Application of automatic differentiation to an incompressible URANS solver This paper deals with the task of generating a discrete adjoint solver from a given primal Unsteady Reynolds Averaged Navier-Stokes (URANS) solver for incompressible flows. This adjoint solver is to be employed in active flow control problems to enhance the performance of aerodynamic configurations. We discuss on how the development of such a code can be eased through the use of the reverse mode of Automatic/Algorithmic Differentiation (AD). If AD is applied in a black-box fashion then the resulting adjoint URANS solver will have prohibitively expensive memory requirements. We present several strategies to circumvent the excessive memory demands. We also address the parallelization of the adjoint code and the adjoint counterparts of the MPI directives that are used in the primal solver. The adjoint code is validated by applying it to the standard test case of a rotating cylinder by active flow control. The sensitivities based on the adjoint code are compared with the values obtained from finite differences and forward mode AD code.