TY  - JOUR
KW  - Turbulent combustion; Detailed reaction mechanism; 
Transient simulation; Deep neural network; 
Spatiotemporal series prediction; 
Long-term forecast stability
TI  - A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning
UR  - http://dx.doi.org/10.1016/j.egyai.2023.100333
PB  - Elsevier BV
VL  - 15
A1  - Wu, Sipei
A1  - Wang, Haiou
A1  - Luo, Kai Hong
Y1  - 2024/01//
N2  - This paper systematically develops a high-fidelity turbulent combustion surrogate model using deep learning. We construct a surrogate model to simulate the turbulent combustion process in real time, based on a state-of-the-art spatiotemporal forecasting neural network. To address the issue of shifted distribution in autoregressive long-term prediction, two training techniques are proposed: unrolled training and injecting noise training. These techniques significantly improve the stability and robustness of the model. Two datasets of turbulent combustion in a combustor with cavity and a vitiated co-flow burner (Cabra burner) have been generated for model validation. The effects of model architecture, unrolled time, noise amplitude, and training dataset size on the long-term predictive performance are explored. The well-trained model can be applicable to new cases by extrapolation and give spatially and temporally consistent results in long-term predictions for turbulent reacting flows that are highly unsteady.
AV  - public
JF  - Energy and AI
N1  - Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
SN  - 2666-5468
ID  - discovery10185732
ER  -