf=y, recvbuf=(y_data, sendcounts), root=0) ### gather velocity values - comm.Gatherv(sendbuf=vx, recvbuf=(vx_data, sendcounts), root=root_proc) + comm.Gatherv(sendbuf=vx, recvbuf=(vx_data, sendcounts), root=0) + + comm.Gatherv(sendbuf=vy, recvbuf=(vy_data, sendcounts), root=0) - comm.Gatherv(sendbuf=vz, recvbuf=(vz_data, sendcounts), root=root_proc) + if rank == 0: + nd_D = nd( self.D ) - if rank == root_proc: - ### Put back into combined array - surface_data[:,0] = x_surface_data - surface_data[:,1] = z_surface_data + surface_data = np.zeros((len(x_data), 4), dtype='float64') + surface_data[:,0] = x_data + surface_data[:,1] = y_data surface_data[:,2] = vx_data - surface_data[:,3] = vz_data + surface_data[:,3] = vy_data - ### remove dummy data surface_data = surface_data[~np.isnan(surface_data[:,0])] - - ### sort by x values - surface_data = surface_data[np.argsort(surface_data[:, 0])] - + surface_data = surface_data[np.argsort(surface_data[:,0])] # # Advect top surface - x2 = surface_data[:,0] + (surface_data[:,2] * dt) - z2 = surface_data[:,1] + (surface_data[:,3] * dt) + x_new = (surface_data[:,0] + (surface_data[:,2]*dt)) + y_new = (surface_data[:,1] + (surface_data[:,3]*dt)) + ## Spline top surface + f = interp1d(x_new, y_new, kind='cubic', fill_value='extrapolate') + + ''' interpolate new surface back onto original grid ''' + x_nd = self.nd_coords[:,0] + z_nd = f(x_nd) - # # Spline top surface - f = interp1d(x2, z2, kind='cubic', fill_value='extrapolate') + ### time to diffuse surface based on Model dt + total_time = dt - ### update surface tracer position - # surface_data[:,0] = (surface_data[:,0]) - surface_data[:,1] = f(surface_data[:,0]) + '''Velocity surface process''' + '''erosion dt for vel model''' + surface_dt_diffusion = ( 0.2 * ( (self.min_dist**2) / nd_D ) ) - ### gets the x and y coordinates from the tracers - x = dimensionalise(surface_data[:,0], u.kilometer).magnitude - z = dimensionalise(surface_data[:,1], u.kilometer).magnitude + vel_for_surface = max(abs(vx_data.max()), abs(vy_data.max())) + surface_dt_vel = (0.2 * ( self.min_dist / vel_for_surface) ) - ### time to diffuse surface based on Model dt - total_time = (dimensionalise(dt, u.year)).magnitude + surface_dt = min(surface_dt_diffusion, surface_dt_vel) - '''Diffusion surface process''' - '''erosion dt for diffusion surface''' - surface_time = min(self.surface_dt_diffusion, total_time) + surf_time = min(surface_dt, total_time) - nts = math.ceil(total_time/surface_time) + nts = math.ceil(total_time/surf_time) + + surf_dt = (total_time / nts) + + print('SP total time:', dimensionalise(total_time, u.year), 'timestep:', dimensionalise(surf_dt, u.year), 'No. of its:', nts, flush=True) - surface_dt = total_time / nts - print('SP total time:', round(total_time,2), 'years, timestep:', round(surface_dt,2), 'years, No. of its:', nts, flush=True) ### Basic Hillslope diffusion for i in range(nts): - qs = -self.D * np.diff(z)/self.dx - dzdt = -np.diff(qs)/self.dx - + qs = -nd_D * np.diff(z_nd)/np.diff(x_nd) + dzdt = -np.diff(qs)/np.diff(x_nd[:-1]) - z[1:-1] += dzdt*surface_dt + z_nd[1:-1] += dzdt*surface_dt - x_nd = nd(x*u.kilometer) - z_nd = nd(z*u.kilometer) - ''' updates material near to boundary back to original coordinates ''' - z_original_surface = self.original_surface(x_nd) - z_nd[(x_nd < nd(self.updateSurfaceLB * u.kilometer)) | (x_nd > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB * u.kilometer))))] = z_original_surface[(x_nd < nd(self.updateSurfaceLB * u.kilometer)) | (x_nd > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB * u.kilometer))))] + ''' creates no movement condition near boundary ''' + ''' important when imposing a velocity as particles are easily deformed near the imposed condition''' + ''' This changes the height to the points original height ''' + resetArea_x = (self.nd_coords[:,0] < nd(self.updateSurfaceLB)) | (self.nd_coords[:,0] > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB)))) - ### creates function for the new surface that has eroded, to be broadcast back to nodes - f1 = interp1d(x_nd, z_nd, fill_value='extrapolate', kind='cubic') + z_nd[resetArea_x] = self.originalZ[resetArea_x] + - # print('finished surface process on global rank:', rank, flush= True) + ### creates function for the new surface that has eroded, to be broadcast back to nodes + f1 = interp1d(self.nd_coords[:,0], z_nd, fill_value='extrapolate', kind='cubic') comm.barrier() '''broadcast the new surface''' ### broadcast function for the surface - f1 = comm.bcast(f1, root=root_proc) + f1 = comm.bcast(f1, root=0) + + + + ### update the z coord of the surface array + self.nd_coords[:,1] = f1(self.nd_coords[:,0]) comm.barrier() - ''' replaces the new diffused surface data, only changes z as x values don't change ''' - ### update the surface on individual nodes - self.surface_data_local[:,1] = f1(self.surface_data_local[:,0]) - ### update the global surface tracers + + ### has to be done on all procs due to an internal comm barrier in deform swarm (?) with self.Model.surface_tracers.deform_swarm(): self.Model.surface_tracers.data[:,1] = f1(self.Model.surface_tracers.data[:,0]) + comm.barrier() + + if self.Model.surface_tracers.data.size != 0: + ### update the surface only on procs that have the tracers + self.Model.surface_tracers.z_coord.data[:,0] = self.Model.surface_tracers.data[:,1] + + comm.barrier() + + ### update the time of the sediment and air material as sed & erosion occurs + if self.timeField: + ### Set newly deposited sediment time to 0 (to record deposition time) + self.Model.timeField.data[(self.Model.swarm.data[:,1] < f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] == self.airIndex)] = 0. + ### reset air material time back to the model time + self.Model.timeField.data[(self.Model.swarm.data[:,1] > f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] != self.airIndex)] = self.Model.timeField.data.max() + '''Erode surface/deposit sed based on the surface''' ### update the material on each node according to the spline function for the surface self.Model.materialField.data[(self.Model.swarm.data[:,1] > f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] != self.airIndex)] = self.airIndex