Paul-Ehrlich-Straße 34/Geb. 36
Phone: +49 (0)631 205 5639
The over-arching goal of my research is to provide the aviation community the tools it urgently needs to analyze, design and certify the next-generation aircraft that are safer, greener and quieter. To that end, I focus on developing efficient adjoint-based optimization methods and multi-fidelity simulation methodologies to enable efficient aerodynamic and aeroacoustic designs.
Since 2015, I have 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. Active users of the SU2-CAA solver include NASA, Embraer S.A. and a number of research groups in academia.
Meanwhile, I serve as a consultant to the NASA Langley Research Center on the topic of adjoint-based aeroacoustic design optimization.
In addition to aeroacoustic prediction and optimization, other areas of my research interest include: hybrid RANS/LES methods for turbulent flows, design under uncertainty, and bio-inspired aeronautical designs.
- 12/2018 – Present: Research Scientist at Scientific Computing Group, TU Kaiserslautern Consultant for NASA Langley Research Center on Aeroacoustic Optimization
- 02/2018 – 11/2018: Postdoctoral Researcher in Aeroacoustics at NASA Langley Research Center and National Institute of Aerospace
- 04/2018: Ph.D. in Engineering with N. R. Gauger and W. Schröder, RWTH Aachen University
- 09/2014 – 02/2018: Research assistant at Scientific Computing Group, TU Kaiserslautern
- 10/2012 – 08/2014: Research assistant at MathCCES, RWTH Aachen University
- 09/2012: M.S. in Aeronautics and Astronautics with K. E. Willcox, Massachusetts Institute of Technology
- 05/2010: B.A.Sc., Engineering Science (Aerospace), University of Toronto
Rotor/Propeller Noise Prediction and Minimization
This NASA-funded effort focuses on the development of an integrated aeroacoustic optimization framework to enable efficient noise-reducing designs of propeller and rotor blades, in support of the NASA Transformational Tools and Technologies (TTT) Program. This project builds on the existing development of the fixed-body Ffowcs Williams-Hawkings solver and the coupled CFD-FWH adjoint in SU2 (see Past Projects below). In particular, the current FWH implementation will be generalized to account for moving sources. The coupled adjoint solver will also be modified to enable sensitivity computations on rotating/sliding meshes. In addition to isolated propellers/rotors, prop-prop and prop-wing interaction noise will also be investigated. The target configuration is the NASA X57 Maxwell distributed propulsion aircraft.
Project Partners: Leonard V. Lopes (NASA Langley Research Center)
Omur Icke, Andy Moy and Oktay Baysal (Old Dominion University)
Boris Diskin (National Institute of Aerospace)
Funding Source: NASA Transformational Tools and Technologies (TTT) Program
Validation Campaign for the SU2 Aeroacoustic Solver
A range of aeroacoustic benchmark cases are scheduled to be examined to validate the noise prediction capabilities of the SU2 aeroacoustic solver. The turbulent flow field is resolved with the DDES solver with a new shear-layer adapted (SLA) subgrid scale model and the modified simple low dissipation AUSM (SLAU2) flux scheme. The acoustic propagation is performed with the solid/permeable-surface FWH solver. The hybrid DDES-FWH framework has been applied to the first validation case of the tandem cylinder. Turbulent flow quantities as well as far-field noise levels are compared against measurements obtained at NASA facilities. Other planned validation cases include: 30P30N three-element high-lift configuration, single-stream round jet, and rudimentary landing gear.
Project Partners: Eduardo E. Molina and Juan J. Alonso (Stanford University)
- E.S. Molina, B.Y. Zhou, J.J. Alonso, M. Righi, R. G. Silva, “Flow and Noise Predictions Around Tandem Cylinders using DDES Approach with SU2”, AIAA-2019- 0326, AIAA SciTech Forum, San Diego, California, January 2019.
Adjoint-based Broadband Noise Minimization using Stochastic Noise Generation
The conventional approach to predict broadband noise (BBN) is to first resolve the near-field turbulent flow with a scale-resolving method such as LBM, LES or hybrid RANS/LES. The noise source is then propagated to the far-field using an acoustic analogy such as FWH. This two-stage approach is well-established in the aeroacoustic community. However, when an adjoint-based method is developed for such framework for optimal design purposes, the chaotic nature of the underlying turbulent flow causes the adjoint solution to diverge — an active research topic investigated by several groups. In this work, we develop an efficient framework based on stochastic noise generation (SNG), whereby the BBN source is stochastically synthesized using the mean flow turbulent quantities extracted from a preceding RANS solution. In addition to accessing BBN source characteristics and providing reliable design trends at a significantly reduced computational cost, this method also circumvents the divergence issue plaguing the adjoint solutions for scale-resolving simulations. This framework has been implemented in the SU2 solver suite, with coupled RANS-SNG adjoint to enable design optimization. Efforts to verify the reliability of this method using LES+FWH are currently underway.
