1.1. Prerequisites #

The installation of DeepFlame is simple and requires OpenFOAM-7 , LibCantera , and PyTorch .

First, install OpenFOAM-7.

# Install OpenFOAM release by apt
sudo sh -c "wget -O - https://dl.openfoam.org/gpg.key | apt-key add -"
sudo add-apt-repository http://dl.openfoam.org/ubuntu
sudo apt-get update
sudo apt-get -y install openfoam7

OpenFOAM-7 and ParaView-5.6.0 will be installed in the /opt directory.

Alternatively, one can compile OpenFOAM-7 from source code .

gcc --version
sudo apt-get install build-essential cmake git ca-certificates
sudo apt-get install flex libfl-dev bison zlib1g-dev libboost-system-dev libboost-thread-dev libopenmpi-dev openmpi-bin gnuplot libreadline-dev libncurses-dev libxt-dev
cd $HOME # the path OpenFOAM will be installed
wget -O - http://dl.openfoam.org/source/7 | tar xz
wget -O - http://dl.openfoam.org/third-party/7 | tar xz
mv OpenFOAM-7-version-7 OpenFOAM-7
mv ThirdParty-7-version-7 ThirdParty-7
source OpenFOAM-7/etc/bashrc
./OpenFOAM-7/Allwmake -j

LibCantera and PyTorch can be easily installed via conda . If your platform is compatible, run the following command to install the dependencies.

conda create -n deepflame python=3.8
conda activate deepflame
conda install -c cantera libcantera-devel=




    
2.6 cantera
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
conda install pybind11 pkg-config

# For CUDA-supported platforms
conda create -n deepflame \
    pytorch torchvision torchaudio libcantera-devel easydict pybind11 pkg-config \
    -c pytorch -c nvidia -c cantera -c conda-forge
conda activate deepflame

1.2. Configure #

1. Source your OpenFOAM-7 bashrc to configure the $FOAM environment.

If you have installed using apt-get install , then:

source /opt/openfoam7/etc/bashrc

If you compiled from source following the official guide , then:

source $HOME/OpenFOAM/OpenFOAM-7/etc/bashrc

To source the bashrc file automatically when opening your terminal, type

echo "source /opt/openfoam7/etc/bashrc" >> ~/.bashrc

or

echo "source $HOME/OpenFOAM/OpenFOAM-7/etc/bashrc" >> ~/.bashrc

Then source the bashrc file by:

source ~/.bashrc

2. Clone the DeepFlame repository:

git clone https://github.com/deepmodeling/deepflame-dev.git

If you want to use the submodules included in DeepFlame: the WENO scheme and the libROUNDSchemes , run

git clone --recursive https://github.com/deepmodeling/deepflame-dev.git

Detailed instructions for compiling these two submodules can be found in their original repositories.

3. Configure the DeepFlame environment:

cd




    
 deepflame-dev
. configure.sh --use_pytorch
source ./bashrc

1.3. Build and Install #

Finally you can build and install DeepFlame:

. install.sh

conda install fmt
conda install eigen

cd ~/miniconda3/envs/deepflame/lib
ln -s libmkl_rt.so.1 libmkl_rt.so.2

If you have compiled DeepFlame successfully, you should see the print message in your terminal:

1.4. Other Options #

DeepFlame also provides users with full GPU version, CVODE (no DNN version) and PaddlePaddle options.

1. If you just need DeepFlame’s CVODE solver without DNN model, just install LibCantera via conda .

conda create -n df-notorch python=3.8
conda activate df-notorch
conda install -c conda-forge libcantera-devel

If the conda env df-notorch is activated, install DeepFlame by running:

cd deepflame-dev
. configure.sh
source ./bashrc
. install.sh

If df-notorch not activated (or you have a self-compiled libcantera), specify the path to your libcantera:

. configure.sh --libcantera_dir /your/path/to/libcantera/
source ./bashrc
. install.sh

2. If you wish to employ dfMatrix and the AMGX library for accelerating PDE solving using GPU:

To begin, you will need to install AMGX. You can find the instructions for installing AMGX on its official website. Follow the instructions provided to install AMGX on your system. Once you have installed AMGX, navigate to the DeepFlame directory and follow the commands below.

cd deepflame-dev
. configure.sh --amgx_dir /your/path/to/AMGX/ --libtorch_dir /path/to/libtorch/
source ./bashrc
. install.sh

Also, you will need to add configuration files for AMGX for each euqation under system folder and name them in the pattern of amgxpOptions , amgxUOptions . Please refer to the AMGX official website to find out detailed instructions.

If you have compiled DeepFlame with GPU solver successfully, you should see the print message in your terminal:

 = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
|     deepflame (linked with libcantera) compiled successfully! Enjoy!!          |
|        select the GPU solver coupled with AMGx library to solve PDE            |
 = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

3. If you wish to install DeepFlame with CMake

You will need to follow the same procedures to install prerequisites and configure DeepFlame.

cd deepflame-dev
. configure.sh --use_pytorch
source ./bashrc

After this, first install libraries:

cd $DF_ROOT
cmake -B build
cd build