Optimizing Flamelet Generated Manifold Models: A Machine Learning Performance Study

TL;DR

This study compares machine learning algorithms to efficiently generate Flamelet Generated Manifold libraries for methane combustion, achieving high accuracy and reducing memory requirements in combustion modeling.

Contribution

It introduces a machine learning-based approach to create FGM libraries, optimizing accuracy and computational efficiency for methane combustion simulations.

Findings

ML algorithms can accurately regenerate FGM libraries with errors around 2.3%.

MLP with hyperparameter tuning achieved 99.81% accuracy.

Optimal MLP architecture includes four hidden layers with 10-25 neurons.

Abstract

In chemistry tabulations and Flamelet combustion models, the Flamelet Generated Manifold (FGM) is recognized for its precision and physical representation. The practical implementation of FGM requires a significant allocation of memory resources. FGM libraries are developed specifically for a specific fuel and subsequently utilized for all numerical problems using machine learning techniques. This research aims to develop libraries of Laminar FGM utilizing machine learning algorithms for application in combustion simulations of methane fuel. This study employs four Machine Learning algorithms to regenerate Flamelet libraries, based on an understanding of data sources, techniques, and data-driven concepts. 1. Multi-Layer Perceptron; 2. Random Forest; 3. Linear Regression; 4. Support Vector Machine. Seven libraries were identified as appropriate for constructing a database for training machine learning models, giving an error rate of 2.30%. The default architectures of each method were evaluated to determine the optimal approach, leading to the selection of the MLP method as the primary choice. The method was enhanced through hyperparameter tuning to improve accuracy. The quantity of hidden layers and neurons significantly influences method performance. The optimal model, comprising four hidden layers with 10, 15, 20, and 25 neurons respectively, achieved an accuracy of 99.81%.