Node Assigned physics-informed neural networks for thermal-hydraulic system simulation: CVH/FL module

Node Assigned physics-informed neural networks for thermal-hydraulic system simulation: CVH/FL module Jeesuk Shin a,1, Cheolwoong Kim b,1, Sunwoong Yang c, Minseo Lee a, Sung Joong Kim b,, Joongoo Jeon a,d,e, a Department of Applied Plasma and Quantum Beam Engineering, Jeonbuk National University, Jeonju-si, Republic of Korea b Department of Nuclear Engineering, Hanyang University, Seoul, Republic of Korea c Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea d Department of Quantum System Engineering, Jeonbuk National University, Jeonju-si, Republic of Korea e Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju-si, Republic of KoreaAbstract 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 Corresponding author Corresponding author Email addresses: sungjkim@hanyang.ac.kr (Sung Joong Kim), jgjeon41@jbnu.ac.kr (Joongoo Jeon) 1 These authors contributed equally to this work. 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 Keywords: FDM, PINN, Thermal-hydraulics, Control-volume approach1. INTRODUCTION Due to the extremely low frequency of severe accident (SA) in nuclear power plants (NPPs) and the limited availability of real-world accident data, SA-related research inevitably relies on the use of system codes to simulate hypothetical accident scenarios and assess the potential safety concerns. Widely used system codes, such as RELAP5/SCDAP, MAAP, and MEL-COR, model the physical behavior of NPP components and simulate accident progression by accounting for complex thermal-hydraulic (TH) and physicochemical interactions arising under SA conditions.