A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning

Advanced search
Browse by:

Department | Year

UCL Theses | Latest

Deposit your research

A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning

Wu, Sipei; Wang, Haiou; Luo, Kai Hong; (2024) A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning. Energy and AI , 15 , Article 100333. 10.1016/j.egyai.2023.100333. Green open access

[thumbnail of 1-s2.0-S2666546823001052-main.pdf]

Preview

Text
1-s2.0-S2666546823001052-main.pdf - Published Version
Download (3MB) | Preview

Abstract

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.

Type:	Article
Title:	A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1016/j.egyai.2023.100333
Publisher version:	http://dx.doi.org/10.1016/j.egyai.2023.100333
Language:	English
Additional information:	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/).
Keywords:	Turbulent combustion; Detailed reaction mechanism; Transient simulation; Deep neural network; Spatiotemporal series prediction; Long-term forecast stability
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI:	https://discovery.ucl.ac.uk/id/eprint/10185732