Predicting combustion behavior in rotating detonation engines using an interpretable deep learning method

• Published in: Physics of fluids (1994) 2023-07, Vol.35 (7)
• Main Authors: Shen Dawen, Sheng Zhaohua, Zhang Yunzhen, Rong Guangyao, Wu, Kevin, Wang, Jianping
• Format: Article
• Language: English
• Subjects: Active control; Coders; Combustion stability; Decision making; Deep learning; Detonation; Fluid dynamics; Monitoring; Multivariate analysis; Neural networks; Physics; Rotation; Teaching methods; Transformers
• ISSN: 1070-6631; 1089-7666
• DOI: 10.1063/5.0155991

As rotating detonation engine (RDE) is maturing toward engineering implementation, it is a crucial step in developing real-time diagnostics capable of monitoring the combustion state therein to prevent combustion instability, such as detonation quenching, re-initiation, and mode switch. However, previous studies rarely consider monitoring combustion behavior in RDEs, let alone predicting the impending combustion instabilities based on the warning signals. Given active control requirements, a novel Transformer-based neural network, RDE-Transformer, is proposed for monitoring and predicting the combustion states in advance. RDE-Transformer is a multi-horizon forecasting model fed by univariate or multivariate time series data including pressure signals and aft-end photographs. Model hyper-parameters, namely, the number of encoder and decoder layers, the number of attention heads, implementation of positional encoding, and prediction length, are investigated for performance improvements. The results show that the optimal architecture can reliably predict pressures up to 5 detonation periods ahead of the current time, with a mean squared error of 0.0057 and 0.0231 for the training and validation set, respectively. Moreover, the feasibility of predicting combustion instability is validated, and the decision-making process through the attention mechanism is visualized by attention maps, making the model interpretable and superior to other “black-box” deep learning methods. In summary, the high performance and high interpretability of RDE-Transformer make it a promising diagnostics functional component for RDEs toward applied technology.