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，本文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，并由此分别建立了一个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（Root Mean Square Error，RMSE）和决定系数（R2），并将预测结果与CFD的计算结果进行对比。以上结果均表明，本文提出的人工智能模型针对自由场爆炸和密闭空间爆炸工况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。基于GNN的爆炸压力时空分布预测模型在爆炸荷载的快速预测上具有强大的潜力，对爆炸防护工程、弹药工程和爆破工程具有参考意义。
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 validity of 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 (RMSE) 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. In light of the GNN-based model for rapidly predicting the overpressure field, it has the potential to be implemented in protective engineering, ammunition engineering, and blasting technology.
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