# Data-assisted combustion simulations with dynamic submodel assignment using random forests

arXiv: https://arxiv.org/abs/2009.04023 · HTML: https://mlsanity.com/paper/2009.04023/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Improving aircraft performance using machine learning: a review](https://mlsanity.com/paper/2210.11481/) (2022)
- [Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion](https://mlsanity.com/paper/2103.06397/) (2021)
- [Local manifold learning and its link to domain-based physics knowledge](https://mlsanity.com/paper/2207.00275/) (2022)
- [Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow](https://mlsanity.com/paper/2210.16248/) (2022)
- [FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flames](https://mlsanity.com/paper/2406.09071/) (2024)
- [Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool](https://mlsanity.com/paper/2202.00596/) (2022)
- [Physics-Guided Machine Learning for Uncertainty Quantification in Turbulence Models](https://mlsanity.com/paper/2511.05633/) (2025)

## Abstract (arXiv)

In this investigation, we outline a data-assisted approach that employs random forest classifiers for local and dynamic combustion submodel assignment in turbulent-combustion simulations. This method is applied in simulations of a single-element GOX/GCH4 rocket combustor; a priori as well as a posteriori assessments are conducted to (i) evaluate the accuracy and adjustability of the classifier for targeting different quantities-of-interest (QoIs), and (ii) assess improvements, resulting from the data-assisted combustion model assignment, in predicting target QoIs during simulation runtime. Results from the a priori study show that random forests, trained with local flow properties as input variables and combustion model errors as training labels, assign three different combustion models - finite-rate chemistry (FRC), flamelet progress variable (FPV) model, and inert mixing (IM) - with reasonable classification performance even when targeting multiple QoIs. Applications in a posteriori studies demonstrate improved predictions from data-assisted simulations, in temperature and CO mass fraction, when compared with monolithic FPV calculations. These results demonstrate that this data-driven framework holds promise for the dynamic combustion submodel assignment in reacting flow simulations.