Abstract Microrib is a promising structural optimization method proposed in heat transfer research project of scramjet regenerative cooling channels. In this article, a new type of boot-shaped microribs was designed to enhance the heat transfer performance in mini cooling channels for transcritical n-Decane. Response Surface Methodology (RSM) and Multi-objective Genetic Algorithm (MOGA) were utilized to optimize the regenerative cooling channels with boot-shaped ribs. In order to improve the various design variables on the objective functions, a series of flow and thermal characteristics simulations were conducted. Four geometric parameters (h, w, pi and Re) were adopted as the design variables. The average temperature of the heated wall ( ) and the pressure drop between the inlet and oulet ( ) were selected as the objective functions. The statistical importance of the regression models for and was tested by analysis of variance (ANOVA). Then, the Pareto-optimal fronts were obtained via MOGA. The experimental results indicate that the variables influencing and from highest to lowest impact are as follows: Re, pi, h, w. The optimum designing parameters of the regenerative cooling channels with boot-shaped ribs in transcritical regions are found to be h = 0.18 mm, w = 0.05 mm, p = 3.70 mm, Re = 7449.70 mm, corresponding to the minimum values of =912.439 K and =8485.32 Pa.

中文翻译：

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使用 RSM 和 MOGA 多目标优化跨临界区带靴形肋的微型通道的冷却性能

摘要 Microrib是超燃冲压发动机蓄热式冷却通道传热研究项目中提出的一种很有前景的结构优化方法。在本文中，设计了一种新型靴形微肋，以提高跨临界正癸烷微型冷却通道的传热性能。响应面方法 (RSM) 和多目标遗传算法 (MOGA) 被用来优化具有靴形肋的再生冷却通道。为了改进目标函数上的各种设计变量，进行了一系列的流动和热特性模拟。采用四个几何参数（h、w、pi 和 Re）作为设计变量。选择受热壁的平均温度 ( ) 和入口和出口之间的压降 ( ) 作为目标函数。回归模型对 和 的统计重要性通过方差分析 (ANOVA) 进行了测试。然后，通过 MOGA 获得帕累托最优前沿。实验结果表明，影响从高到低的变量如下：Re、pi、h、w。发现跨临界区带靴形肋的蓄热冷却通道的最佳设计参数为 h = 0.18 mm，w = 0.05 mm，p = 3.70 mm，Re = 7449.70 mm，对应的最小值为 =912.439 K和 =8485.32 帕。