Study on the application of artificial neural network-based flamelet/progress variable model in supersonic combustion

• Published in: AIP advances 2023-11, Vol.13 (11), p.115319-115319-15
• Main Authors: Lian, Chengyue, Tang, Tao, Wang, Hongbo, Yu, Jiangfei, Sun, Mingbo, Xiong, Dapeng, Yang, Yixin
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Central processing units; CPUs; Data processing; Graphics processing units; Heat release rate; Mathematical models; Neural networks; Parameters; Performance evaluation; Supersonic combustion; Training evaluation; Turbulent combustion
• ISSN: 2158-3226; 2158-3226
• DOI: 10.1063/5.0171442

The flamelet model has the characteristics of high efficiency and physical intuition and has excellent application prospects in supersonic turbulent combustion simulation. Expanding the dimensions of the flamelet model is a potential direction for model development in order to improve its applicability and accuracy, but the accompanying surge in memory is a problem that must be avoided. Therefore, the idea of using the artificial neural network (ANN) model to replace the flamelet database is a feasible exploration currently and has been preliminarily applied in 2D flamelet databases based on central processing unit frameworks. Based on the 3D flamelet database of the flamelet/progress variable (FPV) model, this article studies the strategy of using ANN to replace the flamelet database of the FPV model in a graphics processing unit framework. Due to the significant influence of the progress variable source term and heat release rate on the combustion calculation and the large range of these two parameters, four data processing methods are used to train the parameters separately, and three indicators are used to evaluate the training performance. Subsequently, based on the ANN model using different data processing methods mentioned earlier, calculations are conducted on a hydrogen-fueled supersonic combustion, and the computational accuracy is evaluated. The results indicate that the strategy proposed in this study can screen out artificial neural network replacement models with the same accuracy as the traditional flamelet model.