  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.
