دورية أكاديمية
A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning
| العنوان: | A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning |
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| المؤلفون: | Sipei Wu, Haiou Wang, Kai Hong Luo |
| المصدر: | Energy and AI, Vol 15, Iss , Pp 100333- (2024) |
| بيانات النشر: | Elsevier, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Electrical engineering. Electronics. Nuclear engineering LCC:Computer software |
| مصطلحات موضوعية: | Turbulent combustion, Detailed reaction mechanism, Transient simulation, Deep neural network, Spatiotemporal series prediction, Long-term forecast stability, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2666-5468 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2666546823001052; https://doaj.org/toc/2666-5468 |
| DOI: | 10.1016/j.egyai.2023.100333 |
| URL الوصول: | https://doaj.org/article/172162821cec43f1afe6fdc62679aafb |
| رقم الأكسشن: | edsdoj.172162821cec43f1afe6fdc62679aafb |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26665468 |
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| DOI: | 10.1016/j.egyai.2023.100333 |