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Title: CAD-based CFD shape optimisation using discrete adjoint solvers
Author: Xu, Shenren
ISNI:       0000 0004 7962 3250
Awarding Body: Queen Mary, University of London
Current Institution: Queen Mary, University of London
Date of Award: 2015
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Computational fluid dynamics is reaching a level of maturity that it can be used as a predictive tool. Consequently, simulation-driven product design and optimisation is starting to be deployed for industrial applications. When performing gradient-based aerodynamic shape optimisation for industrial applications, adjoint method is preferable as it can compute the design gradient of a small number of objective functions with respect to a large number of design variables efficiently. However, for certain industrial cases, the iterative calculation of steady state nonlinear flow solver based on the Reynolds-averaged Navier{Stokes equations tends to fail to converge asymptotically. For such cases, the adjoint solver usually diverges exponentially, due to the inherited linear instability from the non-converged nonlinear flow. A method for stabilising both the nonlinear flow and the adjoint solutions via an improved timestepping method is developed and applied successfully to industrial relevant test cases. Another challenge in shape optimisation is the shape parametrisation method. A good parametrisation should represent a rich design space to be explored and at the same time be flexible to take into account the various geometric constraints. In addition, it is preferable to be able to transform from the parametrisation to a format readable by most CAD software, such as the STEP le. A novel NURBS-based parametrisation method is developed that uses the control points of the NURBS patches as design variables. In addition, a test-point approach is used to impose various geometric constraints. The parametrisation is fully compatible with most CAD software. The NURBS-based parametrisation is applied to several industrial cases.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Engineering ; Materials Science