爆炸与冲击2026，Vol.46Issue(4)：99-114,16.DOI:10.11883/bzycj-2024-0503
基于GNN的爆炸压力时空分布预测模型
GNN-based predictive model for spatial and temporal distribution of blast overpressure
摘要
Abstract
To meet the need for accurate and rapid prediction of overpressure generated by an explosion,a graph neural network(GNN)-based artificial intelligence model was proposed in this paper for predicting the spatial and temporal distribution of the blast overpressure.The model relies on high-fidelity training data generated through computational fluid dynamics(CFD)simulations using the open-source software blastFoam,and the numerical simulations was validated against experimental data from existing literature.In the simulations,the computational domain was discretized using hexahedral meshes,and key physical parameters—including pressure,velocity,and node type—were extracted and converted into structured graph data via mesh remapping technology.This approach enabled the construction of two specialized datasets:a free-field explosion dataset and a confined explosion dataset for TNT,which serve as the foundation for training and evaluating the GNN model.The GNN model contains three modules:an encoder,a processor and a decoder.The predicted results of the pressure field can be output through inputting the standard graph format data.The GNN model was trained using the two training datasets for the two specialized scenarios,separately.The root mean square error(σ)and the coefficient of determination(R2)of the model on the testing datasets were monitored,and the predicted results were compared with the computed results of the CFD.All the above comparisons show that the GNN-based model proposed in this paper attains good predicted results in both the free-field explosion and the confined explosion scenarios.The GNN-based model has the advantages in extracting strong feature under small samples,rapid prediction with stratified accuracy,and versatile applications.Moreover,the GNN-based model can achieve the prediction of the blast overpressure field of the three-dimensional space both in temporal and spatial dimensions.关键词
自由场爆炸/内爆炸/爆炸压力/时空分布/机器学习/图神经网络Key words
free-field explosion/internal explosion/blast overpressure/spatial and temporal distribution/machine learning/graph neural network分类
数理科学引用本文复制引用
李般若,霍璞,喻君..基于GNN的爆炸压力时空分布预测模型[J].爆炸与冲击,2026,46(4):99-114,16.基金项目
国家自然科学基金(52378490) （52378490）
