Topic: Aeroacoustic Optimization Capabilities in the Open-Source SU2 Solver
Date: Tuesday, March 13, 2018
Time: 11am-noon (EST)
Room: NIA, Rm137
Speaker: Beckett Zhou
Mediasite: http://bit.ly/2FrSpE7
Abstract: A hybrid noise prediction framework is developed for the open-source SU2 solver suite, in which a permeable surface Ffowcs Williams and Hawkings (FW-H) Equation solver is implemented and coupled with an unsteady Reynolds-averaged Navier-Stokes (URANS) solver. The accuracy of this hybrid framework is verified using a number of canonical test cases. A discrete adjoint solver based on algorithmic differentiation (AD) is developed for the coupled system which directly inherits the convergence properties of the primal flow solver due to the differentiation of the entire nonlinear fixed-point iterator. This framework is applied to 2-D and 3-D noise minimization cases via shape optimization. The lift and noise design objectives were shown to be competing in all cases studied — noise minimization always leads to a marked loss of lift. Lift-constrained noise minimization were performed for all 2-D cases and shown to be able to successfully constrain the mean lift at its baseline level while still reducing noise. A number of unconventional optimal designs were obtained, including airfoil designs with wavy surfaces to reduce wake interaction noise. In the 3-D case, the baseline and optimized designs were also analyzed using a turbulence-resolving delayed detached-eddy simulation (DDES). The results indicate that the tonal noise reduction attained by URANS-FWH-based noise minimization is consistent with the higher-fidelity DDES-FWH noise prediction results.
Bio: Beckett Zhou is a Research Scholar at the National Institute of Aerospace. He performed doctoral research on adjoint-based aeroacoustic optimization at the RWTH Aachen University in Germany under the supervision of Professors Nicolas Gauger and Wolfgang Schröder from 2012 to 2017, and will defend his PhD thesis in April 2018. Since 2015, he has led the development of the aeroacoustics branch of the SU2 Solver. His primary research interests are: adjoint-based design optimization, computational aeroacoustics and hybrid RANS/LES methods. He received a Masters in Aeronautics and Astronautics from MIT in 2012, and a Bachelor of Applied Science from the University of Toronto in 2010.