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Multi-parameter optimization of NPP simulation models using enhanced particle swarm method
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DOI：10.1016/j.pnucene.2025.105671.png)
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摘要
En 中文
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.
Keyword:
Nuclear power plant simulation model
Improved particle swarm algorithm
Parameter optimization
System-level optimization
Digital twin
