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

LI Banruo; HUO Pu; YU Jun

doi:10.11883/bzycj-2024-0503

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LI Banruo, HUO Pu, YU Jun. GNN-based predictive model for spatial and temporal distribution of blast overpressure[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0503

Citation:

LI Banruo, HUO Pu, YU Jun. GNN-based predictive model for spatial and temporal distribution of blast overpressure[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0503

LI Banruo, HUO Pu, YU Jun. GNN-based predictive model for spatial and temporal distribution of blast overpressure[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0503

Citation:

LI Banruo, HUO Pu, YU Jun. GNN-based predictive model for spatial and temporal distribution of blast overpressure[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0503

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

doi: 10.11883/bzycj-2024-0503

LI Banruo^1
,,
HUO Pu¹,
YU Jun^{1
,
,}

1.
School of Civil Engineering, Southeast University, Nanjing 211189, Jiangsu, China

Received Date: 2024-12-25
Rev Recd Date: 2025-05-28

Available Online: 2025-06-03

Abstract

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.
- free-field explosion,
- internal explosion,
- blast overpressure,
- spatial and temporal distribution,
- machine learning,
- graph neural network

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References(30)

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