Project Partners: Hua-Dong Yao, Shia-Hui Peng and Lars Davidson (Chalmers University of Technology)
- B.Y. Zhou, N.R. Gauger, H-D. Yao, S-H. Peng, L. Davidson, “Towards Adjoint-based Broadband Noise Minimization using Stochastic Noise Generation”, AIAA-2019- 0002, AIAA SciTech Forum, San Diego, California, January 2019.
Automated Mesh Generation and Adaptation for Turbulent Flow Simulations in SU2
In this exploratory effort, undertaken jointly with mesh generation experts at the Old Dominion University and NASA Langley Research Center, an automatic grid generation procedure is coupled with the open-source SU2 solver suite. The proposed method is based on two building blocks targeting two different regions of the input geometry — a fully automated Boundary Layer (BL) approach capable of handling arbitrary geometries for the viscous region and a parallel local reconnection method for the inviscid region. The mesh thus generated is used by the SU2 DDES solver to predict the turbulent flow over a delta wing experiencing vortex breakdown. The DDES solver employs a new shear-layer adapted (SLA) subgrid scale model and the modified simple low dissipation AUSM (SLAU2) flux scheme. It is also planned to explore solution-based anisotropic grid adaptation for turbulent flows in the near future. Other configurations of interest include chevron nozzles and landing gear models with increasing levels of complexity.
Project Partners: Christos Tsolakis, Juliette Pardue, Andrey Chernikov and Nikos Chrisochoides (ODU)
Mike Park (NASA Langley Research Center)
Boris Diskin and Hiro Nishikawa (National Institute of Aerospace)
Numerical Optimization of Porous Surfaces for Trailing-Edge Noise Reduction (Phase II: Broadband Noise)
In this project, we continue our earlier effort (see Past Projects below) in developing an adjoint-based framework for the optimal design of porous aerodynamic surfaces to reduce trailing-edge noise, which is a dominant airframe noise source on an aircraft flying in the ‘clean’ configuration with all high-lift devices and landing gear stowed. The focus of the current work is on broadband noise. To that end, porous material is applied to an airfoil section with a sharp trailing edge. In addition, a poro-serrated trailing edge design is also investigated. Both aerodynamic and aeroacoustic performances of the designs are evaluated at two operating conditions: the low-Mach number takeoff/landing condition and the transonic cruise condition.
Project Partners: Matthias Meinke and Wolfgang Schröder (Aachen Institute of Aerodynamics)
Funding Source: German Research Foundation (DFG)
Development of Aeroacoustic Prediction and Optimization Capabilities in SU2
A permeable surface Ffowcs Williams-Hawkings (FW-H) acoustic solver is implemented in the open-source multi-physics solver SU2. The FWH solver is coupled with the existing URANS and DDES solvers in SU2 for aeroacoustic acoustic predictions at arbitrary far-field observer locations. In addition, a consistent and robust discrete adjoint solver is developed on the basis of algorithmic differentiation (AD), to enable efficient aero-acoustic design sensitivity evaluation of the coupled CFD-FWH system.
This coupled simulation and design framework is applied to the shape optimization of an airfoil section of a rod-airfoil configuration, to minimize the interaction noise. The optimization is conducted on the basis of URANS-FWH while the aeroacoustic analysis of the baseline and optimized designs are performed using the high fidelity DDES-FWH solver.
Project Partners: Carlos R. Ilário da Silva (Embraer S. A.)
Thomas Economon and Juan J. Alonso (Stanford University)
Funding Source: Natural Science and Engineering Research Council Postgraduate Scholarship (NSERC-PGS-D)
- B.Y. Zhou, T.A. Albring, N.R. Gauger, C.R. Ilario da Silva, T.D. Economon, and J.J. Alonso, “Reduction of Airframe Noise Components Using a Discrete Adjoint Approach”, AIAA-2017- 3658, 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Denver, Colorado, June 2017.
- B.Y. Zhou, T.A. Albring, N.R. Gauger, C.R. Ilario da Silva, T.D. Economon, and J.J. Alonso, “A Discrete Adjoint Approach for Jet-Flap Interaction Noise Reduction”, AIAA 2017-0130, 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Grapevine, TX, January 2017.
- B.Y. Zhou, T.A. Albring, N.R. Gauger, T.D. Economon, F. Palacios, and J.J. Alonso, “A Discrete Adjoint Framework for Unsteady Aerodynamic and Aeroacoustic Optimization”, AIAA-2015-3355, 16th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Dallas, TX, June 2015. (Best Paper Award)
Numerical Optimization of Porous Surfaces for Trailing-Edge Noise Reduction (Phase I: Tonal Noise)
In order to meet the stringent noise emission requirement imposed by various regulatory bodies, it is insufficient to simply reduce the noise components related to undercarriages and high-lift devices — the noise from the ‘clean’ aircraft flying at constant altitude with all high-lift devices and landing gear stowed, must also be reduced. In this work, we focus on the minimization of trailing-edge noise — a dominant airframe noise mechanism on an aircraft flying in the clean configuration, generated due to the scattering of energy in turbulent eddies in the boundary layer as they convect across a trailing edge. To that end, porous material is applied to a flat plate with a blunt trailing edge, which exhibits a pronounced tonal noise component associated with periodic vortex shedding due to the bluntness of the trailing edge.
