SUMMARYA novel domain element shape parameterization method is presented for computational fluid dynamicsbased shape optimization. The method is to achieve two aims: (1) provide a generic 'wrap-around' optimization tool that is independent of both flow solver and grid generation package and (2) provide a method that allows high-fidelity aerodynamic optimization of two-and three-dimensional bodies with a low number of design variables. The parameterization technique uses radial basis functions to transfer domain element movements into deformations of the design surface and corresponding aerodynamic mesh, thus allowing total independence from the grid generation package (structured or unstructured). Independence from the flow solver (either inviscid, viscous, aeroelastic) is achieved by obtaining sensitivity information for an advanced gradient-based optimizer (feasible sequential quadratic programming) by finite-differences.Results are presented for two-dimensional aerofoil inverse design and drag optimization problems. Inverse design results demonstrate that a large proportion of the design space is feasible with a relatively low number of design variables using the domain element parameterization. Heavily constrained (in lift, volume, and moment) two-dimensional aerofoil drag optimization has shown that significant improvements over existing designs can be achieved using this method, through the use of various objective functions.

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SUMMARYAerodynamic shape optimization technology is presented, using an efficient domain element parameterization approach. This provides a method that allows geometries to be parameterized at various levels, ranging from gross three-dimensional planform alterations to detailed local surface changes. Design parameters control the domain element point locations and, through efficient global interpolation functions, deform both the surface geometry and corresponding computational fluid dynamics volume mesh, in a fast, high quality, and robust fashion. This results in total independence from the mesh type (structured or unstructured), and optimization independence from the flow-solver is achieved by obtaining gradient information for an advanced gradient-based optimizer by finite-differences. Hence, the optimization tool can be used in conjunction with any flow-solver and/or mesh generator. Results have been presented recently for two-dimensional aerofoil cases, and shown impressive results; drag reductions of up to 45% were demonstrated using only 22 active design parameters. This paper presents the extension of these methods to three dimensions, with results for highly constrained optimization of a modern aircraft wing in transonic cruise. The optimization uses combined global and local parameters, giving 388 design variables, and produces a shock-free geometry with an 18% reduction in drag, with the added advantage of significantly reduced root moments.

Generic aerodynamic shape optimization technology is presented, based on a domain element approach linked with global interpolation functions. This allows an efficient shape parameterization from which both the design surface geometry and corresponding computational fluid dynamics volume mesh can be deformed directly, in a high quality and robust fashion. The technique also provides a method that allows geometries to be parameterized at various levels, ranging from general planform alterations to detailed local surface changes. The global interpolation developed is totally independent of mesh type (structured or unstructured), and optimization independence from the flow solver is achieved by obtaining sensitivity information for an advanced gradient-based optimizer (feasible sequential quadratic programming) by finite differences. Results have been presented recently for two-dimensional aerofoil cases, and drag reductions of up to 45 per cent were demonstrated. Hence, this article presents initial extension of the method to three dimensions. Results are presented for highly constrained optimization of a modern aircraft wing in transonic cruise, using only planform parameters (design variables), i.e. wing sections can move but may not deform locally. This is done to test and validate the method before moving on to higher fidelity optimizations, and more computationally expensive applications. Even with this fidelity of parameterization, only 30 parameters are used, optimization produces an 8 per cent reduction in drag.