Abstract
As more and more multiphysics effects are entering the field of CFD simulations, the question is raised inside the shape optimization community how they can be accurately captured in gradient computations.
The latter has been successfully enriched over the last years by the use of (discrete) adjoints. One can think of them as Lagrange multipliers to the flow field problem linked to an objective function that depends on quantities like pressure or momentums, and they will also set the framework for this paper.
It is split into two main parts: First, we show how one can compute coupled discrete adjoints using automatic differentiation in an effective way that is still easily extendable for all kinds of other couplings.
Second, we suppose that a valuable first application are so-called conjugate heat transfer problems which are gaining more and more interest from the automobile and aeronautics industry. Therefore we present an implementation for this capability within the open-source solver SU2 [1] as well as for the generic adjoint computation algorithm.
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Burghardt, O., Gauger, N.R. (2020). Accurate Gradient Computations for Shape Optimization via Discrete Adjoints in CFD-Related Multiphysics Problems. In: Dillmann, A., Heller, G., Krämer, E., Wagner, C., Tropea, C., Jakirlić, S. (eds) New Results in Numerical and Experimental Fluid Mechanics XII. DGLR 2018. Notes on Numerical Fluid Mechanics and Multidisciplinary Design, vol 142. Springer, Cham. https://doi.org/10.1007/978-3-030-25253-3_3
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DOI: https://doi.org/10.1007/978-3-030-25253-3_3
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