self.Model.materialField.data[(self.Model.swarm.data[:,1] < f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] == self.airIndex)] = self.sedimentIndex - comm.barrier() return @@ -761,8 +818,6 @@ def __init__(self, airIndex, sedimentIndex, sedimentationRate, erosionRate, surf Distance to update surface from right boundary, default is 0 km which results in a free slip boundary - - ***All units are converted under the hood*** *** @@ -782,189 +837,223 @@ def __init__(self, airIndex, sedimentIndex, sedimentationRate, erosionRate, surf self.airIndex = airIndex self.sedimentIndex = sedimentIndex self.timeField = timeField - self.dx = None - self.updateSurfaceLB = updateSurfaceLB.to(u.kilometer).magnitude - self.updateSurfaceRB = updateSurfaceRB.to(u.kilometer).magnitude - self.surfaceElevation = surfaceElevation.to(u.kilometer).magnitude - self.sedimentationRate = abs(sedimentationRate.to(u.kilometer / u.year).magnitude) - self.erosionRate = -1. * abs(erosionRate.to(u.kilometer / u.year).magnitude) + + self.ve = sedimentationRate.to(u.kilometer / u.year) + self.vs = erosionRate.to(u.kilometer / u.year) + + ### a conversion, will throw an error if units are neglected + self.surfaceArray = surfaceArray + self.updateSurfaceLB = updateSurfaceLB.to(u.kilometer) + self.updateSurfaceRB = updateSurfaceRB.to(u.kilometer) + + + self.surfaceElevation = surfaceElevation.to(u.kilometer) self.Model = Model - self.surfaceArray = surfaceArray - self.surface_data_local = None - self.original_surface = None + self.originalZ = None + self.min_dist = None + self.nd_coords = None + + self.tkey = self.__class__.__name__+"_surface" + def _init_model(self): - self.Model.add_passive_tracers(name="surface", vertices=self.surfaceArray, advect=False) + ''' creates a PT output ''' + ### automatically non-dimensionalises the imput coords if they have a dim + ## TODO: Fix naming for internal passive tracer swarm + self.Model.add_passive_tracers(name=self.tkey, vertices=nd(self.surfaceArray), advect=False) - self.dx = dimensionalise((self.surfaceArray[:,0][1] - self.surfaceArray[:,0][0]), u.kilometer).magnitude + st = self.Model.passive_tracers[self.tkey] + assert( st != None, f"Error getting passive tracer {self.tkey}") + st.allow_parallel_nn = True - '''set up custom tracers for surface''' - x_min_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,0].min() - x_max_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,0].max() + self.nd_coords = nd(self.surfaceArray) - ''' create surface tracers to advect on each node''' - self.surface_data_local = np.zeros_like(self.surfaceArray[(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)]) + ### get distance between 1st and 2nd x coord and y coords to determine min distance between grid points + x = np.sort(np.unique(self.nd_coords[:,0])) - self.surface_data_local[:,0] = self.surfaceArray[:,0][(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)] - self.surface_data_local[:,1] = self.surfaceArray[:,1][(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)] + self.min_dist = np.diff(x).min() - # # Spline original surface for no slip condition near boundary - self.original_surface = interp1d(self.surfaceArray[:,0], self.surfaceArray[:,1], kind='cubic', fill_value='extrapolate') + ### create copy of original surface + self.originalZ = self.nd_coords[:,1] + comm.barrier() - def solve(self, dt): - root_proc = 0 - z_max_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,1].max() - z_min_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,1].min() + ### add fields to track - ### collect surface data on each node - if z_max_local >= self.surface_data_local[:,1].min(): - ### gets the x and z data of the surface tracers - x_data = np.ascontiguousarray(self.surface_data_local[:,0].copy()) - z_data = np.ascontiguousarray(self.surface_data_local[:,1].copy()) + ### track velocity field on tracers +# st.add_tracked_field(self.Model.velocityField, +# name="surface_vel", +# units=u.centimeter/u.year, +# dataType="float", count=self.Model.mesh.dim) +# +# st.add_tracked_field(st.particleCoordinates, +# name="coords", +# units=u.centimeter/u.year, +# dataType="float", count=self.Model.mesh.dim) - ### Get the velocity of the surface tracers - tracer_velocity = self.Model.velocityField.evaluate(self.surface_data_local) - vx = np.ascontiguousarray(tracer_velocity[:,0]) - vz = np.ascontiguousarray(tracer_velocity[:,1]) + comm.barrier() + + def solve(self, dt): + + st = self.Model.passive_tracers[self.tkey] + assert( st != None, f"Error getting passive tracer {self.tkey}") + if st.data.shape[0] > 0: + ### evaluate on all nodes and get the tracer velocity on root proc + tracer_velocity_local = self.Model.velocityField.evaluate(st.data) + x_local = nd(self.Model.x.evaluate(st.data)) + y_local = nd(self.Model.y.evaluate(st.data)) + + x = np.ascontiguousarray(x_local) + y = np.ascontiguousarray(y_local) + vx = np.ascontiguousarray(tracer_velocity_local[:,0]) + vy = np.ascontiguousarray(tracer_velocity_local[:,1]) else: - ### creates dummy data on nodes without the surface - x_data = np.array([None], dtype='float64') - z_data = np.array([None], dtype='float64') + x = np.array([None], dtype='float64') + y = np.array([None], dtype='float64') vx = np.array([None], dtype='float64') - vz = np.array([None], dtype='float64') + vy = np.array([None], dtype='float64') + comm.barrier() ### Collect local array sizes using the high-level mpi4py gather - sendcounts = np.array(comm.gather(len(x_data), root=root_proc)) + sendcounts = np.array(comm.gather(len(x), root=0)) comm.barrier() - if rank == root_proc: + if rank == 0: ### creates dummy data on all nodes to store the surface # surface_data = np.zeros((npoints,2)) - x_surface_data = np.zeros((sum(sendcounts)), dtype='float64') - z_surface_data = np.zeros((sum(sendcounts)), dtype='float64') + x_data = np.zeros((sum(sendcounts)), dtype='float64') + y_data = np.zeros((sum(sendcounts)), dtype='float64') vx_data = np.zeros((sum(sendcounts)), dtype='float64') - vz_data = np.zeros((sum(sendcounts)), dtype='float64') - surface_data = np.zeros((sum(sendcounts), 4), dtype='float64') + vy_data = np.zeros((sum(sendcounts)), dtype='float64') else: - x_surface_data = None - z_surface_data = None + x_data = None + y_data = None vx_data = None - vz_data = None - surface_data = None + vy_data = None ### store the surface spline on each node f1 = None - comm.barrier() + comm.Gatherv(sendbuf=x, recvbuf=(x_data, sendcounts), root=0) - ## gather x values, can't do them together - comm.Gatherv(sendbuf=x_data, recvbuf=(x_surface_data, sendcounts), root=root_proc) - ## gather z values - comm.Gatherv(sendbuf=z_data, recvbuf=(z_surface_data, sendcounts), root=root_proc) + comm.Gatherv(sendbuf=y, recvbuf=(y_data, sendcounts), root=0) ### gather velocity values - comm.Gatherv(sendbuf=vx, recvbuf=(vx_data, sendcounts), root=root_proc) + comm.Gatherv(sendbuf=vx, recvbuf=(vx_data, sendcounts), root=0) - comm.Gatherv(sendbuf=vz, recvbuf=(vz_data, sendcounts), root=root_proc) + comm.Gatherv(sendbuf=vy, recvbuf=(vy_data, sendcounts), root=0) + if rank == 0: - if rank == root_proc: - ### Put back into combined array - surface_data[:,0] = x_surface_data - surface_data[:,1] = z_surface_data - surface_data[:,2] = vx_data - surface_data[:,3] = vz_data + nd_ve = -1. * abs( nd(self.ve) ) ### erode down(negative) + nd_vs = 1. * abs( nd(self.vs) ) ### sed up (positive) + surface_data = np.zeros((len(x_data), 4), dtype='float64') + surface_data[:,0] = x_data + surface_data[:,1] = y_data + surface_data[:,2] = vx_data + surface_data[:,3] = vy_data - ### remove dummy data surface_data = surface_data[~np.isnan(surface_data[:,0])] - - ### sort by x values - surface_data = surface_data[np.argsort(surface_data[:, 0])] - + surface_data = surface_data[np.argsort(surface_data[:,0])] # # Advect top surface - x2 = surface_data[:,0] + (surface_data[:,2] * dt) - z2 = surface_data[:,1] + (surface_data[:,3] * dt) - - - # # Spline top surface - f = interp1d(x2, z2, kind='cubic', fill_value='extrapolate') - - ### update surface tracer position - # surface_data[:,0] = (surface_data[:,0]) - surface_data[:,1] = f(surface_data[:,0]) + x_new = (surface_data[:,0] + (surface_data[:,2]*dt)) + y_new = (surface_data[:,1] + (surface_data[:,3]*dt)) + ## Spline top surface + f = interp1d(x_new, y_new, kind='cubic', fill_value='extrapolate') + + ''' interpolate new surface back onto original grid ''' + z_nd = f(self.nd_coords[:,0]) - ### gets the x and y coordinates from the tracers - x = dimensionalise(surface_data[:,0], u.kilometer).magnitude - z = dimensionalise(surface_data[:,1], u.kilometer).magnitude + ### Ve and Vs for loop to preserve original values + Ve_loop = np.zeros_like(z_nd, dtype='float64') + Vs_loop = np.zeros_like(z_nd, dtype='float64') ### time to diffuse surface based on Model dt - total_time = (dimensionalise(dt, u.year)).magnitude + total_time = dt '''Velocity surface process''' '''erosion dt for vel model''' + vel_for_surface = max(vx_data.max(), vy_data.max()) + Vel_for_surface = max(abs(nd_ve), abs(nd_ve), abs(vx_data.max()), abs(vy_data.max())) - Vel_for_surface = max(abs(self.erosionRate * u.kilometer / u.year),abs(self.sedimentationRate*u.kilometer / u.year), abs(dimensionalise(self.Model.velocityField.data.max(), u.kilometer/u.year))) - - - surface_dt_vel = (0.2 * (self.dx / Vel_for_surface.magnitude)) + surface_dt_vel = (0.2 * (self.min_dist / Vel_for_surface) ) - surface_time = min(surface_dt_vel, total_time) + surf_time = min(surface_dt_vel, total_time) - nts = math.ceil(total_time/surface_time) - surface_dt = total_time / nts + nts = math.ceil(total_time/surf_time) + + surf_dt = (total_time / nts) - print('SP total time:', round(total_time,2), 'years, timestep:', round(surface_dt,2), 'years, No. of its:', nts, flush=True) + print('SP total time:', dimensionalise(total_time, u.year), 'timestep:', dimensionalise(surf_dt, u.year), 'No. of its:', nts, flush=True) ### Velocity erosion/sedimentation rates for the surface for i in range(nts): - Ve_loop = np.where(z <= 0., 0., self.erosionRate) - Vs_loop = np.where(z >= 0., 0., self.sedimentationRate) + ''' determine if particle is above or below the original surface elevation ''' + ''' erosion function ''' + Ve_loop[:] = nd(0. * u.kilometer/u.year) + Ve_loop[(z_nd > nd(self.surfaceElevation))] = nd_ve + + ''' sedimentation function ''' + Vs_loop[:] = nd(0. * u.kilometer/u.year) + Vs_loop[(z_nd <= nd(self.surfaceElevation))] = nd_vs + dzdt = Vs_loop + Ve_loop - z[:] += dzdt*surface_dt + z_nd += (dzdt[:]*surf_dt) - x_nd = nd(x*u.kilometer) + ''' creates no movement condition near boundary ''' + ''' important when imposing a velocity as particles are easily deformed near the imposed condition''' + ''' This changes the height to the points original height ''' + resetArea_x = (self.nd_coords[:,0] < nd(self.updateSurfaceLB)) | (self.nd_coords[:,0] > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB)))) - z_nd = nd(z*u.kilometer) - ''' updates material near to boundary back to original coordinates ''' - z_original_surface = self.original_surface(x_nd) - z_nd[(x_nd < nd(self.updateSurfaceLB * u.kilometer)) | (x_nd > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB * u.kilometer))))] = z_original_surface[(x_nd < nd(self.updateSurfaceLB * u.kilometer)) | (x_nd > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB * u.kilometer))))] + z_nd[resetArea_x] = self.originalZ[resetArea_x] + ### creates function for the new surface that has eroded, to be broadcast back to nodes - f1 = interp1d(x_nd, z_nd, fill_value='extrapolate', kind='cubic') - + f1 = interp1d(self.nd_coords[:,0], z_nd, fill_value='extrapolate', kind='cubic') comm.barrier() '''broadcast the new surface''' ### broadcast function for the surface - f1 = comm.bcast(f1, root=root_proc) + f1 = comm.bcast(f1, root=0) + + + + ### update the z coord of the surface array + self.nd_coords[:,1] = f1(self.nd_coords[:,0]) comm.barrier() - ''' replaces the new diffused surface data, only changes z as x values don't change ''' - ### update the surface on individual nodes - self.surface_data_local[:,1] = f1(self.surface_data_local[:,0]) - ### update the global surface tracers - with