Optimisation Of A Turbine Inlet Guide Vane By Gradient-Based And Metamodel-Assisted Methods

INTERNATIONAL JOURNAL OF COMPUTATIONAL FLUID DYNAMICS(2019)

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摘要
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.
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关键词
Kriging, adjoint, CFD, turbomachinery, maximum likelihood, LS-82
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