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Multi-objective optimization and parameter sensitivity study on microreactor nuclear power systems
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DOI：10.1016/j.csite.2025.106854.png)
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Abstract
En 中文
The micro-reactor nuclear power system (MRNPS) is an important way to realize the multi-purpose development and utilization of nuclear energy, which can be used for power supply in remote areas, emergency energy security, seawater desalination, nuclear hydrogen production and other occasions. For such applications, it was not only needed to achieve high system thermal efficiency, but also needed to achieve as small as possible and light in weight. Aiming at the MRNPS based on Brayton cycles, the particle swarm optimization (PSO) method was used to carry out the optimization calculation and analysis of three target parameters: system thermal efficiency, power-to-weight ratio and radiator heat removal area. A set of comprehensive calculation models suitable for multi-objective optimization of system performance were established from three aspects, including thermal cycle calculation, heat exchanger thermal balance and component weight estimation. Meanwhile, the sensitivity studies were carried out on several design variables such as compressor inlet temperature, reactor outlet temperature, circulating pressure ratio and heat recuperation ratio. The regression prediction model corresponding to the three target parameters were obtained, which was capable to analyze the significance ranking of the above four key design variables. The results show that the compressor inlet temperature has the most obvious impact on the thermal efficiency of the system and the radiator heat removal area, while the reactor outlet temperature has the most obvious impact on the power-to-weight ratio. The PSO algorithm code was used to calculate and compare the benchmark case of MRNPS, and the optimized Pareto optimal solution set was obtained. The three target parameters were significantly improved compared with the calculation results before optimization, such as the thermal efficiency of the system increased by 30.8 %, the power-to-weight ratio reduced by 49.3 %, and the radiator heat removal area reduced by 25.0 %.
Keywords:
PSO algorithm
Multi-objective optimization
Regression prediction model
Brayton cycles
MRNPS
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