diff --git a/idaes_examples/notebooks/docs/power_gen/ngcc/gas_turbine_init.json.gz b/idaes_examples/notebooks/docs/power_gen/ngcc/gas_turbine_init.json.gz
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diff --git a/idaes_examples/notebooks/docs/power_gen/ngcc/hrsg_init.json.gz b/idaes_examples/notebooks/docs/power_gen/ngcc/hrsg_init.json.gz
new file mode 100644
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diff --git a/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc.ipynb b/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc.ipynb
index 084f9bbc..6b65abf4 100644
--- a/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc.ipynb
+++ b/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "c886cc60",
"metadata": {
"tags": [
@@ -51,7 +51,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "82c19b96",
"metadata": {},
"outputs": [],
@@ -84,7 +84,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "0e45525b",
"metadata": {},
"outputs": [],
@@ -114,7 +114,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "42e0b37d",
"metadata": {},
"outputs": [],
@@ -140,17 +140,98 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "9913783b",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2024-05-29 22:45:47 [INFO] idaes.init.fs: NGCC load initial from ngcc_init.json.gz\n",
+ "Ipopt 3.13.2: nlp_scaling_method=user-scaling\n",
+ "tol=1e-06\n",
+ "max_iter=200\n",
+ "linear_solver=ma57\n",
+ "ma57_pivtol=1e-05\n",
+ "ma57_pivtolmax=0.1\n",
+ "option_file_name=C:\\Users\\javal\\AppData\\Local\\Temp\\tmpcj6a2lhz_ipopt.opt\n",
+ "\n",
+ "Using option file \"C:\\Users\\javal\\AppData\\Local\\Temp\\tmpcj6a2lhz_ipopt.opt\".\n",
+ "\n",
+ "\n",
+ "******************************************************************************\n",
+ "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
+ " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
+ " For more information visit http://projects.coin-or.org/Ipopt\n",
+ "\n",
+ "This version of Ipopt was compiled from source code available at\n",
+ " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n",
+ " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n",
+ " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n",
+ "\n",
+ "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n",
+ " for large-scale scientific computation. All technical papers, sales and\n",
+ " publicity material resulting from use of the HSL codes within IPOPT must\n",
+ " contain the following acknowledgement:\n",
+ " HSL, a collection of Fortran codes for large-scale scientific\n",
+ " computation. See http://www.hsl.rl.ac.uk.\n",
+ "******************************************************************************\n",
+ "\n",
+ "This is Ipopt version 3.13.2, running with linear solver ma57.\n",
+ "\n",
+ "Number of nonzeros in equality constraint Jacobian...: 7661\n",
+ "Number of nonzeros in inequality constraint Jacobian.: 0\n",
+ "Number of nonzeros in Lagrangian Hessian.............: 5948\n",
+ "\n",
+ "Total number of variables............................: 2404\n",
+ " variables with only lower bounds: 87\n",
+ " variables with lower and upper bounds: 1447\n",
+ " variables with only upper bounds: 0\n",
+ "Total number of equality constraints.................: 2404\n",
+ "Total number of inequality constraints...............: 0\n",
+ " inequality constraints with only lower bounds: 0\n",
+ " inequality constraints with lower and upper bounds: 0\n",
+ " inequality constraints with only upper bounds: 0\n",
+ "\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 0 0.0000000e+00 3.50e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
+ "Reallocating memory for MA57: lfact (111709)\n",
+ " 1 0.0000000e+00 3.49e-01 1.12e+04 -1.0 3.06e+03 - 9.90e-01 9.90e-01h 1\n",
+ " 2 0.0000000e+00 3.15e-03 5.15e+02 -1.0 3.02e+03 - 9.89e-01 9.91e-01h 1\n",
+ " 3 0.0000000e+00 2.95e-07 9.98e+02 -1.0 3.74e+01 - 9.90e-01 1.00e+00h 1\n",
+ "\n",
+ "Number of Iterations....: 3\n",
+ "\n",
+ " (scaled) (unscaled)\n",
+ "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n",
+ "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n",
+ "Constraint violation....: 2.9462171369232237e-07 2.9462171369232237e-07\n",
+ "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n",
+ "Overall NLP error.......: 2.9462171369232237e-07 2.9462171369232237e-07\n",
+ "\n",
+ "\n",
+ "Number of objective function evaluations = 4\n",
+ "Number of objective gradient evaluations = 4\n",
+ "Number of equality constraint evaluations = 4\n",
+ "Number of inequality constraint evaluations = 0\n",
+ "Number of equality constraint Jacobian evaluations = 4\n",
+ "Number of inequality constraint Jacobian evaluations = 0\n",
+ "Number of Lagrangian Hessian evaluations = 3\n",
+ "Total CPU secs in IPOPT (w/o function evaluations) = 0.081\n",
+ "Total CPU secs in NLP function evaluations = 1.193\n",
+ "\n",
+ "EXIT: Optimal Solution Found.\n"
+ ]
+ }
+ ],
"source": [
"m = pyo.ConcreteModel()\n",
"m.fs = ngcc.NgccFlowsheet(dynamic=False)\n",
"iscale.calculate_scaling_factors(m)\n",
"m.fs.initialize(\n",
- " load_from=None,\n",
- " save_to=None,\n",
+ " load_from='ngcc_init.json.gz',\n",
+ " save_to='ngcc_init.json.gz',\n",
")\n",
"res = solver.solve(m, tee=True)"
]
@@ -167,10 +248,2533 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "ca2cf4a0",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "Gas Turbine Section\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "HRSG Section\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "Steam Turbine Section\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"def display_pfd():\n",
" print(\"\\n\\nGas Turbine Section\\n\")\n",
@@ -198,7 +2802,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "70964b53",
"metadata": {},
"outputs": [],
@@ -221,10 +2825,31 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"id": "3602759a",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"from matplotlib import pyplot as plt\n",
"\n",
@@ -267,7 +2892,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"id": "cd410c4b",
"metadata": {},
"outputs": [],
@@ -331,15 +2956,28 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"id": "b97e03c7",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "AssertionError",
+ "evalue": "Numerical issues found (2).",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[20], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01midaes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DiagnosticsToolbox\n\u001b[0;32m 3\u001b[0m dt\u001b[38;5;241m=\u001b[39mDiagnosticsToolbox(m)\n\u001b[1;32m----> 4\u001b[0m \u001b[43mdt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massert_no_numerical_warnings\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 5\u001b[0m dt\u001b[38;5;241m.\u001b[39massert_no_structural_warnings(ignore_evaluation_errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+ "File \u001b[1;32mc:\\users\\javal\\desktop\\internship\\idaes-pse\\idaes\\core\\util\\model_diagnostics.py:1418\u001b[0m, in \u001b[0;36mDiagnosticsToolbox.assert_no_numerical_warnings\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1416\u001b[0m warnings, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_collect_numerical_warnings()\n\u001b[0;32m 1417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(warnings) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m-> 1418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumerical issues found (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(warnings)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m).\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "\u001b[1;31mAssertionError\u001b[0m: Numerical issues found (2)."
+ ]
+ }
+ ],
"source": [
"from idaes.core.util import DiagnosticsToolbox\n",
"\n",
"dt=DiagnosticsToolbox(m)\n",
- "dt.assert_no_numerical_warnings(ignore_parallel_components=True)\n",
+ "dt.assert_no_numerical_warnings()\n",
"dt.assert_no_structural_warnings(ignore_evaluation_errors=True)"
]
}
diff --git a/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_init.json.gz b/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_init.json.gz
new file mode 100644
index 00000000..323e55d0
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