Accurate gradient computations for shape optimization via discrete adjoints in CFD-related multiphysics problems
This work addresses the need for accurate gradient computations in multiphysics shape optimization, which is important for industries like automotive and aeronautics, but the approach is incremental as it extends existing adjoint methods.
The paper presents a method for computing coupled discrete adjoints using automatic differentiation for shape optimization in CFD-related multiphysics problems, with an implementation for conjugate heat transfer in the open-source solver SU2.
As more and more multiphysics effects are entering the field of CFD simulations, this raises the question how they can be accurately captured in gradient computations for shape optimization. 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 set also 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 as well as for the generic adjoint computation algorithm.