A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning  

作　　者：Sipei Wua Haiou Wang Kai Hong Luo 

机构地区：[1]Center for Combustion Energy,Key Laboratory for Thermal Science and Power Engineering of Ministry of Education,Department of Energy and Power Engineering,Tsinghua University,Beijing 100084,China [2]State Key Laboratory of Clean Energy Utilization,Zhejiang University,Hangzhou 310027,China [3]Department of Mechanical Engineering,University College London,Torrington Place,London WC1E 7JE,United Kingdom

出　　处：《Energy and AI》2024年第1期300-311,共12页能源与人工智能（英文）

基　　金：support from the National Natural Science Foundation of China(Grant No.52250710681 and 52022091);Support from the UK Engineering and Physical Sciences Research Council under the project“UK Consortium on Mesoscale Engineering Sciences(UKCOMES)”(Grant No.EP/X035875/1)is also acknowledged.

摘　　要：This paper systematically develops a high-fidelity turbulent combustion surrogate model using deep learning.We construct a surrogate model to simulate the turbulent combustion process in real time,based on a state-ofthe-art spatiotemporal forecasting neural network.To address the issue of shifted distribution in autoregressive long-term prediction,two training techniques are proposed:unrolled training and injecting noise training.These techniques significantly improve the stability and robustness of the model.Two datasets of turbulent combustion in a combustor with cavity and a vitiated co-flow burner(Cabra burner)have been generated for model validation.The effects of model architecture,unrolled time,noise amplitude,and training dataset size on the long-term predictive performance are explored.The well-trained model can be applicable to new cases by extrapolation and give spatially and temporally consistent results in long-term predictions for turbulent reacting flows that are highly unsteady.

关 键 词：Turbulent combustion Detailed reaction mechanism Transient simulation Deep neural network Spatiotemporal series prediction Long-term forecast stability 

分 类 号：TK16[动力工程及工程热物理—热能工程] TP39[自动化与计算机技术—计算机应用技术]