Utilizing neural networks to supplant chemical kinetics tabulation through mass conservation and weighting of species depletion  

作　　者：Franz M.Rohrhofer Stefan Posch Clemens Gößnitzer JoséM.García-Oliver Bernhard C.Geiger 

机构地区：[1]Know-Center GmbH,Sandgasse 36,Graz,8010,Styria,Austria [2]LEC GmbH,Inffeldgasse 19,Graz,8010,Styria,Austria [3]Universitat Politècnica de València,Camíde Vera,València,46022,València,Spain

出　　处：《Energy and AI》2024年第2期101-110,共10页能源与人工智能（英文）

摘　　要：Artificial Neural Networks(ANNs)have emerged as a powerful tool in combustion simulations to replace memory-intensive tabulation of integrated chemical kinetics.Complex reaction mechanisms,however,present a challenge for standard ANN approaches as modeling multiple species typically suffers from inaccurate predictions on minor species.This paper presents a novel ANN approach which can be applied on complex reaction mechanisms in tabular data form,and only involves training a single ANN for a complete reaction mechanism.The approach incorporates a network architecture that automatically conserves mass and employs a particular loss weighting based on species depletion.Both modifications are used to improve the overall ANN performance and individual prediction accuracies,especially for minor species mass fractions.To validate its effectiveness,the approach is compared to standard ANNs in terms of performance and ANN complexity.Four distinct reaction mechanisms(H_(2),C_(7)H_(16),C_(12)H_(26),OME_(34))are used as a test cases,and results demonstrate that considerable improvements can be achieved by applying both modifications.

关 键 词：Neural network approach Chemical kinetics Flamelet tabulation Mass conservation Species loss weighting 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]