Multi-parameter optimization of NPP simulation models using enhanced particle swarm method

This paper delves into the optimization of simulation models for large-scale complex dynamic systems that couple multiple disciplines such as nuclear physics, heat transfer, and fluid mechanics, within the context of digital transformation in nuclear power. An enhanced particle swarm optimization (P...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Progress in nuclear energy (New series) Ročník 184; s. 105671
Hlavní autoři: Li, Zikang, Wang, Hang, Fei, Li, Peng, Minjun, Xian, Zhang, Zhou, Gui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.06.2025
Témata:
Digital twin
Improved particle swarm algorithm
Nuclear power plant simulation model
Parameter optimization
System-level optimization
Improved particle swarm algorithm
Nuclear power plant simulation model
Digital twin
Parameter optimization
System-level optimization
ISSN:0149-1970
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper delves into the optimization of simulation models for large-scale complex dynamic systems that couple multiple disciplines such as nuclear physics, heat transfer, and fluid mechanics, within the context of digital transformation in nuclear power. An enhanced particle swarm optimization (PSO) algorithm-based multi-parameter optimization method is proposed. This method integrates various strategies to improve the simulation accuracy of system-level models in replicating the operational characteristics of real systems. The effectiveness of this method is demonstrated through experiments on simulation models of the reactor coolant system and the chemical and volume control system within a full-range simulator. Post-optimization, the errors of key parameters are reduced to within 2%. This approach not only aids researchers in refining parameter design during the model development phase but also enables automatic parameter adjustments based on the actual system status after deployment. It meets the needs for online optimization and rapid tracking of actual system states in the application of nuclear power digital twin models. •Proposed enhanced PSO for NPP simulation models, integrating multi-strategies.•Optimized 25 parameters of RCS & RCV using plant data, reduced key param discrepancies <2%.•Developed software for auto optimization, improving efficiency.•Analyzed factors influencing opt performance, guiding future research of NPP Digital twins.
ISSN:0149-1970
DOI:10.1016/j.pnucene.2025.105671