2026-10-12T14:36:26Z https://www.scidb.cn/oai

10.57760/sciencedb.hjs.00406 2025-04-14T16:14:56Z

2025-04-14 Inverse optimization design of heat transfer performance for the reactor pressure vessel insulation structure based on reduced-order model doi:10.57760/sciencedb.hjs.00406 en [Background] Thermal leakage in reactor pressure vessel (RPV) insulation structure poses safety risks, and traditional trial-and-error parameter optimization methods are inefficient for handling complex variables. [Purpose] This study aims to develop a proper orthogonal decomposition (POD)-based inverse design methodology to enhance the thermal performance of RPV insulation structure. [Methods] The optimization variables and their level numbers for RPV insulation structure were systematically determined. A comprehensive dataset comprising 24 CFD simulations was generated through full factorial design method, with subsequent computational results employed as POD snapshots. This was followed by POD-based reconstruction to establish 275 validated cases. Three optimization objectives were defined: average heat flux, average temperature on the RPV insulation’s outer wall, and maximum local temperature of the concrete. Based on the above three optimization objectives, a dimensionless comprehensive score on a percentage scale was established to evaluate the overall heat transfer performance. [Results] The POD reconstruction achieved high fidelity, with an average determination coefficient exceeding 0.95 and mean relative deviation (MRD) below 2.87%, while reducing CPU time by 99% compared to CFD. The optimal variable combination (2 mm-16℃-309.5℃) yielded a comprehensive score of 97.4, improving overall heat transfer performance by 59.2% over the original design. [Conclusions] The POD-based methodology provides an efficient and precise approach for RPV insulation structure optimization, significantly enhancing safety and operational efficiency. RPV insulation structure; Reduced-order model; Inverse design; Numerical simulation; Proper orthogonal decomposition(POD) Jie Zhao Dai Yanjun Wang Yungang Guo Zhenyuan Bai fan PUBLIC https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset http://www.doi.org/10.3724/j.0253-3219.2026.hjs.49.240552 Science Data Bank