Application of machine learning in low-order manifold representation of chemistry in turbulent flames

Abstract

The Uniform Conditional State (UCS) and the Multidimensional Flamelet Manifold (MFM) models are methods for the tabulation of chemistry in simulations of turbulent flames. The high-dimensionality of the tables these models generate and many possible combinations of the values for the input variables necessitate the allocation of a considerable size of memory during CFD calculations. This issue becomes even more problematic when adding more conditioning variables to the model. In this study, two Artificial Intelligence (AI)-based approaches referred to as Decision Tree (DT) and Artificial Neural Network (ANN) are developed and tested to provide in situ chemistry representation. The goal is to predict four parameters (outputs) accurately with low memory demand and computational cost. The trained AI models are then employed for simulation of a turbulent premixed flame. Comparison of the results from the AI-based approaches to those from the conventional UCS model shows acceptable agreement. The memory and CPU requirements from the different approaches are compared. It is found that the ANN model reduces the size of the chemistry table by around 92%. Conversely, the DT-based model reduces the size of the chemistry model by only 40%. The CPU time for using the DT model during the CFD calculations was around 10% shorter than the conventional approach while it was 8% higher for the ANN model. It was concluded that, based on the particular applications, different AI-based methods can facilitate an efficient representation of the chemistry manifold.