Qiuju Ma , Zhennan Chen , Jianhua Chen , Yubo Sun , Nan Chen , Mengzhen Du
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引用次数: 0
Abstract
Accurate heat flux prediction in cabinet fires is essential for evaluating thermal risks. However, traditional methods become constrained due to flame obstruction, unavailable key parameters on-site, and limited future prediction capability. This study proposes a hybrid framework combining extreme gradient boosting (XGBoost), a physics-based model, and bidirectional long short-term memory (BiLSTM) for heat flux inference and forecasting. XGBoost is employed to infer target temperatures from cabinet wall thermocouples, which are then converted into heat flux through a physics-based model, establishing a real-time inference pathway from accessible measurements to heat flux. Then, BiLSTM is introduced to forecast heat flux 60 s ahead, thereby capturing the future evolution of the target variable. Results demonstrate that the predicted heat flux achieves errors below 0.17 kW/m2 in training, validation, and test sets. When using predicted heat flux as input, BiLSTM maintains errors below 5.9 %. Compared with other models, our methods achieve lower errors. In addition, a feature selection strategy based on correlation analysis and hierarchical clustering is developed to identify the optimal thermocouple combination when constructing the dataset. The proposed method provides a practical and efficient insight for predicting heat flux in cabinet fires, contributing to enhanced fire risk management in nuclear industry.
期刊介绍:
Applied Thermal Engineering disseminates novel research related to the design, development and demonstration of components, devices, equipment, technologies and systems involving thermal processes for the production, storage, utilization and conservation of energy, with a focus on engineering application.
The journal publishes high-quality and high-impact Original Research Articles, Review Articles, Short Communications and Letters to the Editor on cutting-edge innovations in research, and recent advances or issues of interest to the thermal engineering community.