Dr. Yaomin Zhao is a postdoctoral research fellow in the Department of Mechanical Engineering.
His research interests include high-fidelity simulation of turbomachinery flows, turbulence model development with machine learning methods, and boundary layer transition, etc. The objective is to leverage latest capability advances in performing high-fidelity simulations to improve our understanding and modeling of turbulent flows with industrial backgrounds.
Carrer & Education:
- 2017-present: Postdoctoral Research Fellow in the Department of Mechanical Engineering at the University of Melbourne
- 2011-2017: PhD in Department of Mechanics and Engineering Science at Peking Univeristy in 'Lagrangian Investigation on Transitional Wall-Bounded Flows' (with Prof. Shiyi Chen and Prof. Yue Yang)
- 2007-2011: B.S. in Yuanpei College at Peking University, major in Physics
- Outstanding PhD Thesis by Chinese Society of Theoretical and Applied Mechanics, 2018
- Outstanding PhD Thesis by Peking University, China, 2017
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Yaomin Zhao's selected work
Bypass transition in boundary layers subject to strong pressure gradient and curvature eff..
Large-Eddy Simulation and RANS Analysis of the End-Wall Flow in a Linear Low-Pressure Turb..
Large Eddy Simulation and RANS Analysis of the End-Wall Flow in a Linear Low-Pressure-Turb..
Turbulence Model Development for Low & High Pressure Turbines Using a Machine Learning App..
Displaying the 18 most recent scholarly works by Yaomin Zhao.
RANS turbulence model development using CFD-driven machine learning
Y Zhao, HD Akolekar, J Weatheritt, V Michelassi, RD Sandberg
Journal article | 2020 | Journal of Computational Physics
This paper presents a novel CFD-driven machine learning framework to develop Reynolds-averaged Navier-Stokes (RANS) models. The CF..
Data-driven scalar-flux model development with application to jet in cross flow
Jack Weatheritt, Yaomin Zhao, Richard D Sandberg, Satoshi Mizukami, Koichi Tanimoto
Journal article | 2020 | International Journal of Heat and Mass Transfer
The classical gradient-diffusion hypothesis has known deficiencies when applied to cooling applications. In this paper, the gene-e..
Large-Eddy Simulation and RANS Analysis of the End-Wall Flow in a Linear Low-Pressure Turbine Cascade, Part I: Flow and Secondary Vorticity Fields Under Varying Inlet Condition
Richard Pichler, Yaomin Zhao, Richard Sandberg, Vittorio Michelassi, Roberto Pacciani, Michele Marconcini, Andrea Arnone
Journal article | 2019 | Journal of Turbomachinery
Abstract In low-pressure turbines (LPTs), around 60–70% of losses are generated away from end-walls, while the rema..
Turbulence Model Development for Low & High Pressure Turbines Using a Machine Learning Approach
Harshal Akolekar, Yaomin Zhao, Richard Sandberg, Nicholas Hutchins
Conference Proceedings | 2019 | Proceedings of ISABE 2019
Accurately predicting the wake mixing in gas turbines is of utmost importance from the perspective of blade designers as this phen..
Using a new Entropy Loss Analysis to Assess the Accuracy of RANS Predictions of an HPT Vane
Richard Sandberg, Yaomin Zhao
Conference Proceedings | 2019 | Volume 2C: Turbomachinery
Entropy loss is widely used to quantify the efficiency of components in turbomachines, and empirical relations have been developed..
LES AND RANS ANALYSIS OF THE END-WALL FLOW IN A LINEAR LPT CASCADE WITH VARIABLE INLET CONDITIONS, PART II: LOSS GENERATION
Michele Marconcini, Roberto Pacciani, Andrea Arnone, Vittorio Michelassi, Richard Pichler, Yaomin Zhao, Richard Sandberg
Conference Proceedings | 2018 | Volume 2B: Turbomachinery
Research Fellow In Computational Fluid Dynamics
Doctor of Natural Science
Bachelor of Science