Single/Multi-Objective Optimization Design and Numerical Studies for Lead-to-Supercritical Carbon Dioxide Heat Exchanger Based on Genetic Algorithm

Single-/multi-objective optimization based on genetic algorithm is employed in the present study to conduct an design for primary heat exchanger (HE) a lead-cooled fast reactor (LFR), where liquid lead and supercritical carbon dioxide (SCO2) are working fluids side cold of HE, respectively. A preliminary model HE was first theoretically calculated by subsection equal transfer power, then performed algorithm, entropy generation number total pumping power were adopted as objective functions. Moreover, numerical simulation Ansys-Fluent software also flow performances optimized exchanger. The results show that irreversible loss reduced 25% after single-objective optimization. hydraulic performance can be together with multi-objective non-dominated sorting II (NSGA-II). In addition, field synergy angle SCO2 decreases, which indicates improvement comprehensive HE. work helpful LFR.