Modeling Combustion Reaction ODEs with Neural Networks

Steven Kiyabu, Nicholas Wimer

Research output: NRELPresentation


The chemistry of combustion reactions is complex as it involves many species and reactions. In practice, such a system is often modeled computationally using an empirically derived chemical mechanism. Given the large range of reaction rates, the system is then time evolved using a stiff ODE solver. However, even when reduced chemical mechanisms are employed, the system can become computationally expensive for two-dimensional or three-dimensional systems. Such systems can also face problems with instability. As such, it is desirable to find a cheaper, stable alternative to solving the reaction system. Neural networks offer the potential to learn these reaction ODEs and time evolve a combustion reaction in a more cost-efficient manner than stiff ODE solvers. In this study, a variety of neural networks are trained on zero-dimensional Cantera simulations of methane combustion with varying initial conditions. Several predictive approaches as well as several neural network architectures (artificial neural network with dropout, ResNet, and Neural ODE) are compared in their ability to predict combustion trajectories. Promising models are then identified.
Original languageAmerican English
Number of pages14
StatePublished - 2021

Publication series

NamePresented at the Rocky Mountain Fluid Mechanics Research Symposium, 10 August 2021, Boulder, Colorado

NREL Publication Number

  • NREL/PR-2C00-80668


  • combustion kinetics
  • machine learning
  • neural networks


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