Efficient modeling of jet diffusion during accidental release is critical for operation and maintenance management of hydrogen facilities. Deep learning has proven effective for concentration prediction in gas jet diffusion scenarios. Nonetheless, its reliance on extensive simulations as training data and its potential disregard for physical laws limit its applicability to unseen accidental scenarios. Recently, physics-informed neural networks (PINNs) have emerged to reconstruct spatial information by using data from sparsely-distributed sensors which are easily collected in real-world applications. However, prevailing approaches use the fully-connected neural network as the backbone without considering the spatial dependency of sensor data, which reduces the accuracy of concentration prediction. This study introduces the physics-informed graph deep learning approach (Physic_GNN) for efficient and accurate hydrogen jet diffusion prediction by using sparsely-distributed sensor data. Graph neural network (GNN) is used to model the spatial dependency of such sensor data by using graph nodes at which governing equations describing the physical law of hydrogen jet diffusion are immediately solved. The computed residuals are then applied to constrain the training process. Public experimental data of hydrogen jet is used to compare the accuracy and efficiency between our proposed approach Physic_GNN and state-of-the-art PINN. The results demonstrate our Physic_GNN exhibits higher accuracy and physical consistency of centerline concentration prediction given sparse concentration compared to PINN and more efficient compared to OpenFOAM. The proposed approach enables accurate and robust real-time spatial consequence reconstruction and underlying physical mechanisms analysis by using sparse sensor data.

翻译：在意外泄漏过程中，对射流扩散进行高效建模对于氢设施的运行与维护管理至关重要。深度学习已被证明在气体射流扩散场景的浓度预测中行之有效。然而，其对大量模拟数据作为训练数据的依赖以及可能忽视物理定律的局限性，限制了其在未见意外场景中的适用性。近年来，物理信息神经网络（PINNs）通过利用真实应用中易于采集的稀疏分布传感器数据重建空间信息。然而，现有方法通常采用全连接神经网络作为主干架构，未考虑传感器数据的空间依赖性，从而降低了浓度预测的准确性。本研究提出基于物理信息的图深度学习方法（Physic_GNN），利用稀疏分布传感器数据实现高效准确的氢气射流扩散预测。采用图神经网络（GNN）对这种传感器数据的空间依赖性进行建模，通过图节点在描述氢气射流扩散物理定律的控制方程上直接求解。随后将计算得到的残差用于约束训练过程。基于公开的氢气射流实验数据，对比了所提Physic_GNN方法与现有先进PINN方法的准确性与效率。结果表明，在给定稀疏浓度数据的情况下，与PINN相比，我们的Physic_GNN在中心线浓度预测方面展现出更高的准确性和物理一致性，且与OpenFOAM相比计算效率更优。该方法通过稀疏传感器数据，能够实现准确鲁棒的空间实时后果重建及潜在物理机制分析。