Arrhenius

We are in an early-development. Expect some adventures and rough edges.

Installation

pkg> add https://github.com/DENG-MIT/Arrhenius.jl

Publication

• Arrhenius.jl: A Differentiable Combustion Simulation Package: overview of Arrhenius.jl and applications in deep mechanism reduction, uncertainty quantification, mechanism tuning and model discovery. Slides in NCM21, Vedio for NCM21.
• Machine Learning Approaches to Learn HyChem Models: demonstrate 1000 times faster than genetic algorithms using commercial software for optimizing complex kinetic models.
• Neural Differential Equations for Inverse Modeling in Model Combustors
• SGD-based Optimization in Modeling Combustion Kinetics: Case Studies in Tuning Mechanistic and Hybrid Kinetic Models

Applications

• Sensitivity analysis for auto-ignition | repo | Features: auto-differentiation, multi-threading, sensitivity to all of three Arrhenius params A, b and Ea, active subspace based uncertainty quantification
• Sensitivity analysis for one-dimensional flames | repo | Features: auto-differentiation, multi-threading, sensitivity to all of three Arrhenius params A, b and Ea.
• Automonous learn kinetic mechanism using neural network | repo | Features: Chemical Reaction Neural Network (CRNN), Neural Ordinary Differential Equations.
• Deep Reduction | repo | Features: Two-stages mechanism reduction with deep learning.

Examples

Note that some of the examples are in development and you can have early access by contacting Weiqi Ji

• Pyrolysis of JP10
• Perfect Stirred Reactor
• Auto-ignition
• Compute Jacobian using AD

Relevent packages

• ReactionMechanismSimulator.jl The amazing Reaction Mechanism Simulator for simulating large chemical kinetic mechanisms
• Cantera A comprehensive C++ based combustion simulation package and with great python interface. Arrhenius relies on Cantera when it is applicable.