Adjoint-based Trailing-edge Noise Minimization using Stochastic Noise Generation

In this paper, we present an adjoint-based trailing-edge noise minimization framework using stochastic noise generation (SNG). The SNG module is implemented in the open-source multi-physics solver suite SU2 and coupled with the existing Reynolds-averaged Navier-Stokes (RANS) to allow fast assessment of broadband noise sources. In addition, a discrete adjoint solver on the basis of algorithmic differentiation (AD) is developed for the coupled RANS-SNG system to enable efficient evaluation of broadband noise design sensitivities. The adjoint-based RANS-SNG framework developed in this work not only avoids the regularization problem that plagues the adjoint solutions for scale-resolving simulations such as large-eddy simulations (LES), but also significantly lowers the computational cost and leads to a faster turn-around time for the initial design evaluation phase. A shape optimization performed on the basis of such coupled-sensitivity is shown to be effective in removing the broadband noise source in the trailing edge of a NACA0012 airfoil profile. The baseline and optimized results are compared by high-fidelity zonal RANS/LES simulations.