Associate Professor, School of Aeronautics, Northwestern Polytechnical University, China
Huan Zhao is an associate professor at the School of Aeronautics, Northwestern Polytechnical University (NPU), China. He specializes in aerodynamics, multidisciplinary design optimization, uncertainty quantification, and machine learning, focusing on CFD simulation, AI-based global optimization, and surrogate modeling. He is also the executive deputy director of the Institute of Digital Intelligence for Flight Mechanics and Aerodynamic Design (IDIFMAD). Zhao has made significant contributions to the fields of aerodynamic shape optimization, high-dimensional global optimization, and uncertainty-based robust design. He holds several patents and has authored many high-impact publications. 🌐✈️
Publication Profile
Education
Huan Zhao completed his Ph.D. in Fluid Dynamics at Northwestern Polytechnical University (NPU) in 2020, following a B.Eng. in Aircraft Design and Engineering from the same university in 2014. 📚🎓
Experience
Zhao served as a tenure-track assistant professor at Sun Yat-sen University (SYSU) before joining NPU as a tenure-track associate professor in 2023. He has directed and participated in numerous research projects focusing on aerodynamic design optimization, high-speed rotor airfoil design, and surrogate-assisted design techniques. He is a principal investigator (PI) for multiple projects funded by the National Natural Science Foundation of China (NSFC). 👨🏫🔬
Awards and Honors
Huan Zhao has received several awards and honors, including recognition as part of the “Hundred Talents Plan” Young Academic Backbone at SYSU and multiple patents for his innovative contributions to aerodynamic design. 🏆🎖️
Research Focus
Zhao’s research interests lie in aerodynamics, including multi-fidelity polynomial chaos-Kriging models, aerodynamic shape optimization, and uncertainty quantification. His work has contributed significantly to the design and optimization of high-lift airfoils, laminar flow airfoils, and robust design methods under uncertainty. His expertise also includes machine learning, AI-based global optimization, and the application of surrogate models in complex design scenarios. 🔍🧑💻
Conclusion
Huan Zhao’s innovative work has had a profound impact on the field of aerodynamics and optimization. His research has not only advanced the understanding of aerodynamic design but has also led to practical improvements in the development of high-performance aircraft and related technologies. He continues to drive forward cutting-edge research in aerodynamics and multidisciplinary design optimization. 🚀🌍
Publications
An efficient adaptive forward–backward selection method for sparse polynomial chaos expansion, Computer Methods in Applied Mechanics and Engineering, 2019.
Review of robust aerodynamic design optimization for air vehicles, Archives of Computational Methods in Engineering, 2019.
Effective robust design of high lift NLF airfoil under multi-parameter uncertainty, Aerospace Science and Technology, 2017.
Adaptive multi-fidelity sparse polynomial chaos-Kriging metamodeling for global approximation of aerodynamic data, Structural and Multidisciplinary Optimization, 2021.
Uncertainty-based design optimization of NLF airfoil for high altitude long endurance unmanned air vehicles, Engineering Computations, 2019.
Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model, Computers & Fluids, 2022.
Research on efficient robust aerodynamic design optimization method of high-speed and high-lift NLF airfoil, Acta Aeronautica et Astronautica Sinica, 2021.
Research on Novel High-Dimensional Surrogate Model-Based Aerodynamic Shape Design Optimization, Acta Aeronautica et Astronautica Sinica, 2022.
Research on novel multi-fidelity surrogate model assisted many-objective global optimization method, Acta Aeronautica et Astronautica Sinica, 2022.
Adaptive multi-fidelity polynomial chaos-Kriging model-based efficient aerodynamic design optimization method, Chinese Journal of Theoretical and Applied Mechanics, 2023.