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Heat transfer enhancement in microchannel heat sinks with dual split-cylinder and its intelligent algorithm based fast optimization

机译:微通道散热器中的传热增强与双分流缸及其基于快速优化的智能算法

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摘要

Microchannel heat sink (MCHS) with dual split-cylinder (initial model) is proposed in this work, to obtain better thermal performance (TP) and eliminate local heat transfer deterioration. Comparing with the MCHS with cy-linder (prototype), the results indicate that the maximum relative increment of TP of initial model is 34.63%, obtained at Re = 200. And, the temperature uniformity is also improved at this case as well. However, this superiority is weakened as Re increases or decreases. Therefore, intelligent algorithm based fast optimization method, combing three dimensional geometry reconstruction, spatial discretization, and simulation, is further established and applied in the initial model. The generalized pattern search algorithm (GPS) is utilized to control the direct search of width (S-width) and location (S-location) of dual split-cylinder channel for optimum TP. In the optimization, the interval size of search is adjusted to improve optimization precision and avoid the duplication of search points. After optimization, the optimum S-width for each Re differs, which is decreased with the increase of Re. While, for all Re, the optimum S-location are decreased to the boundary of design variables range. Accordingly, comparing with initial model, the TP of optimized model is increased a lot at each Re. And, the largest TP = 3.10 is obtained at Re = 300. And that, the maximum relative increment of TP from prototype model to optimized model is obtained at Re = 250, which is 63.41%. Besides, the temperature uniformity of optimized model is also improved effectively, which is more obvious as Re increases.
机译:在这项工作中提出了具有双分裂缸(初始模型)的微通道散热器(MCH),以获得更好的热性能(TP)并消除局部传热劣化。与具有Cy-Linder(原型)的MCH进行比较,结果表明,在RE = 200中获得的初始模型TP的最大相对增量是34.63%。并且,在这种情况下也改善了温度均匀性。然而,随着RE增加或减少,这种优越性被削弱。因此,基于智能算法的快速优化方法,梳理三维几何重建,空间离散化和仿真,在初始模型中应用。广义模式搜索算法(GPS)用于控制双分流缸通道的宽度(S宽度)和位置(S-Location)以获得最佳TP。在优化中,调整搜索的间隔大小以提高优化精度并避免搜索点的重复。优化后,每个RE的最佳S宽度不同,随着RE的增加而降低。虽然,对于所有RE,最佳S位置减少到设计变量范围的边界。因此,与初始模型相比,优化模型的TP在每个RE上增加了很多。并且,在RE = 300中获得最大TP = 3.10.并且在RE = 250处获得从原型模型到优化模型的TP的最大相对增量,其为63.41%。此外,优化模型的温度均匀性也有效地提高,随着重新增加而言,更明显。

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