Learning High-Performance Long-term Temporal Evolution for Fluid Flow

Install Mantaflow first

This installation guide focusses on Ubuntu 14.04 as a distribution. The process will however look very similar with other distributions, the main differences being the package manager and library package names.

First, install a few pre-requisites:

sudo apt-get install cmake g++ git python3-dev qt5-qmake qt5-default

Then, change to the directory to install the source code in, and obtain the current sources from the repository (or alternatively download and extract a source code package)

cd <gitdir>

To build the project using CMake, set up a build directory and choose the build options (explanation)

mkdir mantaflow/build

cd mantaflow/build

cmake .. -DGUI=ON -DOPENMP=ON -DNUMPY=ON -DPYTHON_VERSION=3.6

make -j4

That's it! You can now test mantaflow using an example scene

./manta ../scenes/examples/simpleplume.py

Common Linux problems:

• In conjunction with tensorflow, it can easily happen these days that you have multiple version of python installed. If cmake for mantaflow finds a different one than you're using for tensorflow, you will get errors such as ''ImportError: no module named XXX''. To fix this problem, manually select a python version in cmake with -DPYTHON_VERSION=X.Y
• It is also possible to directly specify the directory in which the python installation resides as follows:
  • DPYTHON_INCLUDE_DIR=/PATH/TO/PYTHONENV/include/python3.6m
  • DPYTHON_LIBRARY=/PATH/TO/PYTHONENV/lib/libpython3.6m.so

Further information on the installation process can be found on the project website http://mantaflow.com/.

Model Training

Open the source directory cd <gitdir>/src

2D Long-term Prediction model for density can be trained with

python DensityPrediction2D.py -m train -r 64 --density_train_path path_to_density_data --velocity_train_path path_to_velocity_data

2D Long-term Prediction model for velocity can be trained with

python VelocityPrediction2D.py -m train -r 64 --velocity_train_path path_to_velocity_data

3D Long-term Prediction model for density can be trained with

python DensityPrediction3D.py -m train -r 64 --density_train_path path_to_density_data --velocity_train_path path_to_velocity_data

3D Long-term Prediction model for velocity can be trained with

python VelocityPrediction3D.py -m train -r 64 --velocity_train_path path_to_velocity_data

Model Test

2D Long-term Prediction model for density can be tested with

python DensityPrediction2D.py -m test -r 64 --density_test_path path_to_density_data --velocity_test_path path_to_velocity_data --model_path path_to_model --model_name model_name

2D Long-term Prediction model for velocity can be tested with

python VelocityPrediction2D.py -m test -r 64 --velocity_test_path path_to_velocity_data --model_path path_to_model --model_name model_name

3D Long-term Prediction model for density can be tested with

python DensityPrediction3D.py -m test -r 64 --density_test_path path_to_density_data --velocity_test_path path_to_velocity_data --model_path path_to_model --model_name model_name

3D Long-term Prediction model for velocity can be tested with

python VelocityPrediction3D.py -m test -r 64 --velocity_test_path path_to_velocity_data --model_path path_to_model --model_name model_name

File path:

• Datasets: <gitdir>/datasets/
• Source Code Path: <gitdir>/src/
• Density Model Path: <gitdir>/density_model/
• Velocity Model Path: <gitdir>/velocity_model/
• Prediction Path： <gitdir>/predictions/