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

AIAA SciTech Forum

Abstract

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.

Authors 5

1. Beckett Yx Zhou

   University of Kaiserslautern

   Affiliation as printed

   Technical University of Kaiserslautern

2. Nicolas R. Gauger

   University of Kaiserslautern

   Affiliation as printed

   Technical University of Kaiserslautern

3. Sutharsan Satcunanathan Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

4. Matthias H. Meinke Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

5. Wolfgang Schröeder Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University