Application of multi-task learning method in replacing flamelet database with neural networks
To achieve high-precision substitution of flamelet model databases using neural networks,residual neural networks trained by multi-task learning(MTL)method were taken as the subject.Integrated with the fmFoam solver and targeting the Sandia D diffusion flame,the precision of the trained neural network model was validated.The results indicate that the MTL approach significantly improves the prediction accuracy of the neural network for flamelet database.Compared to residual neural networks alone,the MTL-trained neural network increases the Pearson correlation coefficient of predicted physical quantities from 0.9990 to 0.9999,and reduces the average relative error for the top 10 components by mass fraction at least 81.1% .In numerical simulations of the Sandia D flame using OpenFOAM,the results of MTL-trained neural network along the centerline and various radial positions matches those of traditional methods,with only minor differences in the reaction progress variable source term.Using traditional lookup methods as a baseline,the maximum relative error for the peak temperature and main combustion products along the centerline calculated by the FGM-MTL method is 0.98%,with the maximum relative error in the peak position being 2.37% .
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