Discrete adjoint optimization with OpenFOAM
Aachen (2018, 2019) [Dissertation / PhD Thesis]
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Computer simulations and computer aided design in the past decades have evolved into a valuable instrument, penetrating just about every branch of engineering in industry and academia. More specifically, computational fluid dynamics (CFD) simulations allow to inspect flow phenomena in a variety of applications. As simulation methods evolve, mature, and are adopted by a rising number of users, the demand for methods which not only predict the result of a specific configuration, but can give indications on how to improve the design, increases. This thesis is concerned with the efficient calculation of sensitivity information of CFD algorithms, and their application to numerical optimization. The sensitivities are obtained by applying Algorithmic Differentiation (AD).A specific emphasis of this thesis is placed on the efficient application of adjoint methods, including parallelism, for commonly used CFD finite volume methods (FVM) and their implementation in the open source framework OpenFOAM.