  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.