Adjoint-based noise minimization is performed to determine the optimal distribution of the design variables that govern the porosity and permeability of the trailing edge material. The optimal design obtained is found to attain a maximum noise reduction in OASPL of 12dB from the solid trailing edge and 3dB from the baseline design with a linear porosity variation respectively. The design space of this optimization problem is also found to be multi-modal.
Project Partners: Seong R. Koh, Matthias Meinke and Wolfgang Schröder (Aachen Institute of Aerodynamics)
Funding Source: German Research Foundation (DFG)
- B.Y. Zhou, N. R. Gauger, S. Koh, M. Meinke, and W. Schröder “A Discrete Adjoint Framework for Trailing-Edge Noise Minimization via Porous Material”, Computers and Fluids, Vol. 172, pp. 97-108, May 2018
- S. Koh, B.Y. Zhou, M. Meinke, N. R. Gauger, and W. Schröder “Numerical Analysis of the Impact of Variable Porosity on Trailing-Edge Noise”, Computers and Fluids, Vol. 167, pp. 66-81, June 2018
|(2019): Adjoint-based Broadband Noise Minimization using Stochastic Noise Generation. In: AIAA 2019-2697, 2019.|
|(2019): Flow and Noise Predictions Around Tandem Cylinders using DDES Approach with SU2. In: AIAA 2019-0326, 2019.|
|(2019): Towards Adjoint-based Broadband Noise Minimization using Stochastic Noise Generation. In: AIAA 2019-0002, 2019.|
|(2019): Challenges in Sensitivity Computations for (D) DES and URANS. In: AIAA 2019-0169, 2019.|
|(2018): A Discrete Adjoint Framework for Trailing-Edge Noise Minimization via Porous Material. In: Computers and Fluids, 172 pp. 97–108, 2018.|
|(2018): Numerical Analysis of the Impact of Variable Porosity on Trailing-Edge Noise. In: Computers and Fluids, 167 pp. 66-81, 2018.|
|(2018): Low-cost unsteady discrete adjoints for aeroacoustic optimization using temporal and spatial coarsening techniques. In: AIAA 2018-1911, 2018.|
|(2017): Impact of Permeable Surface on Trailing-Edge Noise at Varying Lift. In: AIAA 2017-3497, 2017.|
|(2017): Reduction of Airframe Noise Components Using a Discrete Adjoint Approach. In: AIAA 2017-3658, 2017.|
|(2017): A Discrete Adjoint Approach for Jet-Flap Interaction Noise Reduction. In: AIAA 2017-0130, 2017.|
|(2016): Optimal Flow Actuation for Separation Control and Noise Minimization. In: Proceedings of 9th International Conference on Computational Fluid Dynamics, ICCFD9-2016-191, 2016.|
|(2016): An Efficient Unsteady Aerodynamic and Aeroacoustic Design Framework Using Discrete Adjoint. In: AIAA 2016-3369, 2016.|
|(2016): A Discrete Adjoint Framework for Trailing-Edge Turbulence Control and Noise Minimization via Porous Material. In: AIAA 2016-2777, 2016.|
|(2016): Towards adjoint-based trailing-edge noise minimization using porous material. In: Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 132 pp. 789-798, 2016.|
|(2015): A Discrete Adjoint Framework for Unsteady Aerodynamic and Aeroacoustic Optimization. In: AIAA 2015-3355, 2015.|
|(2015): On the Adjoint-based Control of Trailing-Edge Turbulence and Noise Minimization via Porous Material. In: AIAA 2015-2530, 2015.|
|(2015): An Aerodynamic Design Framework based on Algorithmic Differentiation. In: ERCOFTAC Bulletin, 102 pp. 10-16, 2015.|
|(2015): A Discrete Adjoint Approach For Trailing-Edge Noise Minimization using Porous Material. In: Computational Methods in Applied Sciences, 36 pp. 351–365, 2015.|
|(2014): Adjoint-based Trailing-Edge Noise Minimization using Porous Material. In: AIAA 2014-3040, 2014.|
|(2014): Noise Sources of Trailing-Edge Turbulence Contolled by Porous Media. In: AIAA 2014-3038, 2014.|
|(2014): Towards Adjoint-based Trailing-Edge Noise Minimization using Porous Material. In: Jahresbericht der Deutschen Strömungsmechanischen Arbeitsgemeinschaft STAB, pp. 204–205, 2014.|