@article{discovery10185732,
       publisher = {Elsevier BV},
            note = {Copyright {\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/).},
          volume = {15},
           month = {January},
            year = {2024},
           title = {A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning},
         journal = {Energy and AI},
        keywords = {Turbulent combustion; Detailed reaction mechanism; 
Transient simulation; Deep neural network; 
Spatiotemporal series prediction; 
Long-term forecast stability},
             url = {http://dx.doi.org/10.1016/j.egyai.2023.100333},
          author = {Wu, Sipei and Wang, Haiou and Luo, Kai Hong},
        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.},
            issn = {2666-5468}
}