Journal article

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

Publication Details

Author list: Laubscher Ryno, Rousseau Pieter

Publisher: Elsevier

Publication year: 2021

Journal: Applied Soft Computing

Issue number: 107074

ISSN: 1568-4946



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.


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Machine Learning

Last updated on 2021-03-01 at 10:51