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Optimisation of a turbine inlet guide vane by gradient-based and metamodel-assisted methods
International Journal of Computational Fluid Dynamics ( IF 1.3 ) Pub Date : 2019-08-09 , DOI: 10.1080/10618562.2019.1683168
Mohamed Hassanine Aissa 1 , Roberto Maffulli 2 , Lasse Mueller 1 , Tom Verstraete 1
Affiliation  

ABSTRACT Design processes nowadays rely more and more on automated optimisation methods to shorten the development cycle. Within those optimisation methods, gradient-free ones converge slower but rather to a global optimum, while gradient-based methods converge faster to a local optimum. Quite recently gradient-free methods have been assisted by metamodels to improve their convergence and gradient-based methods are making use of adjoints to speed up the gradient evaluation. In this article, we compare an adjoint-assisted gradient-based and a metamodel-assisted gradient-free method with respect to convergence, local/global optima and especially the computational time. On a constrained multipoint aerodynamic optimisation of a turbine inlet vane, gradient-based and gradient-free methods reached 22% and 24%, respectively, of total pressure loss reduction. The metamodel-assisted method reached a 2% higher objective value at double the cost of flow evaluations, an additional cost related mainly to the evaluation of an initial database.

中文翻译:

通过基于梯度和元模型辅助的方法优化涡轮进口导叶

摘要 现在的设计过程越来越依赖于自动化优化方法来缩短开发周期。在这些优化方法中,无梯度的方法收敛较慢,但收敛到全局最优,而基于梯度的方法收敛到局部最优的速度更快。最近,元模型辅助无梯度方法来提高它们的收敛性,而基于梯度的方法正在利用伴随来加速梯度评估。在本文中,我们比较了基于伴随辅助梯度和元模型辅助无梯度的方法在收敛性、局部/全局最优,尤其是计算时间方面。在涡轮进口叶片的约束多点气动优化上,基于梯度和无梯度的方法分别达到了 22% 和 24%,总压力损失减少。元模型辅助方法以流量评估成本的两倍达到了高出 2% 的目标值,额外的成本主要与初始数据库的评估有关。
更新日期:2019-08-09
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