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首页> 外文期刊>International Journal of Heat and Mass Transfer >Genetic algorithm based topology optimization of heat exchanger fins used in aerospace applications
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Genetic algorithm based topology optimization of heat exchanger fins used in aerospace applications

机译：基于遗传算法的航空航天应用中热交换器翅片的拓扑优化

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Topology Optimization (TO) in the design of structural components is commonly used and well explored. However, its utilization in the design of complex thermo-fluid equipment used in aerospace applications is limited and relatively new. This is because the coupling between the fluid dynamics, heat transfer, and the shape is complex and nonlinear. Furthermore, the resulting geometry from a TO analysis is often very complex and difficult to manufacture due to the free forms that can occur. With the advent of Additive Manufacturing (AM), however, it has become possible to directly manufacture complex geometries. This study develops a new Genetic Algorithm (GA) based TO combined with Computational Fluid Dynamics (CFD) to produce optimized fin shapes for heat exchangers used in aerospace applications. To implement this approach, a rectangular shaped baseline fin geometry was created using voxel representation. An initial population is generated by mutating the baseline fin a random number of times. The CFD package OpenFOAM is then used to evaluate the performance of each design, after which the optimization algorithm is applied. The GA sorts the designs using a composite fitness function that is comprised of the overall heat transfer and pressure drop, and generates new generations based on mutation and carryover of top performing designs. The study also explores the sensitivity of the GA to the various GA parameters as well as the effect of varying flow Reynolds number. In general, as Reynolds number increases, the percent improvement in the optimum relative to the baseline increases, with potentially an 89% performance improvement. Overall, the approach enables generation of novel freeform designs that may open new performance space for heat transfer applications.
机译：结构部件设计中的拓扑优化（至）是常用的，探索良​​好。然而，它在航空航天应用中使用的复杂热流体设备设计中的利用是有限且相对较新的。这是因为流体动力学，传热和形状之间的耦合是复杂的和非线性的。此外，来自A分析的得到的几何形状通常非常复杂，并且由于可能发生的自由形式而难以制造。然而，随着添加剂制造（AM）的出现，已经可以直接制造复杂的几何形状。该研究开发了一种基于与计算流体动力学（CFD）的新的遗传算法（GA），以生产用于航空航天应用中的热交换器的优化翅片形状。为了实现这种方法，使用体素表示来创建矩形基线鳍片几何。通过突变基线鳍随机次数来生成初始群体。然后使用CFD封装OpenFoam来评估每个设计的性能，之后应用优化算法。 GA使用由整体传热和压降组成的复合健身功能对设计进行排序，并基于顶部执行设计的突变和携带产生新一代。该研究还探讨了GA对各种GA参数的敏感性以及不同流动雷诺数的效果。通常，随着雷诺数的增加，相对于基线最佳的改善百分比增加，潜在的性能提高了89％。总的来说，该方法使得能够产生新颖的自由形式设计，可以为传热应用开辟新的性能空间。

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