Optimisation of a turbine inlet guide vane by gradient-based and metamodel-assisted methods. Issue 6 (9th August 2019)

• Record Type: Journal Article
• Authors: Aissa, Mohamed Hassanine; Maffuli, Roberto; Mueller, Lasse; Verstraete, Tom
• Is Part Of: International journal of computational fluid dynamics. Volume 33:Issue 6/7(2019)
• Page Start: 302
• Page End: 316
• Publication Date: 2019-08-09
• Subjects: Kriging -- adjoint -- CFD -- turbomachinery -- maximum likelihood -- LS-82; Fluid dynamics -- Data processing -- Periodicals; 532.05
• DOI: 10.1080/10618562.2019.1683168
• Languages: English
• ISSNs: 1061-8562

Abstract

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.