Node Assigned physics-informed neural networks for thermal-hydraulic system simulation: CVH/FL module
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2025
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Abstract
Severe accidents (SAs) in nuclear power plants have been analyzed using
thermal-hydraulic (TH) system codes such as MELCOR and MAAP. These codes
efficiently simulate the progression of SAs, while they still have inherent
limitations due to their inconsistent finite difference schemes. The use of
empirical schemes incorporating both implicit and explicit formulations
inherently induces unidirectional coupling in multi-physics analyses. The
objective of this study is to develop a novel numerical method for TH system
codes using physics-informed neural network (PINN). They have shown strength in
solving multi-physics due to the innate feature of neural networks-automatic
differentiation. We propose a node-assigned PINN (NA-PINN) that is suitable for
the control volume approach-based system codes. NA-PINN addresses the issue of
spatial governing equation variation by assigning an individual network to each
nodalization of the system code, such that spatial information is excluded from
both the input and output domains, and each subnetwork learns to approximate a
purely temporal solution. In this phase, we evaluated the accuracy of the PINN
methods for the hydrodynamic module. In the 6 water tank simulation, PINN and
NA-PINN showed maximum absolute errors of 1.678 and 0.007, respectively. It
should be noted that only NA-PINN demonstrated acceptable accuracy. To the best
of the authors' knowledge, this is the first study to successfully implement a
system code using PINN. Our future work involves extending NA-PINN to a
multi-physics solver and developing it in a surrogate manner.
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| Authors | Jeesuk Shin; Cheolwoong Kim; Sunwoong Yang; Minseo Lee; Sung Joong Kim; Joongoo Jeon |
| Journal | arXiv |
| Year | 2025 |
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