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

Abstract

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