Page 102 - 《爆炸与冲击》2026年第4期

第 46 卷 第 4 期 爆 炸 与 冲 击 Vol. 46, No. 4
2026 年 4 月 EXPLOSION AND SHOCK WAVES Apr., 2026

DOI：10.11883/bzycj-2024-0503

基于 GNN 的爆炸压力时空分布预测模型 *

李般若，霍 璞，喻 君

（东南大学土木工程学院，江苏 南京 211189）

摘要： 为了满足对爆炸产生的压力荷载进行准确快速预测的需求，提出了一项基于图神经网络（graph neural
network, GNN）的爆炸压力时空分布预测人工智能模型：利用开源软件 blastFoam 进行计算流体动力学（computational
fluid dynamics, CFD）仿真，并通过网格重映射技术，以空间六面体网格划分为基础，将物理状态信息编写到节点特征
中，以此将计算结果转化为标准的图格式数据，并由此建立了一个 TNT 自由场爆炸数据集和一个 TNT 密闭空间内爆
炸数据集；将 GNN 模型分别在两个数据集的训练集上进行训练，监测模型在测试集上的均方根误差（σ）和决定系数
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（R ），并将预测结果与 CFD 的计算结果进行对比。结果表明，针对自由场爆炸和密闭空间爆炸工况，本文提出的人工
智能模型均得到了良好的预测效果。该人工智能模型具有在小样本上提取特征能力强、预测速度快、预测效果好、应
用场景多样的优势，并且能够实现在三维空间内对爆炸压力场进行时间和空间维度的预测。
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
中图分类号： O382 国标学科代码： 130.3520 文献标志码： A

GNN-based predictive model for spatial and temporal
distribution of blast overpressure

LI Banruo, HUO Pu, YU Jun
（School of Civil Engineering, Southeast University, Nanjing 211189, Jiangsu, China）

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
2
determination (R ) 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

* 收稿日期： 2024-12-25；修回日期： 2025-05-28
基金项目： 国家自然科学基金（52378490）
第一作者： 李般若（2001－ ），男，硕士生，banruoli@seu.edu.cn
通信作者： 喻 君（1982－ ），男，博士，教授，junyu@seu.edu.cn

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