self.Model.surface_tracers.deform_swarm(): - self.Model.surface_tracers.data[:,1] = f1(self.Model.surface_tracers.data[:,0]) + + ### has to be done on all procs due to an internal comm barrier in deform swarm (?) + with st.deform_swarm(): + st.data[:,1] = f1(st.data[:,0]) + + comm.barrier() + + ### update the time of the sediment and air material as sed & erosion occurs + if self.timeField: + ### Set newly deposited sediment time to 0 (to record deposition time) + self.Model.timeField.data[(self.Model.swarm.data[:,1] < f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] == self.airIndex)] = 0. + ### reset air material time back to the model time + self.Model.timeField.data[(self.Model.swarm.data[:,1] > f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] != self.airIndex)] = self.Model.timeField.data.max() '''Erode surface/deposit sed based on the surface''' ### update the material on each node according to the spline function for the surface @@ -972,12 +1061,11 @@ def solve(self, dt): self.Model.materialField.data[(self.Model.swarm.data[:,1] < f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] == self.airIndex)] = self.sedimentIndex - comm.barrier() return -class velocitySurface3D(SurfaceProcesses): +class velocitySurface_3D(SurfaceProcesses): """velocity surface erosion """ @@ -1073,21 +1161,23 @@ def __init__(self, airIndex, sedimentIndex, self.surfaceElevation = surfaceElevation.to(u.kilometer) self.Model = Model - self.originalSurface = None - self.z_new = None + self.originalZ = None + self.z_surf = None self.min_dist = None self.nd_coords = None - - ''' function to create grid for surface ''' + # we save the key of the passive tracer swarm, rather than the instance, because + # upon restarts the instance can be replaced + self.tkey = self.__class__.__name__+"_surface" def _init_model(self): - ''' creates a PT output ''' ### automatically non-dimensionalises the imput coords if they have a dim - self.Model.add_passive_tracers(name="surface", vertices=self.surfaceArray, advect=False) + self.Model.add_passive_tracers(name=self.tkey, vertices=self.surfaceArray, advect=False) - self.Model.surface_tracers.allow_parallel_nn = True + st = self.Model.passive_tracers[self.tkey] + assert( st != None, f"Error getting passive tracer {self.tkey}") + st.allow_parallel_nn = True self.nd_coords = nd(self.surfaceArray) @@ -1102,20 +1192,6 @@ def _init_model(self): ### create copy of original surface self.originalZ = self.nd_coords[:,2] - #### add variables for tracking that aren't included in UWGeo - self.Model.surface_tracers.z_coord = self.Model.surface_tracers.add_variable( dataType="double", count=1 ) - - self.Model.surface_tracers.ve = self.Model.surface_tracers.add_variable( dataType="double", count=1 ) - self.Model.surface_tracers.vs = self.Model.surface_tracers.add_variable( dataType="double", count=1 ) - - if self.Model.surface_tracers.data.size != 0: - ### erosion is downward (negative) - self.Model.surface_tracers.ve.data[:] = -1. * abs(self.ve_condition.evaluate(self.Model.surface_tracers.data)) - ### sedimentation is upward (positive) - self.Model.surface_tracers.vs.data[:] = 1. * abs(self.vs_condition.evaluate(self.Model.surface_tracers.data)) - - self.Model.surface_tracers.z_coord.data[:,0] = self.Model.surface_tracers.data[:,2] - comm.barrier() @@ -1124,64 +1200,257 @@ def _init_model(self): ### add fields to track ### track velocity field on tracers - self.Model.surface_tracers.add_tracked_field(self.Model.velocityField, - name="surface_vel", - units=u.centimeter/u.year, - dataType="float", count=self.Model.mesh.dim) +# self.Model.surface_tracers.add_tracked_field(self.Model.velocityField, +# name="surface_vel", +# units=u.centimeter/u.year, +# dataType="float", count=self.Model.mesh.dim) +# +# self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.particleCoordinates, +# name="coords", +# units=u.centimeter/u.year, +# dataType="float", count=self.Model.mesh.dim) +# - self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.particleCoordinates, - name="coords", - units=u.centimeter/u.year, - dataType="float", count=self.Model.mesh.dim) + # def solve(self, dt): - ## track the surface coordinates (could change to only show the height) - self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.z_coord, - name="topo_height", - units=u.kilometer, - dataType="float", count=1) + # ### evaluate on all nodes and get the tracer velocity on root proc + # tracer_velocity = self.Model.velocityField.evaluate_global(self.nd_coords) - ### track the erosion rate - self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.ve, - name="erosion_rate", - units=u.millimeter/u.year, - dataType="float", count=1) - ### track the sedimentation rate - self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.vs, - name="sedimentation_rate", - units=u.millimeter/u.year, - dataType="float", count=1) + # ### utilises the evaluate_global to get values that are across multiple CPUs on root CPU + # ve = (self.ve_condition.evaluate_global(self.nd_coords)) + # vs = (self.vs_condition.evaluate_global(self.nd_coords)) - comm.barrier() - def solve(self, dt): + # comm.barrier() + + + # if rank == 0: + + # ve = -1. * abs(ve) ### erode down(negative) + # vs = 1. * abs(vs) ### sed up (positive) + + # # # Advect top surface + # x_new = (self.nd_coords[:,0] + (tracer_velocity[:,0]*dt)) + # y_new = (self.nd_coords[:,1] + (tracer_velocity[:,1]*dt)) + # z_new = (self.nd_coords[:,2] + (tracer_velocity[:,2]*dt)) + + # ''' interpolate new surface back onto original grid ''' + # #### griddata seems to be okay, rbf was causing issues with memory usage in parallel + # z_nd = griddata((x_new, y_new), z_new, (self.nd_coords[:,0], self.nd_coords[:,1]), method=self.method).ravel() - ### evaluate on all nodes and get the tracer velocity on root proc - tracer_velocity = self.Model.velocityField.evaluate_global(self.nd_coords) + # ### Ve and Vs for loop to preserve original values + # Ve_loop = np.zeros_like(z_nd, dtype='float64') + # Vs_loop = np.zeros_like(z_nd, dtype='float64') + + + # ### time to diffuse surface based on Model dt + # total_time = dt + + # '''Velocity surface process''' + + # '''erosion