This paper presents an industrial approach to optimisation using the ANSYS Fluent Discrete Adjoint Solver (AS) and a morphing tool utilising Radial Basis Functions (RBF Morph). The approach shown here is based on the use of the AS to drive the shape modification of the considered geometry: the adjoint sensitivities are used to guide intelligent design modifications and improve the product performance. We show also how the presence of geometrical uncertainties can be handled using RBF morph that combines a very accurate control of the geometrical parameters with fast mesh deformation: a system of radial basis functions is used to produce a solution for the mesh movement/morphing, from a list of source points and their displacements. An industrial application is presented to show that the AS can be used for optimisation of a Formula 1 front wing, taking into account the geometrical uncertainties associated with the rotating rubber tire and vehicle steering.
Shape optimisation for aerodynamic performance using adjoint methods
2014
9 Seiten, Bilder, Tabellen, 11 Quellen
Conference paper
English
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