A deep-learning reduced-order model for thermal hydraulic characteristics rapid estimation of steam generators

Abstract

Model reduction is a method that maps full-order conservation equations into lower-order subspaces or establish a data-driven surrogate model to reduce the complexity of the entire physical system, which has been widely applied in various fields in recent years. Compared with computational fluid dynamics (CFD) simulations, reduced-order model (ROM) can quickly and instantly obtain simulation results at low cost, which provides an economical alternative approach for the research and design process which need large number of repetitive simulations. In this paper, a deep-learning ROM was developed based on the proper orthogonal decomposition (POD) and machine learning (ML) method. The rapid estimation of two significant thermal hydraulic parameters in steam generator (SG), including the void fraction and temperature, was carried out by ROM. By POD mode analysis, the order for void fraction and temperature field was reduced by 88.3% and 96.7%, respectively. An artificial neural network was trained to reflect the implicit nonlinear mapping relationship between the CFD inputs and feature coefficients. The ROM was validated by comparing the predicted results with refined CFD results. The maximum absolute errors of void fraction and temperature are 0.1 and 0.03 K with speedup on the order of 104, indicating that the developed ROM can quickly and accurately estimate the thermal hydraulic characteristics of SG under different operating conditions. This work may provide a novel approach for the parameter sensitivity analysis and optimization design of SG and give valuable reference for the digital twin and the real-time online monitoring of the SG.