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

查看全文 作　　者：Kaimeng [1]Li;Pourya [1,2]Rahnama;Ricardo [2]Novella;Bart [1]Somers

机构地区：[1]Power&Flow–Department of Mechanical Engineering.Technical University of Eindhoven.P.O.Box 513,5600 MB Eindhoven,The Netherlands;[2]CMT–Motores T´ermicos.Universidad Polit´ecnica de Valencia.Camino de Vera s/n,E-46022 Valencia,Spain

出　　处：《Energy and AI》索引2023年第14卷第4期,共16页

基　　金：This work was funded by the Netherlands Organisation for Scientific Research(NWO,project number 14927). 摘　　要：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. 关键词：Flamelet models Tabulated chemistry models Computational fluid dynamics Machine learning Non-premixed diffusion flame

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

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