A mechanism model and data-driven fusion approach for rapid consequence prediction of explosion accidents in chemical clusters

Explosions are considered as a major hazard for chemical cluster safety because they can cause catastrophic disasters. The rapid explosion consequence prediction is critical for emergency response to prevent such disasters. Traditional modeling methods, such as experiment and numerical simulation, provide insights into the explosion mechanism but require high time overhead, thus challenging for real-time predictions. This paper proposes a mechanical model and data-driven fusion method for promptly predicting consequences of potential explosions in the specific chemical cluster. It exploits machine learning (ML) for explosion modeling of specific scenes. Numerical model validated by experiment is employed to provide high-quality benchmark data. Meanwhile, a zoning modeling and progressive-learning strategy is designed for enhancing data-driven model's accuracy and efficiency. As a result, potential consequence of any explosion occurring in this specific scene can be quickly predicted by using the developed data-driven model. The proposed method is applied to vapor cloud explosion (VCE). Two data-driven models were developed to predict peak overpressure and time-varying overpressure, respectively. The results from numerical data show that our model exhibits high efficiency and strong generalization. Finally, a case study is presented to showcase the utilization of the proposed method. This research provides a reliable tool for achieving risk warning and effective response of explosions in chemical clusters.