| Summary: | This thesis describes the investigation and development of a novel CAD-based aerodynamic optimisation system, with the aim of allowing gradient-based optimisation of feature-based, parametric models within commercial CAD packages in timescales acceptable for industrial design processes. The process developed is based on linking parametric design velocities (geometric sensitivities computed from the CAD model representing the displacement of a point on the model boundary due to a perturbation of a CAD model parameter) with adjoint surface mesh sensitivities (which represent the derivative of a goal function with respect to surface mesh node position). A CAD-based design velocity computation method has been developed based on projection between discrete representations of perturbed geometries which can be linked to virtually any existing commercial CAD system. A key characteristic of the approach is that it can cope with the discontinuous changes in CAD model topology and face labelling that can occur under even small changes in CAD parameters. Use of the above approach allows computation of parametric sensitivities with respect to aerodynamic coefficients for native CAD parameters within feature-based commercial CAD modelling systems using adjoint data at a computational cost of just one adjoint analysis per objective function and one design velocity field evaluation per parameter. Gradient computation is demonstrated on test cases for an aerofoil model, a turbine blade model and a 3D wing model. Using these computed sensitivities enables the creation of a truly CAD-based aerodynamic optimisation system incorporating adjoint CFD data and using design velocities for computing geometric sensitivities and as input to a mesh deformation step. A prototype implementation of this system is presented and used to optimise a parametric CAD-based aerofoil model. In order to develop the approach further, future work should focus on resolving issues encountered when using design velocities for mesh deformation, extending the approach to more complex test cases, and potentially incorporating parametric effectiveness as a measure of the suitability of a given CAD parameterisation for optimisation purposes.
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