首页> 外文会议>Evolutionary Multi-Criterion Optimization; Lecture Notes in Computer Science; 4403 >MOGA-II for an Automotive Cooling Duct Optimization on Distributed Resources
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MOGA-II for an Automotive Cooling Duct Optimization on Distributed Resources

机译:MOGA-II用于分布式资源上的汽车冷却风道优化

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In this paper a procedure for the multi-objective optimization of an automotive cooling duct is described. The two objectives considered are the minimization of the pressure drop between the inlet and the outlet of the duct and the maximization of the outlet flow velocity. Since there is no a single optimum to be found, the MOGA-II was used as multi-objective genetic algorithm. The optimization of the duct was obtained employing a parametric model, performing flow analysis with an open source suite and using a multi-objective optimization product. The distributed optimization search exploited the parallelization capabilities of the MOGA-II algorithm which allowed the evaluation of several designs configurations by running concurrent threads of the flow analysis solver. The results obtained are very satisfactory, and the procedure described can be applied to even more complex problems.
机译:在本文中,描述了用于汽车冷却管道的多目标优化的过程。所考虑的两个目标是使管道的入口和出口之间的压降最小和出口流速最大。由于找不到单个最佳值,因此将MOGA-II用作多目标遗传算法。使用参数模型,使用开源套件执行流量分析以及使用多目标优化产品来获得管道的优化。分布式优化搜索利用了MOGA-II算法的并行化功能,该算法允许通过运行流分析求解器的并发线程来评估几种设计配置。获得的结果非常令人满意,并且所描述的过程可以应用于甚至更复杂的问题。

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