Multi-objective optimization of mini U-channel cold plate with SiO2 nanofluid by RSM and NSGA-II

A multi-objective optimization of mini U-channel cold plate with SiO2 nanofluid is conducted to obtain the optimal performance by Response Surface Methodology (RSM) and Non-dominated Sorting Genetic Algorithm (NSGA-II). Numerical investigations arranged by Box-Behnken design are performed to optimize the design variables including inlet velocity (v(in)), inlet temperature (T-in), volume fraction of nanofluid (phi), channel radius (C-r) and channel number (C-n) on the objective functions including maximum temperature (T-max), temperature difference (Delta T) and the pressure drop (Delta p). Analysis of variance (ANOVA) is employed to verify whether the constructed regression models are appropriate and reliable. Response surface analysis is applied to show the interaction effect between each pair of design parameters. With the regression models constructed by RSM, the NSGA-II is adopted to obtain the Pareto-optimal fronts. According to Pareto optimal solution, the optimum objective functions are T-max = 299.42 K, Delta T = 2.66 K, Delta p = 436.19 Pa, respectively, corresponding design variables are v(in) = 0.033 m/s, T-in = 15.04 K, phi = 1.40%, C-r = 0.64 mm and C-n = 6. This work offers us significant reference to design battery thermal management system with nanofluid. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A multi-objective optimization study of a mini U-channel cold plate with SiO2 nanofluid was conducted using Response Surface Methodology (RSM) and Non-dominated Sorting Genetic Algorithm (NSGA-II). Numerical investigations were performed to optimize design variables and objective functions, with analysis of variance used to verify the reliability of the regression models. The optimal design parameters and performance indicators were obtained through Pareto optimal solution.