dt for vel model''' + # Vel_for_surface = max(abs(vs).max(), abs(ve).max(), abs(tracer_velocity).max()) + + # surface_dt_vel = (0.2 * (self.min_dist / Vel_for_surface) ) + + # surf_time = min(surface_dt_vel, total_time) + + # nts = math.ceil(total_time/surf_time) + + # surf_dt = (total_time / nts) + + # print('SP total time:', dimensionalise(total_time, u.year), 'timestep:', dimensionalise(surf_dt, u.year), 'No. of its:', nts, flush=True) + + + # ### Velocity erosion/sedimentation rates for the surface + # for i in range(nts): + # ''' determine if particle is above or below the original surface elevation ''' + # ''' erosion function ''' + # Ve_loop[:] = nd(0. * u.kilometer/u.year) + # Ve_loop[(z_nd > nd(self.surfaceElevation))] = ve[:,0][(z_nd > nd(self.surfaceElevation))] + + # ''' sedimentation function ''' + # Vs_loop[:] = nd(0. * u.kilometer/u.year) + # Vs_loop[(z_nd <= nd(self.surfaceElevation))] = vs[:,0][(z_nd <= nd(self.surfaceElevation))] + + + # dzdt = Vs_loop + Ve_loop + + # z_nd += (dzdt[:]*surf_dt) + + + # ''' creates no movement condition near boundary ''' + # ''' important when imposing a velocity as particles are easily deformed near the imposed condition''' + # ''' This changes the height to the points original height ''' + # resetArea_x = (self.nd_coords[:,0] < nd(self.updateSurfaceLB)) | (self.nd_coords[:,0] > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB)))) + + # resetArea_y = (self.nd_coords[:,1] < nd(self.updateSurfaceBB)) | (self.nd_coords[:,1] > (nd(self.Model.maxCoord[1]) - (nd(self.updateSurfaceTB)))) + + + # z_nd[resetArea_x | resetArea_y] = self.originalZ[resetArea_x | resetArea_y] + + + # self.z_new = z_nd + + + # comm.barrier() + + # '''broadcast the new surface''' + # ### broadcast function for the surface + # self.z_new = comm.bcast(self.z_new, root=0) + + + # comm.barrier() + + # ### update the z coord of the surface array + # self.nd_coords[:,2] = self.z_new + + # comm.barrier() + + + # ### has to be done on all procs due to an internal comm barrier in deform swarm (?) + # with self.Model.surface_tracers.deform_swarm(): + # self.Model.surface_tracers.data[:,2] = griddata((self.nd_coords[:,0], self.nd_coords[:,1]), self.z_new, (self.Model.surface_tracers.data[:,0], self.Model.surface_tracers.data[:,1]), method=self.method).ravel() + + # comm.barrier() + + # if self.Model.surface_tracers.data.size != 0: + # ### update the surface only on procs that have the tracers + # self.Model.surface_tracers.z_coord.data[:,0] = self.Model.surface_tracers.data[:,2] + + # comm.barrier() + + + # ### cacluate surface for swarm particles + # z_new_surface = griddata((self.nd_coords[:,0], self.nd_coords[:,1]), self.z_new, (self.Model.swarm.data[:,0], self.Model.swarm.data[:,1]), method=self.method).ravel() + + # comm.barrier() + + # ### update the time of the sediment and air material as sed & erosion occurs + # if self.timeField: + # ### Set newly deposited sediment time to 0 (to record deposition time) + # self.Model.timeField.data[(self.Model.swarm.data[:,2] < z_new_surface) & (self.Model.materialField.data[:,0] == self.airIndex) ] = 0. + # ### reset air material time back to the model time + # self.Model.timeField.data[(self.Model.swarm.data[:,2] >= z_new_surface) & (self.Model.materialField.data[:,0] != self.airIndex) ] = self.Model.timeField.data.max() + + # '''Erode surface/deposit sed based on the surface''' + # ### update the material on each node according to the spline function for the surface + # self.Model.materialField.data[(self.Model.swarm.data[:,2] >= z_new_surface) & (self.Model.materialField.data[:,0] != self.airIndex) ] = self.airIndex + # self.Model.materialField.data[(self.Model.swarm.data[:,2] < z_new_surface) & (self.Model.materialField.data[:,0] == self.airIndex) ] = self.sedimentIndex + + # comm.barrier() + + + + # return + + def solve(self, dt): + st = self.Model.passive_tracers[self.tkey] + assert( st != None, f"Error getting passive tracer {self.tkey}") + if st.data.shape[0] > 0: + x = np.ascontiguousarray(st.data[:,0]) + y = np.ascontiguousarray(st.data[:,1]) + z = np.ascontiguousarray(st.data[:,2]) + + ### evaluate to get the tracer velocity + tracer_velocity = self.Model.velocityField.evaluate(st.data) + vx = np.ascontiguousarray(tracer_velocity[:,0]) + vy = np.ascontiguousarray(tracer_velocity[:,1]) + vz = np.ascontiguousarray(tracer_velocity[:,2]) + + ### evaluate to get the ve and vs values + ve = np.ascontiguousarray(self.ve_condition.evaluate(st.data)) + vs = np.ascontiguousarray(self.vs_condition.evaluate(st.data)) + else: + x = np.array([None], dtype='float64') + y = np.array([None], dtype='float64') + z = np.array([None], dtype='float64') + tracer_velocity = np.array([None], dtype='float64') + vx = np.array([None], dtype='float64') + vy = np.array([None], dtype='float64') + vz = np.array([None], dtype='float64') + ve = np.array([None], dtype='float64') + vs = np.array([None], dtype='float64') - ### utilises the evaluate_global to get values that are across multiple CPUs on root CPU - ve = (self.ve_condition.evaluate_global(self.nd_coords)) - vs = (self.vs_condition.evaluate_global(self.nd_coords)) comm.barrier() + sendcounts = np.array(comm.gather(len(x), root=0)) + + comm.barrier() if rank == 0: + ### creates dummy data on all nodes to store the surface + # surface_data = np.zeros((npoints,2)) + x_data = np.zeros((sum(sendcounts)), dtype='float64') + y_data = np.zeros((sum(sendcounts)), dtype='float64') + z_data = np.zeros((sum(sendcounts)), dtype='float64') - ve = -1. * abs(ve) ### erode down(negative) - vs = 1. * abs(vs) ### sed up (positive) + vx_data = np.zeros((sum(sendcounts)), dtype='float64') + vy_data = np.zeros((sum(sendcounts)), dtype='float64') + vz_data = np.zeros((sum(sendcounts)), dtype='float64') + + ve_data = np.zeros((sum(sendcounts)), dtype='float64') + vs_data = np.zeros((sum(sendcounts)), dtype='float64') + + else: + x_data = None + y_data = None + z_data = None + vx_data = None + vy_data = None + vz_data = None + ve_data = None + vs_data = None + + comm.Gatherv(sendbuf=x, recvbuf=(x_data, sendcounts), root=0) + comm.Gatherv(sendbuf=y, recvbuf=(y_data, sendcounts), root=0) + comm.Gatherv(sendbuf=z, recvbuf=(z_data, sendcounts), root=0) + + ### gather velocity values + comm.Gatherv(sendbuf=vx, recvbuf=(vx_data, sendcounts), root=0) + comm.Gatherv(sendbuf=vy, recvbuf=(vy_data, sendcounts), root=0) + comm.Gatherv(sendbuf=vz, recvbuf=(vz_data, sendcounts), root=0) + + ### Gather SP values + comm.Gatherv(sendbuf=ve, recvbuf=(ve_data, sendcounts), root=0) + comm.Gatherv(sendbuf=vs, recvbuf=(vs_data, sendcounts), root=0) + + + + if rank == 0: + + surface_data = np.zeros((len(x_data), 8), dtype='float64') + surface_data[:,0] = x_data + surface_data[:,1] = y_data + surface_data[:,2] = z_data + + surface_data[:,3] = vx_data + surface_data[:,4] = vy_data + surface_data[:,5] = vz_data + + surface_data[:,6] = ve_data + surface_data[:,7] = vs_data + + surface_data = surface_data[~np.isnan(surface_data[:,0])] + # surface_data = surface_data[np.argsort(surface_data[:,0])] + + ve = -1. * abs(surface_data[:,6]) ### erode down(negative) + vs = 1. * abs(surface_data[:,7]) ### sed up (positive) # # Advect top surface - x_new = (self.nd_coords[:,0] + (tracer_velocity[:,0]*dt)) - y_new = (self.nd_coords[:,1] + (tracer_velocity[:,1]*dt)) - z_new = (self.nd_coords[:,2] + (tracer_velocity[:,2]*dt)) + x_new = (surface_data[:,0] + (surface_data[:,3]*dt)) + y_new = (surface_data[:,1] + (surface_data[:,4]*dt)) + z_new = (surface_data[:,2] + (surface_data[:,5]*dt)) ''' interpolate new surface back onto original grid ''' #### griddata seems to be okay, rbf was causing issues with memory usage in parallel - z_nd = griddata((x_new, y_new), z_new, (self.nd_coords[:,0], self.nd_coords[:,1]), method=self.method).ravel() + # z_nd = griddata((x_new, y_new), z_new, (self.nd_coords[:,0], self.nd_coords[:,1]), method=self.method).ravel() + z_nd = griddata((x_new, y_new), z_new, (surface_data[:,0], surface_data[:,1]), method=self.method).ravel() ### Ve and Vs for loop to preserve original values @@ -1195,7 +1464,7 @@ def solve(self, dt): '''Velocity surface process''' '''erosion dt for vel model''' - Vel_for_surface = max(abs(vs).max(), abs(ve).max(), abs(tracer_velocity).max()) + Vel_for_surface = surface_data[:,3:].max() surface_dt_vel = (0.2 * (self.min_dist / Vel_for_surface) ) @@ -1213,11 +1482,11 @@ def solve(self, dt): ''' determine if particle is above or below the original surface elevation ''' ''' erosion function ''' Ve_loop[:] = nd(0. * u.kilometer/u.year) - Ve_loop[(z_nd > nd(self.surfaceElevation))] = ve[:,0][(z_nd > nd(self.surfaceElevation))] + Ve_loop[(z_nd > nd(self.surfaceElevation))] = ve[(z_nd > nd(self.surfaceElevation))] ''' sedimentation function ''' Vs_loop[:] = nd(0. * u.kilometer/u.year) - Vs_loop[(z_nd <= nd(self.surfaceElevation))] = vs[:,0][(z_nd <= nd(self.surfaceElevation))] + Vs_loop[(z_nd <= nd(self.surfaceElevation))] = vs[(z_nd <= nd(self.surfaceElevation))] dzdt = Vs_loop + Ve_loop @@ -1228,52 +1497,56 @@ def solve(self, dt): ''' creates no movement condition near boundary ''' ''' important when imposing a velocity as particles are easily deformed near the imposed condition''' ''' This changes the height to the points original height ''' - resetArea_x = (self.nd_coords[:,0] < nd(self.updateSurfaceLB)) | (self.nd_coords[:,0] > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB)))) + resetArea_x = (surface_data[:,0] < nd(self.updateSurfaceLB)) | (surface_data[:,0] > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB)))) - resetArea_y = (self.nd_coords[:,1] < nd(self.updateSurfaceBB)) | (self.nd_coords[:,1] > (nd(self.Model.maxCoord[1]) - (nd(self.updateSurfaceTB)))) + resetArea_y = (surface_data[:,1] < nd(self.updateSurfaceBB)) | (surface_data[:,1] > (nd(self.Model.maxCoord[1]) - (nd(self.updateSurfaceTB)))) z_nd[resetArea_x | resetArea_y] = self.originalZ[resetArea_x | resetArea_y] - self.z_new = z_nd + self.z_surf = griddata((surface_data[:,0], surface_data[:,1]), z_nd, (self.nd_coords[:,0], self.nd_coords[:,1]), method=self.method).ravel() comm.barrier() '''broadcast the new surface''' ### broadcast function for the surface - self.z_new = comm.bcast(self.z_new, root=0) + self.z_surf = comm.bcast(self.z_surf, root=0) comm.barrier() ### update the z coord of the surface array - self.nd_coords[:,2] = self.z_new #griddata((self.nd_coords[:,0], self.nd_coords[:,1]), self.z_new, (self.nd_coords[:,0], self.nd_coords[:,1]), method=self.method).ravel() + self.nd_coords[:,2] = self.z_surf comm.barrier() - if self.Model.surface_tracers.data.size != 0: - ### update the surface only on procs that have the tracers - self.Model.surface_tracers.z_coord.data[:,0] = self.Model.surface_tracers.data[:,2] + ### has to be done on all procs due to an internal comm barrier in deform swarm (?) + with st.deform_swarm(): + st.data[:,2] = griddata((self.nd_coords[:,0], self.nd_coords[:,1]), self.z_surf, (st.data[:,0], st.data[:,1]), method=self.method).ravel() comm.barrier() - - ### has to be done on all procs due to an internal comm barrier in deform swarm (?) - with self.Model.surface_tracers.deform_swarm(): - self.Model.surface_tracers.data[:,2] = griddata((self.nd_coords[:,0], self.nd_coords[:,1]), self.z_new, (self.Model.surface_tracers.data[:,0], self.Model.surface_tracers.data[:,1]), method=self.method).ravel() + if st.data.size != 0: + ### update the surface only on procs that have the tracers + st.z_coord.data[:,0] = st.data[:,2] comm.barrier() ### cacluate surface for swarm particles - # z_new_surface = rbf1(self.Model.swarm.data[:,0], self.Model.swarm.data[:,1]) - z_new_surface = griddata((self.nd_coords[:,0], self.nd_coords[:,1]), self.z_new, (self.Model.swarm.data[:,0], self.Model.swarm.data[:,1]), method=self.method).ravel() + z_new_surface = griddata((self.nd_coords[:,0], self.nd_coords[:,1]), self.z_surf, (self.Model.swarm.data[:,0], self.Model.swarm.data[:,1]), method=self.method).ravel() comm.barrier() + ### update the time of the sediment and air material as sed & erosion occurs + if self.timeField: + ### Set newly deposited sediment time to 0 (to record deposition time) + self.Model.timeField.data[(self.Model.swarm.data[:,2] < z_new_surface) & (self.Model.materialField.data[:,0] == self.airIndex) ] = 