Multi-objective optimization study of airfoil fin printed circuit heat exchanger with tip gap based on machine learning

Abstract

The printed circuit heat exchanger (PCHE), a compact and highly efficient device, is capable of operating effectively under demanding conditions, which makes it ideal for supercritical CO2 Brayton cycles. In this study, we present a novel airfoil fin PCHE with tip gap, and conduct multi-objective optimization on the tip gap and arrangement of airfoil fins based on the analysis of the supercritical CO2 flow and heat transfer characteristics. We conduct a parameter design using Design of Experiment to examine the impact of the dimensionless design parameters, including the horizontal number (ζh), staggered number (ζs), vertical number (ζv), and gap number (ζg). To forecast the flow and heat transfer performance, we utilize a neural network model called Particle Swarm Optimization-Back Propagation (PSO-BP). We employ the non-dominated sorting genetic algorithm II to obtain the Pareto optimal front by utilizing ζh, ζs, ζv, and ζg as variables for optimization, and the volumetric heat transfer coefficient (hv) and Fanning friction factor (f) as objectives for optimization. The results find that the utilization of tip gap can enhance heat transfer while reducing flow resistance. The PSO-BPNN model exhibits higher prediction accuracy and excellent generalization ability compared with traditional BPNN model. The VIKOR and TOPSIS methods identify compromise schemes with excellent thermal-hydraulic performance. The mechanism of heat transfer enhancement can be elucidated using the principle of field synergy.