Artificial Neural Networks for Chemistry Representation in Numerical Simulation of the Flamelet-Based Models for Turbulent Combustion

Abstract

Turbulent combustion is one of the key processes in many energy conversion systems in modern life. In order to improve combustion efficiency and suppress emission of pollutants, many efforts have been made by scholars to investigate turbulent flames. In the present study, Artificial neural network (ANN) was first employed for the storage and interpolation of the flamelet library in flamelet generated manifolds (FGM) model, in which Eulerian stochastic field (ESF) model was used to directly consider the probability density function of the control variables. This new model had been implemented in OpenFOAM and was validated by simulation of the Sandia Flame D under consideration of the detailed chemical reaction mechanism. By comparing the results of numerical simulations and experimental measurements of the temperature and the mass fraction of main components, the accuracy of the proposed ANN-ESFFGM model was verified. Through the use of ANNs to characterize the chemical reactions, the flame simulation accuracy of the new model is higher than that of the original ESFFGM model, especially in the prediction of the ignition position. With the increase in the number of stochastic fields, the simulation accuracy of the new turbulent combustion model is continuously improved until a certain value of stochastic fields was reached. Moreover, excessively high FGM table resolution has limited improvement in numerical simulation accuracy.INDEX TERMS Artificial neural network, Eulerian stochastic field methods, Flamelet generated manifold model, OpenFOAM, turbulent combustion.

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

“…Compared to the aforementioned methods, ML methods can decrease the memory usage much more since only its architecture characteristics and parameters need to be stored. Zhang et al [28] successfully employed an ANN in an FGM modeling of the methane-air mixture burner flame, and the precision of the suggested method was examined by comparing the results of the numerical simulations and experimental measurements.…”

Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion

“…The primary advantage of this approach is that the amount of memory required to store the neural network is greatly reduced relative to the amount required to store the tables in traditional methods; it is nearly constant with manifold dimensionality as opposed to scaling exponentially [26] . With the trend toward higher manifold dimensionality in flamelet-based models to account for more complex physics, as well as the trend toward heterogeneous architectures in scientific computing, the past few years have seen an explosion of interest in using ANNs to replace flamelet tabulation [27][28][29][30][31][32] . A similar approach has been applied to replace the linear reconstruction process for PCA, and has become the prefered approach for PCA-based models [33][34][35] : first, a truncated set of PCs is generated using PCA on a set of thermochemical state data, and subsequently a nonlinear regression (e.g., a neural network) is fit to predict the desired outputs based on these components.…”

Physics-Aware Neural Network Flame Closure for Combustion Instability Modeling in a Single-Injector Engine

“…Thus, the flamelet model provides a proper stepping stone for exploring the use of machine learning in the combustion studies. The NN-based models developed in [9,10,21] successfully replace the flamelet table in their CFD testbeds. In particular, the NN-based flamelet model in [21] has been developed to be used with the Sandia flame D. NNs have been used in [9] for representing a flamelet model in a stably burning flame in Sydney bluff-body swirl-stabilized flame.…”

“…The NN-based models developed in [9,10,21] successfully replace the flamelet table in their CFD testbeds. In particular, the NN-based flamelet model in [21] has been developed to be used with the Sandia flame D. NNs have been used in [9] for representing a flamelet model in a stably burning flame in Sydney bluff-body swirl-stabilized flame. In [10], NN was trained on a pressure-dependent flamelet table and implemented in a single injector rocket configuration with longitudinal instability.…”

“…An ultimate goal of utilizing machine learning in combustion instability studies is to use the data from experiments and detailed computationally expensive CFD simulations to develop lower-cost NN-based descriptive sub-grid models. Among initial steps toward this goal, articles like [9,10,21]…”