DeepBubbleVelocimetry for evaluating the bubble velocity field

This is a project of Multiphase flow & Flow visualization Lab. The purpose of the project is to obtain the bubble velocity field from experimental images using CNN-based optical flow model. More information can be found in the paper (Choi et al. 2022).

The codes are based on PWC_Net (Sun et al. 2018) with the pre-trained weights. The tensorflow version can be found here.

The output of the model is as follows:

• velocity field (two dimensional array in techplot format, or it can be read through wordpad or text in Window OS)
• velocity field contour (png)

The repository includes:

• Source code of implementation of PWC-Net based on the finetuned weights.
• Source code to generate the velocity field (techplot file) and its contour (png).

The examples of the result is shown below:

• Application for bubble plume with various void fractions

• Comparison with the PTV and CNN-based optical flow for dense bubble plume

• Effect of the training (it is seen that the horizontal vectors from the weakly trained model are inaccurate)

Tested environment

This code was tested on the below environment.

• NVIDIA RTX 2080 ti
• Driver 440.95.01
• CUDA 10.2
• cuDNN 7.6.5
• Python 3.7
• TensorFlow 1.14.0
• Keras 2.2.5 (If compatibility issue occurs, please refer to the original PWC-Net link)

Preparing the input

Prepare two consecutive bubble images (format of JPG or PNG or TIF) and one mask image.

• For example, Img_0001.png, Img_0002.png, and msk_0001.png
• (Here, the square size is recommended, e.g., 300 x 300 pixels)

How to test your own bubble image/video

1. Clone this repository

2. Install dependencies (If compatibility issue occurs, please refer to the original PWC-Net link)

3. Download trained weights from this link.

4. Run prediction script (CNN_OpticalFlow.ipynb) in Code/ directory to obtain the velocity field (supports only 3-channel jpg image).

Generating the synthetic bubble images

• Code is attached in the "SyntheticBubbleImage" folder.
• The density, velocity, magnitude of deformation (or the light noise) can be controlled using code.
• Output of the code is as follows: Two consecutive bubble images and one flow file (visualized by color contour below).

Any comments/questions are welcome. Please contact to [email protected]