Combining flamelet-generated manifold and machine learning models in simulation of a non-premixed diffusion flame

Abstract

Flamelet Generated Manifold (FGM) is an example of a chemistry tabulation or a flamelet method that is under attention because of its accuracy and speed in predicting combustion characteristics. However, the main problem in applying the model is a large amount of memory required. One way to solve this problem is to apply machine learning (ML) to replace the stored tabulated data. Four different machine learning methods, including two Artificial Neural Networks (ANNs), a Random Forest (RF), and a Gradient Boosted Trees (GBT), are trained, validated, and compared in terms of various performance measures. The progress variable source term and transport properties are replaced with the ML models. Particular attention was paid to the progress variable source term due to its high gradient and wide range of its value in the control variables space. Data preprocessing is shown to play an essential role in improving the performance of the models. Two ensemble models, namely RF and GBT, exhibit high training efficiency and acceptable accuracy. On the other hand, the ANN models have lower training errors and take longer to train. The four models are then combined with a one-dimensional combustion code to simulate a counterflow non-premixed diffusion flame in engine-relevant conditions. The predictions of the ML-FGM models are compared with detailed chemical simulations and the original FGM model for key combustion properties and representative species profiles.

Keywords

• Flamelet models
• Tabulated chemistry models
• Computational fluid dynamics
• Machine learning
• Non-premixed diffusion flame