Multi-objective Optimization Design of Component Cooling System in HPR1000

To enhance the design of the component cooling system (RRI) within the HPR1000 cold chain system and to address issues of low utilization efficiency and poor economic performance under specific design and operational conditions, a mathematical model was developed. This model was designed to evaluate performance indicators based on the principles of heat load transfer, as well as the system design and operational characteristics of the RRI. The presence of multiple user systems and devices introduces complexity into the design process, making it challenging to improve the current state through a singular optimization approach. Therefore, optimization objectives including weight, volume, system investment cost, and energy consumption, were established. To manage this complexity, a novel optimization algorithm was implemented to perform multi-objective optimization. Additionally, a sensitivity analysis was conducted to assess the impact of various optimization variables on these defined objectives. The final selection of optimization variables consisted of the RRI supply water temperature (T1), the design pressure of the RRI (p1), the seawater flow rate for cooling the RRI (G1), and the water supply flow rates to the RFT (G2), RHR (G3), and CSP (G4). Each variable possesses a specific range of values, which is critical for the optimization process. The theoretical model and the final optimization results demonstrate that the proposed evaluation model and optimization algorithm effectively assess the RRI’s performance in multi-objective optimization calculations, allowing for a substantial degree of RRI optimization. The sensitivity analysis reveals that the T1 and G1 are key optimization variables that significantly influence the weight, volume, system investment cost, and energy consumption of the RRI. These parameters exhibit the most considerable optimization potential and should be prioritized in future research and engineering applications. However, it is important to note that achieving an optimal solution that satisfies all objectives while simultaneously optimizing four distinct targets is inherently challenging. The optimization results indicate that the weight, volume, system investment cost, and energy consumption of the RRI can be enhanced by up to 17.91%, 21.08%, 4.83%, and 29.08%, respectively. Furthermore, the intricate relationships among the performance indicators reflect characteristics of non-dominated optimal solutions. Each optimization target demonstrates improvements compared to the baseline design. This optimized design scheme effectively addresses challenges in RRI design, enhances the economic viability of the HPR1000 RRI, and reduces the footprint of the equipment within the factory building. Such advancements have practical engineering significance and provide a valuable reference for future research and design initiatives pertaining to subsequent iterations of the HPR1000 cold chain system.