期刊文献+

A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning 被引量:1

下载PDF
导出
摘要 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-ofthe-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.
出处 《Energy and AI》 EI 2024年第1期300-311,共12页 能源与人工智能(英文)
基金 support from the National Natural Science Foundation of China(Grant No.52250710681 and 52022091) Support from the UK Engineering and Physical Sciences Research Council under the project“UK Consortium on Mesoscale Engineering Sciences(UKCOMES)”(Grant No.EP/X035875/1)is also acknowledged.
  • 相关文献

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部