冷却孔形状优化的自主大涡模拟装置

Shubham Agarwal, L. Gicquel, F. Duchaine, N. Odier, J. Dombard, D. Bonneau, Michel Slusarz
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引用次数: 1

摘要

气膜冷却是控制汽轮机叶片热环境的常用技术。众所周知,用于产生冷却膜的孔的几何形状对热性能起着非常重要的作用,找到最优化的形状涉及严格的实验和数值研究,以探测起作用的许多参数。本研究开发了一种自动优化工具,并探索了基于大涡模拟(LES)预测进行孔形优化的能力。为了做到这一点,被称为形状冷却孔的特殊几何形状被选择为这个优化过程的基线几何形状。依靠基于简化模型方法的响应面评估,使用实验设计(DOE)方法可以从用于定义当前形状冷却孔的参数空间中探测一组离散值。首先,从定义孔形状的七个参数中只选择两个参数。随后自动生成孔的几何形状、相应的计算域和相关的网格。一旦几何形状和网格被创建,数值设置就会自动完成,包括对流场的第一次猜测,以增加模拟的收敛性,从而获得可利用的解决方案。最后,利用LES流体流动预测来评估问题响应函数的离散值,然后参与简化模型的构建,从而得出优化结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Autonomous Large Eddy Simulations Setup for Cooling Hole Shape Optimization
Film cooling is a common technique to manage turbine blade thermal environment. The geometry of the holes which are used to generate the cooling film is known to play a very important role on thermal performances and finding the most optimized shape involves rigorous experimental as well as numerical investigations to probe the many parameters at play. For the current study an automatic optimization tool is developed and then probed with the capability of performing hole shape optimization based on Large Eddy Simulation (LES) predictions. To do so, the particular geometry called shaped cooling hole is chosen as a baseline geometry for this optimization process. Relying on the response surface evaluation based on a reduced model approach, the use of a Design of Experiments (DOE) method allows probing a discrete set of values from the parameter space used to define the present shaped cooling hole. At first only two parameters are chosen out of the seven parameters defining the hole shape. This is followed by the automatic generation of the hole geometry, the corresponding computational domain and the associated meshes. Once the geometries and meshes are created, the numerical setup is autonomously completed for each of the cases including a first guess of the flow field to increase convergence of the simulation towards an exploitable solution. To finish, the LES fluid flow prediction is used to evaluate the discrete value of the problem response function which can then participate in the reduced model construction from which the optimization is derived.
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