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Coupling of a mapping method and a genetic algorithm to optimize mixing efficiency in periodic chaotic flows

机译:映射方法与遗传算法的耦合以优化周期性混沌流中的混合效率

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We present a new technique to globally optimize stirring and mixing in three-dimensional spatially periodic laminar flows. This technique includes a recently developed mapping method and the use of an optimization method. The method gives the opportunity of the statistics of scalar dissipation for multiple stirring protocols. Exhaustive tests, first conducted for a large number of stirring protocols show that finding a stirring protocol which produces a uniform mixture is rare for the specific geometries studied. The results indicate that a global approach is necessary to handle the problem of concentration homogeniza-tion. It is shown that Genetic Algorithms enable us to rapidly find the best stirring protocols. Thus, the method described in this paper could be an efficient computational tool for chaotic mixing optimization in other types of periodic flows (2D or 3D).
机译:我们提出了一种新的技术来全局优化三维空间周期性层流中的搅拌和混合。该技术包括最近开发的映射方法和优化方法的使用。该方法为多种搅拌方案提供了标量耗散统计的机会。首先对大量搅拌方案进行的详尽测试表明,对于所研究的特定几何形状,很少找到能够产生均匀混合物的搅拌方案。结果表明,必须采用全局方法来处理浓度均质化问题。结果表明,遗传算法使我们能够快速找到最佳的搅拌方案。因此,本文描述的方法可能是在其他类型的周期性流(2D或3D)中进行混沌混合优化的有效计算工具。

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