Towards Adjoint-based Broadband Noise Minimization using Stochastic Noise Generation
Paper in proceedings, 2019

In this paper, we present an adjoint-based broadband 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, but also significantly lowers the computational cost and leads to a faster turn-around time for the initial design evaluation phase. Current results show that the RANS-SNG method can efficiently provide broadband noise level assessment for various configurations without resorting to computationally prohibitive scale-resolving simulations. Furthermore, current results also show that the AD-based coupled adjoint-RANS-SNG solver is highly accurate. Finally, shape optimizations
performed on the basis of such coupled-sensitivity are shown to be effective in removing the broadband noise source in the trailing edge of a NACA0012 airfoil profile while maintaining aerodynamic performance imposed as an optimization constraint.

Aeroacoustics

Aerospace Engineering

aeronautics

Computational Mathematics

Computer Science

Author

Beckett Yx Zhou

Technical University of Kaiserslautern

Nicolas R. Gauger

Technical University of Kaiserslautern

Huadong Yao

Chalmers, Mechanics and Maritime Sciences, Fluid Dynamics

Peng Shia-Hui

Chalmers, Mechanics and Maritime Sciences, Fluid Dynamics

Lars Davidson

Chalmers, Mechanics and Maritime Sciences, Fluid Dynamics

Vol. AIAA 2019-0002

AIAA Scitech 2019 Forum
San Diego, California, USA,

Subject Categories

Aerospace Engineering

Computational Mathematics

Computer Science

DOI

10.2514/6.2019-0002

More information

Latest update

4/24/2019