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

2025-04-23

AI Summary

Conventional thermal-hydraulic system codes (e.g., MELCOR, MAAP) for severe nuclear accident analysis suffer from inconsistent finite-difference schemes, mixed explicit–implicit formulations, unidirectional coupling, and spatial mismatch in governing equations. Method: We propose a Node-Allocation Physics-Informed Neural Network (NA-PINN), the first PINN integrated into a system-level thermal-hydraulic simulation framework. By employing control-volume discretization and a node-decoupled network architecture, each subnetwork learns only temporal evolution—eliminating spatial variable dependencies—and enables strong multi-physics coupling. Results: In a six-tank hydraulic module benchmark, NA-PINN achieves a maximum absolute error of 0.007, representing a three-order-of-magnitude improvement over standard PINN (1.678) and marking the first system-level PINN implementation meeting engineering-grade accuracy requirements.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Multiagent Systems: Adversarial Agents
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Develop PINN-based method for thermal-hydraulic system simulation
• Address inconsistent finite difference schemes in TH codes
• Improve accuracy in multi-physics nuclear accident analysis

Innovation

• Node-assigned PINN for system codes
• Individual networks per nodalization
• Excludes spatial information for temporal solutions