A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics. (arXiv:2201.03549v1 [physics.chem-ph])

Machine learning has long been considered as a black box for predicting
combustion chemical kinetics due to the extremely large number of parameters
and the lack of evaluation standards and reproducibility. The current work aims
to understand two basic questions regarding the deep neural network (DNN)
method: what data the DNN needs and how general the DNN method can be. Sampling
and preprocessing determine the DNN training dataset, further affect DNN
prediction ability. The current work proposes using Box-Cox transformation
(BCT) to preprocess the combustion data. In addition, this work compares
different sampling methods with or without preprocessing, including the Monte
Carlo method, manifold sampling, generative neural network method (cycle-GAN),
and newly-proposed multi-scale sampling. Our results reveal that the DNN
trained by the manifold data can capture the chemical kinetics in limited
configurations but cannot remain robust toward perturbation, which is
inevitable for the DNN coupled with the flow field. The Monte Carlo and
cycle-GAN samplings can cover a wider phase space but fail to capture
small-scale intermediate species, producing poor prediction results. A
three-hidden-layer DNN, based on the multi-scale method without specific flame
simulation data, allows predicting chemical kinetics in various scenarios and
being stable during the temporal evolutions. This single DNN is readily
implemented with several CFD codes and validated in various combustors,
including (1). zero-dimensional autoignition, (2). one-dimensional freely
propagating flame, (3). two-dimensional jet flame with triple-flame structure,
and (4). three-dimensional turbulent lifted flames. The results demonstrate the
satisfying accuracy and generalization ability of the pre-trained DNN. The
Fortran and Python versions of DNN and example code are attached in the
supplementary for reproducibility.

Source: https://arxiv.org/abs/2201.03549


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