Merge pull request #251 from JX278/master

update DeepCombustion to Intelligent Combustion

Author: JX278

.github/workflows/CPU_inferencce_validation.yml

@@ -50,8 +50,8 @@ jobs:
         && ls $PWD
         && ls flareFGM_Table_Download
         && cp -r flareFGM_Table_Download/SandiaD/flare.tbl examples/dfLowMachFoam/2DSandiaD_flareFGM/
-        && git clone https://github.com/deepcombustion/deepcombustion.git 
-        && cp -r deepcombustion/DeePCK/Model/HE04_Hydrogen_ESH2_GMS_sub_20221101/ mechanisms/ && source ~/miniconda3/etc/profile.d/conda.sh && conda activate libcantera && source /opt/openfoam7/etc/bashrc 
+        && git clone https://github.com/intelligent-algorithm-team/intelligent-combustion.git 
+        && cp -r intelligent-combustion/DeePCK/Model/HE04_Hydrogen_ESH2_GMS_sub_20221101/ mechanisms/ && source ~/miniconda3/etc/profile.d/conda.sh && conda activate libcantera && source /opt/openfoam7/etc/bashrc 
         && . configure.sh --use_pytorch && source ./bashrc && . install.sh 
         && cd test && ./Allrun && conda deactivate "
 

README.md

@@ -18,7 +18,7 @@
 
 DeepFlame is a deep learning empowered computational fluid dynamics package for single or multiphase, laminar or turbulent, reacting flows at all speeds. It aims to provide an open-source platform to combine the individual strengths of [OpenFOAM](https://openfoam.org), [Cantera](https://cantera.org), and [PyTorch](https://pytorch.org/) libraries for deep learning assisted reacting flow simulations. It also has the scope to leverage the next-generation heterogenous supercomputing and AI acceleration infrastructures such as GPU and FPGA.
 
-The deep learning algorithms and models used in the DeepFlame tutorial examples are developed and trained independently by our collaborators team – [DeepCombustion](https://github.com/deepcombustion/deepcombustion). Please refer to their website for detailed information.
+The deep learning algorithms and models used in the DeepFlame tutorial examples are developed and trained independently by our collaborators team – [Intelligent Combustion](https://github.com/intelligent-algorithm-team/intelligent-combustion.git). Please refer to their website for detailed information.
 
 ## Documentation
 Detailed guide for installation and tutorials is available on [our documentation website](https://deepflame.deepmodeling.com).

docs/source/index.rst

@@ -11,7 +11,7 @@ Welcome to DeepFlame's Documentation
 
 DeepFlame is a deep learning empowered computational fluid dynamics package for single or multiphase, laminar or turbulent, reacting flows at all speeds. It aims to provide an open-source platform to combine the individual strengths of `OpenFOAM <https://openfoam.org/>`_, `Cantera <https://cantera.org/>`_, and `PyTorch <https://pytorch.org/libraries>`_ libraries for deep learning assisted reacting flow simulations. It also has the scope to incorporate next-generation heterogenous supercomputing and AI acceleration infrastructures such as GPU and FPGA.
 
-The deep learning algorithms and models used in the DeepFlame tutorial examples are developed and trained independently by our collaborator team – `DeepCombustion <https://github.com/deepcombustion/deepcombustion/>`_. Please refer to their website for detailed information.
+The deep learning algorithms and models used in the DeepFlame tutorial examples are developed and trained independently by our collaborator team – `Intelligent Combustion <https://github.com/intelligent-algorithm-team/intelligent-combustion.git/>`_. Please refer to their website for detailed information.
 
 .. Note:: This project is under active development.  
 

docs/source/qs/download_dnn_models.rst

@@ -1,18 +1,18 @@
 Download DNN Models
 ======================================
 The neural network models used in the tutorial examples are indepentently trained
-by our collaborators team – `DeepCombustion <https://github.com/deepcombustion/deepcombustion>`_. 
+by our collaborators team – `Intelligent Combustion <https://github.com/intelligent-algorithm-team/intelligent-combustion.git>`_. 
 To run DeepFlame with DNN, first download the DeepCombustion repository into ``deepflame-dev/``: 
 
 .. code-block:: bash
 
     cd $DF_ROOT
-    git clone https://github.com/deepcombustion/deepcombustion.git 
+    git clone https://github.com/intelligent-algorithm-team/intelligent-combustion.git.git 
     
 Then copy the required DNN model into ``mechanisms/``, for example:
 
 .. code-block:: bash
 
-    cp -r deepcombustion/DeePCK/Model/HE04_Hydrogen_ESH2_GMS_sub_20221101/ mechanisms/
+    cp -r intelligent-combustion/DeePCK/Model/HE04_Hydrogen_ESH2_GMS_sub_20221101/ mechanisms/
     
 .. Note:: Here ``HE04_Hydrogen_ESH2_GMS_sub_20221101`` is the default DNN model for all the tutorial cases in ``$DF_ROOT/examples/``.