0. + ### reset air material time back to the model time + self.Model.timeField.data[(self.Model.swarm.data[:,2] >= z_new_surface) & (self.Model.materialField.data[:,0] != self.airIndex) ] = self.Model.timeField.data.max() '''Erode surface/deposit sed based on the surface''' ### update the material on each node according to the spline function for the surface @@ -1282,6 +1555,5 @@ def solve(self, dt): comm.barrier() - - return + diff --git a/underworld/_version.py b/underworld/_version.py index bd19c7620..41cf90435 100644 --- a/underworld/_version.py +++ b/underworld/_version.py @@ -1 +1 @@ -__version__ = "2.14.2b" +__version__ = "2.15.0b" From a2b3c137406a467dca9278139fa07616bfdc1955 Mon Sep 17 00:00:00 2001 From: Julian Giordani Date: Tue, 31 Oct 2023 20:41:57 +1100 Subject: [PATCH 2/6] starting 2.16.x --- underworld/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/underworld/_version.py b/underworld/_version.py index 41cf90435..a73fdf9a1 100644 --- a/underworld/_version.py +++ b/underworld/_version.py @@ -1 +1 @@ -__version__ = "2.15.0b" +__version__ = "2.16.x" From 4110f1136ce7468837f44684168ce59fdb0c7a18 Mon Sep 17 00:00:00 2001 From: Julian Giordani Date: Mon, 13 Nov 2023 20:30:52 +1100 Subject: [PATCH 3/6] Moving to a specific badlands version & (arm compatible version of) jupyter-server extension command --- docs/development/docker/underworld2/Dockerfile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/development/docker/underworld2/Dockerfile b/docs/development/docker/underworld2/Dockerfile index 38533ddc1..b7224a76e 100644 --- a/docs/development/docker/underworld2/Dockerfile +++ b/docs/development/docker/underworld2/Dockerfile @@ -130,7 +130,7 @@ RUN pip3 install -vvv . RUN pip3 install setuptools --force-reinstall --no-cache-dir \ && pip3 install --no-cache-dir \ git+https://github.com/drufat/triangle.git \ - badlands==2.2.4 \ + git+https://github.com/badlands-model/badlands.git@6ec949472ddd4c012d8694ce5f994b37ef143029#subdirectory=badlands \ jupyter_contrib_nbextensions RUN pip3 freeze >/opt/requirements.txt @@ -147,7 +147,7 @@ COPY --from=build --chown=$NB_USER:users /usr/local /usr/local # must make directory before COPY into it for permissions to work (!!!) RUN mkdir -p $NB_HOME/workspace $NB_HOME/Underworld/UWGeodynamics \ && chown $NB_USER:users -R $NB_HOME \ -&& jupyter serverextension enable --sys-prefix jupyter_server_proxy +&& jupyter-server extension enable --sys-prefix jupyter_server_proxy #Copy in examples, tests, etc. COPY --chown=$NB_USER:users ./docs/examples $NB_HOME/Underworld/examples From 74ec60689636825002abf42e222d62168411f20c Mon Sep 17 00:00:00 2001 From: Neng Lu <57476594+NengLu@users.noreply.github.com> Date: Mon, 15 Jan 2024 13:28:55 +1100 Subject: [PATCH 4/6] update freesurf to 3D in uwg (#683) 1. update the free surface function in UWGeodynamics to a 3D version (see details in _freesurface.py). 2. add an example 1_23_05_FreeSurface_3D_Relaxation in docs/UWGeodynamics/examples/. --- CHANGES.md | 9 +- .../1_23_05_FreeSurface_3D_Relaxation.ipynb | 253 ++++++++++++++++++ .../examples/images/3D_FreeSurface.gif | Bin 0 -> 1468348 bytes .../images/3D_FreeSurface_Topography.png | Bin 0 -> 65264 bytes .../examples/images/crameri2012case1.png | Bin 0 -> 39828 bytes .../examples/images/crameri2012case2.png | Bin 0 -> 41289 bytes .../examples/images/kaus2010RTI.gif | Bin 0 -> 10629147 bytes underworld/UWGeodynamics/_freesurface.py | 55 ++-- 8 files changed, 298 insertions(+), 19 deletions(-) mode change 100644 => 100755 CHANGES.md create mode 100755 docs/UWGeodynamics/examples/1_23_05_FreeSurface_3D_Relaxation.ipynb create mode 100644 docs/UWGeodynamics/examples/images/3D_FreeSurface.gif create mode 100644 docs/UWGeodynamics/examples/images/3D_FreeSurface_Topography.png create mode 100644 docs/UWGeodynamics/examples/images/crameri2012case1.png create mode 100644 docs/UWGeodynamics/examples/images/crameri2012case2.png create mode 100644 docs/UWGeodynamics/examples/images/kaus2010RTI.gif diff --git a/CHANGES.md b/CHANGES.md old mode 100644 new mode 100755 index fa95f61a6..796d84956 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,5 +1,10 @@ CHANGES: Underworld2 ======================= +Release 2.16.0 [2024] +--------------------------- +New: +* New 3D free surface implementation (tested). The vertical coordinates of the mesh nodes are rebuilt by being advected and interpolated from the surface velocities, and then solving the steady state heat equation to get a uniform distribution. See the related example in docs/UWGeodynamics/examples + Release 2.15.0 [2023-04-19] --------------------------- @@ -12,7 +17,7 @@ Changes: Fixes: * UWGeodynamics - add dynamic heating back into the advection diffusion solver, - https://github.com/underworldcode/underworld2/issues/669 + https://github.com/underworldcode/underworld2/issues/669 * Using updated Badlands-2.2.3 without license issue. @@ -162,7 +167,7 @@ Docker: * Images minimised with unnecessary items removed. * XVFB no longer required for image generation within container. - + API changes: * `glucifer` module moved inside `underworld` and renamed `visualisation`. diff --git a/docs/UWGeodynamics/examples/1_23_05_FreeSurface_3D_Relaxation.ipynb b/docs/UWGeodynamics/examples/1_23_05_FreeSurface_3D_Relaxation.ipynb new file mode 100755 index 000000000..6fd74aeb1 --- /dev/null +++ b/docs/UWGeodynamics/examples/1_23_05_FreeSurface_3D_Relaxation.ipynb @@ -0,0 +1,253 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "25381a9f-72ce-4ed9-9d31-5eaf2daa2ae1", + "metadata": {}, + "source": [ + "3D_Relaxation\n", + "======\n", + "\n", + "This notebook reproduce the a cosine perturbation of the surface relaxation with true free surface. Loading of Earth's surface can be described with an initial periodic surface displacement of a viscous fluid within an infinite half space, the solution of which is outlined in Turcotte and Schubert (1982), 6.10 Postglacial Rebound. The surface decreases exponentially with time and is dependent on the magnitude, $w_m$, and wavelength $\\lambda$ of the perturbation, and the viscosity, $\\eta$ and density, $\\rho$ of the fluid,\n", + "\n", + "$$ w = w_m exp\\Big(\\frac{-\\lambda \\rho g t}{4\\pi\\eta}\\Big) $$\n", + "\n", + "where $w$ is displacement, $w_m$ the initial load magnitude, $g$ gravity, $t$ time. This solution can be charaterised by the relaxation time, $t_{relax} = 4\\pi\\eta / \\rho g \\lambda $, the time taken for the initial load to decrease by $e^{-1}$. The solution for an elastic material with the equivalent load produces the same magnitude of displacement instantaneously.