Arrhenius.jl is designed with following in mind:
- Combustion software 2.0
- Differential programing
- Physics informed machine learning
- Combustion simulation education.
We are in an early-development. Expect some adventures and rough edges.
- Machine Learning Approaches to Learn HyChem Models: demonstrate 1000 times faster than genetic algorithms using commercial software for optimizing complex kinetic models.
- Arrhenius.jl: A Differentiable Combustion Simulation Package: overview of Arrhenius.jl and applications in deep mechanism reduction, uncertainty quantification, mechanism tuning and model discovery.
- Neural Differential Equations for Inverse Modeling in Model Combustors
You can start from the example of pyrolysis of JP10 (an aviation fuel power the flight) under the folder of
example. It will guide you on how to implement the governing equations with a couple of lines of code. You will also learn how to use
ForwardDiff.jl to differentiate the solver.
Currently, the package relies on
ReacTorchfor interpreting the reaction mechanism. If you want to have a try, you don't need to install Cantera and ReacTorch, since there are already some pre-compiled reaction mechanisms under the folder of
mechanism. Otherwise, you can install
ReacTorchto compile it using the python script
interpreter.pyunder the folder of
mechanism. You can also ask for help in the discussion forum and our developers can compile the model for you.
Note that some of the examples are in development and you can have early access by contacting Weiqi Ji
- Active Subspace of Reaction Mechanism
- Pyrolysis of JP10
- Perfect Stirred Reactor
- Compute Jacobian using AD
- Couple with CRNN and Neural ODEs
- Deep Reduction: Two-stages mechanism reduction with deep learning