An integrated approach to predict scalar fields of a simulated turbulent jet diffusion flame using multiple fully connected variational autoencoders and MLP networks

A novel integrated deep learning approach for data-driven surrogate modelling of combustion computational fluid dynamics (CFD) simulations is presented. It combines variational autoencoders (VAEs) with deep neural networks (DNNs) to predict detail cell-by-cell two-dimensional distributions of temperature, velocity and species mass fractions from high level inputs such as velocity and fuel and air mass fractions. The VAE model is used to generate low dimensional encodings of the CFD data and the DNN is used in turn to map boundary conditions to the encodings. The results show that regularization is required during all training phases. Sufficiently accurate results were achieved for the reproduced species mass fractions with mean average errors below 0.3 [%wt.]. The validation mean average percentage errors for the temperature and velocity fields are 1.7% and 7.1% respectively. It is therefore possible to predict detail two-dimensional contours of CFD solution data with adequate generalizability and accuracy. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A novel integrated deep learning approach for data-driven surrogate modelling of combustion CFD simulations is proposed, combining VAEs with DNNs to predict detailed cell-by-cell two-dimensional distributions of temperature, velocity, and species mass fractions from high level inputs. Regularization is found to be necessary during all training phases, and sufficiently accurate results were achieved with mean average errors below 0.3% for species mass fractions. Validation mean average percentage errors for temperature and velocity fields are 1.7% and 7.1% respectively, indicating the ability to predict detailed two-dimensional contours of CFD solution data with adequate generalizability and accuracy.