EVENT CANCELLED - 6 March 2020 at 12:00, Sala Consiglio, Second Floor, Building B12, Campus Bovisa
Dipartimento di Scienze e Tecnologie Aerospaziali
Via La Masa 34
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. 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 tonal 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. A number of unconventional optimal designs were obtained, including airfoil designs with wavy surfaces to reduce wake interaction noise. 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.
Beckett Y. Zhou is a research scientist at the Scientific Computing Group of the Technical University of Kaiserslautern, Germany. He obtained his PhD in computational engineering sciences from the RWTH Aachen University, with a dissertation on Numerical Optimization for Airframe Noise Reduction. He performed post-doctoral research with the Aeroacoustics Branch at the NASA Langley Research Center. His research is focused on efficient adjoint-based aeroacoustic optimization and turbulent flow control. Since 2015, he has been one of the principal developers of the open-source SU2 software package for multi-physics analysis and design, leading the development effort in the area of aeroacoustic prediction and design optimization. He holds a masters degree from MIT and a bachelor’s degree from the University of Toronto, both in aerospace engineering.