Efficient Airframe Noise Reduction Framework Via Adjoint-Based Shape Optimization

This paper presents an efficient adjoint-based shape optimization framework for airframe noise reduction in which algorithmic differentiation (AD) is applied to the open-source multiphysics solver SU2 to obtain design sensitivities. An AD-based consistent discrete adjoint solver is developed that directly inherits the convergence properties of the primal flow solver due to the differentiation of the entire nonlinear fixed-point iterator. In addition, a 2D and 3D far-field noise prediction framework coupling the Computational Fluid Dynamics (CFD) and Computational Aeroacoustics (CAA) using a permeable surface Ffowcs Williams-Hawkings (FWH) approach in the time domain is also developed. The resultant AD-based discrete adjoint solver is applied to the reduction of noise radiation from 2D and 3D rod-airfoil configurations. The results suggest that the unsteady adjoint information provided by this AD-based discrete adjoint framework is accurate and robust, due to the AD of the entire design chain including the dynamic mesh movement routine and various turbulence models, as well as the hybrid CFD-CAA model. This framework is shown to be highly effective in reducing the tonal noise component due to the turbulent wake impingement on the airfoil leading edge. This study also compares the optimization results obtained on the basis of 3D unsteady Reynolds-averaged Navier-Stokes equation simulations and analyzes them using scale-resolving simulations of higher fidelity.