\n", + "\n", + "\n", + "**Keywords:** Free surface\n", + "\n", + " \n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f1419bd-076d-4aa8-8093-3f9265f52713", + "metadata": {}, + "outputs": [], + "source": [ + "from underworld import UWGeodynamics as GEO\n", + "from underworld import visualisation as vis\n", + "import underworld as uw\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bbcb3ae5-34ac-4d7f-879e-b6b4263aaa63", + "metadata": {}, + "outputs": [], + "source": [ + "u = GEO.UnitRegistry\n", + "ndim = GEO.non_dimensionalise\n", + "dim = GEO.dimensionalise\n", + "\n", + "# scaling 3: vel\n", + "half_rate = 1.0 * u.centimeter / u.year\n", + "model_length = 100. * u.kilometer\n", + "bodyforce = 3300 * u.kilogram / u.metre**3 * 9.81 * u.meter / u.second**2\n", + "\n", + "KL = model_length\n", + "Kt = KL / half_rate\n", + "KM = bodyforce * KL**2 * Kt**2\n", + "\n", + "GEO.scaling_coefficients[\"[length]\"] = KL\n", + "GEO.scaling_coefficients[\"[time]\"] = Kt\n", + "GEO.scaling_coefficients[\"[mass]\"]= KM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d00b5e0a-95c3-4540-9024-79f05bc62aad", + "metadata": {}, + "outputs": [], + "source": [ + "zres = 8\n", + "xres = zres*4\n", + "yres = zres*4\n", + "figsize = (800,400)\n", + "\n", + "xmin, xmax = -200 * u.kilometer, 200 * u.kilometer\n", + "ymin, ymax = -200 * u.kilometer, 200 * u.kilometer\n", + "zmin, zmax = -100 * u.kilometer, 0 * u.kilometer\n", + "\n", + "eta = ndim(1e21 * u.pascal * u.second)\n", + "density = ndim(3300 * u.kilogram / u.metre**3)\n", + "gravity = ndim(9.81 * u.meter / u.second**2)\n", + "w_m = ndim(5.0 * u.kilometer)\n", + "Lambda = ndim(100.0 * u.kilometer) \n", + "\n", + "densityM = density\n", + "viscM = eta\n", + "ND_gravity = gravity\n", + "\n", + "def perturbation(x):\n", + " return w_m * np.cos(2.*np.pi*(x)/Lambda)\n", + "\n", + "# analytic solution\n", + "xMax = ndim(xmax - xmin)\n", + "x = np.linspace(0, xMax, 200+1)\n", + "w_0 = perturbation(x)\n", + "t_relax = 4 * np.pi * eta / (Lambda * density * gravity)\n", + "tMax = t_relax * 5 \n", + "t = np.linspace(0, tMax, 100 * 10 + 1)\n", + "w_t = w_m * np.exp(-1.*t/t_relax)\n", + "\n", + "max_time = dim(tMax,u.kiloyear)\n", + "dt_set = dim(t_relax*1e-2,u.kiloyear)\n", + "save_every = 5\n", + "checkpoint_interval = dt_set*save_every\n", + "\n", + "Model = GEO.Model(elementRes=(xres,yres,zres),\n", + " minCoord=(xmin,ymin,zmin), \n", + " maxCoord=(xmax,ymax,zmax),\n", + " gravity=(0.0, 0.0,-9.81 * u.meter / u.second**2))\n", + "\n", + "fdir = \"1_23_05_FreeSurface_3D_Relaxation_zres\"+str(zres)+\"/\"\n", + "Model.outputDir = fdir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60244a41-ea7d-44c4-b5b1-f87ee6ec7690", + "metadata": {}, + "outputs": [], + "source": [ + "MShape = GEO.shapes.Layer3D(top=Model.top,bottom=Model.bottom)\n", + "ma = Model.add_material(name=\"material\",shape=MShape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "291c22b7-7a3e-41a7-bfb9-d36775cb61d8", + "metadata": {}, + "outputs": [], + "source": [ + "def perturbation3D(x,y):\n", + " return w_m * np.cos(2.*np.pi*(x)/Lambda)\n", + "\n", + "minCoord = tuple([GEO.nd(val) for val in Model.minCoord])\n", + "maxCoord = tuple([GEO.nd(val) for val in Model.maxCoord])\n", + "\n", + "init_mesh = uw.mesh.FeMesh_Cartesian(elementType=Model.elementType,\n", + " elementRes=Model.elementRes,\n", + " minCoord=minCoord,\n", + " maxCoord=maxCoord,\n", + " periodic=Model.periodic)\n", + "\n", + "TField = init_mesh.add_variable(nodeDofCount=1)\n", + "TField.data[:, 0] = init_mesh.data[:, -1].copy()\n", + "\n", + "top = Model.top_wall\n", + "bottom = Model.bottom_wall\n", + "conditions = uw.conditions.DirichletCondition(variable=TField,indexSetsPerDof=(top + bottom,))\n", + "system = uw.systems.SteadyStateHeat(\n", + " temperatureField=TField,\n", + " fn_diffusivity=1.0,\n", + " conditions=conditions)\n", + "solver = uw.systems.Solver(system)\n", + "\n", + "x = init_mesh.data[top,0]\n", + "y = init_mesh.data[top,1]\n", + "TField.data[top, 0] = perturbation3D(x,y)\n", + "\n", + "solver.solve()\n", + "with Model.mesh.deform_mesh():\n", + " Model.mesh.data[:, -1] = TField.data[:, 0].copy()\n", + "\n", + "Model.population_control.repopulate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b37ca13-01a2-49cf-9f64-32a9d668a04e", + "metadata": {}, + "outputs": [], + "source": [ + "# Fig = vis.Figure(resolution=figsize,rulers=True,margin = 80,axis=True)\n", + "# Fig.Mesh(Model.mesh)\n", + "# lv = Fig.window()\n", + "# lv.rotate('x',-45)\n", + "# lv.redisplay()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b366503-56c0-4b8b-af42-a093d69d5b7a", + "metadata": {}, + "outputs": [], + "source": [ + "ma.density = 3300. * u.kilogram / u.metre**3\n", + "ma.viscosity = 1e21 * u.pascal * u.second\n", + "Model.set_velocityBCs(left=[0.0, None, None],right=[0.0,None, None],\n", + " front=[None, 0.0, None], back=[None, 0.0, None],\n", + " bottom=[0.0, 0.0, 0.0],)\n", + "Model.freeSurface = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c25abe0-36c7-425a-8db6-ccc6eb1e7bbb", + "metadata": {}, + "outputs": [], + "source": [ + "# Fig = vis.Figure(resolution=figsize,rulers=True,margin = 80,axis=True)\n", + "# Fig.Points(Model.swarm, Model.materialField,fn_size=2.0,discrete=True,colourBar=False)\n", + "# lv = Fig.window()\n", + "# lv.rotate('x',-45)\n", + "# lv.redisplay()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa60d111-0ca5-4422-a774-5df68b9c5ab1", + "metadata": {}, + "outputs": [], + "source": [ + "Model.solver.set_inner_method(\"mg\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": 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