Multi-objective optimization of a novel butterfly-wing vortex generator fabricated in a rectangular microchannel based on CFD and NSGA-II genetic algorithm

微通道 涡流发生器 田口方法 努塞尔数 计算流体力学 雷诺数 材料科学 水力直径 机械 涡流 数学 物理 湍流 复合材料
作者
Ananta Kumar Das,Somashekhar S. Hiremath
出处
期刊:Applied Thermal Engineering [Elsevier]
卷期号:234: 121187-121187 被引量:4
标识
DOI:10.1016/j.applthermaleng.2023.121187
摘要

A novel butterfly-wing vortex generator is fabricated inside the microchannel of 1 mm width (W), 0.63 mm height (H) and 50 mm length (L) by micro milling process using KERN EVO micro machining center. The Numerical model is validated by the experiments performed in the microchannel with the butterfly-wing of 0.36 mm bigger width (w), 0.18 mm smaller width (b), 0.2 mm wing height (h), 1 mm wing length (l), and a fixed number of wing (n) i.e., 5. Further, the numerical simulation is carried out to study the thermo-hydraulic performance and entropy-generation in the microchannel by employing Taguchi’s design of experiment. Thus, the L27 experimental design is formed by varying the input variables in 3 levels, i.e., w (0.24–0.48) mm, b (0.12–0.24) mm, h (0.2–0.5) mm, l (1–3) mm, and Reynolds number (Re) (142–544). A sensitivity analysis is performed to know the influence of each input parameter by calculating the signal-to-noise ratio for the friction factor (f), average Nusselt number (Nuavg), and total entropy-generation (SG), and the same is verified by the CFD simulation results. A multi-objective optimization is carried out to balance the trade-off between the increment of Nuavg and the decrement of f and SG. In this optimization study, the performance factor (Pf) and augmentation entropy-generation number (Ns,a) are considered as output responses because both the parameters explain the phenomenon of fluid flow, heat transfer and entropy-generation in the microchannel. Further, utilizing the simulation results, an empirical model is developed by response surface method for both the output responses. With the developed empirical model, the optimization is performed using NSGA-II to maximize the Pf and minimize the Ns,a. A decision-making technique, i.e., TOPSIS is used to get the best optimal solution from the acquired pareto-front. The optimal input parameters obtained from the TOPSIS are 0.48 mm (w), 0.12 mm (b), 1 mm (l), 0.38 mm (h) and 544 (Re) with the output responses of Pf = 1.35 andNs,a = 0.67.
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