A Discrete Adjoint Framework for Unsteady Aerodynamic and Aeroacoustic Optimization

In this paper, we present an unsteady aerodynamic and aeroacoustic optimization framework in which algorithmic differentiation (AD) is applied to the open-source multi-physics solver SU2 to obtain design sensitivities. An AD-based consistent discrete adjoint solver is developed which directly inherits the convergence properties of the primal flow solver due to the differentiation of the entire nonlinear fixed-point iterator. In addition, a coupled CFD-CAA far-field noise prediction framework using a permeable surface Ffowcs WilliamsHawkings approach is also developed. The resultant AD-based discrete adjoint solver is applied to both drag and noise minimization problems. The results suggest that the unsteady adjoint information provided by this AD-based discrete adjoint framework is accurate and robust, due to the algorithmic differentiation of the entire design chain including the dynamic mesh movement routine and various turbulence model, as well as the hybrid CFD-CAA model.