Abstract:

Computational Fluid Dynamics (CFD) is widely employed to address scientific and engineering challenges. However, conventional CFD approaches often lead to either prohibitively high computational costs or reduced accuracy when computing multi-scale and multi-fidelity problems characterized by significant disparities in length and time scales across different regions of the computational domain. To overcome these limitations, we have developed a novel multi-scale method capable of reproducing fine-mesh accuracy on coarse mesh through the collection and propagation of source terms from fine-mesh regions to adjacent coarse-mesh regions. This approach has been demonstrated to substantially reduce computational expenses while maintaining high fidelity, particularly in cases involving repetitive structures. This method combined with other tools developed in our lab, such as parameterization and automatic mesh generation system, introduced advancements in both turbine blade optimization and electric vertical takeoff and landing (eVTOL) design. For turbine blade optimization, the method effectively addresses the computational challenges posed by fully cooled turbine blades with complex film cooling and internal cooling structures. Furthermore, it enables the establishment of a comprehensive public database for turbomachinery blade design, a resource that was previously unavailable in this field. In eVTOL applications, the proposed method enables effective incorporation of urban wind field effects and the concurrent optimization of the gas turbine power system and the airframe configuration. These advancements improve eVTOL safety and extend operational range through implementation of boundary layer ingestion techniques.

Biography

Dr. Duan Penghao is currently an Assistant Professor at the City University of Hong Kong. He earned his doctoral degree from the University of Oxford, where he subsequently served as a postdoc from 2021 to 2022. Before his doctoral studies, he gained industry experience at ACAE (AECC Commercial Aircraft Engines), China's first commercial aeroengine company. He completed his master's degree at ETH Zürich, during which he conducted thesis research at MIT Gas Turbine Laboratory as a ZKS-funded Visiting Scholar. He obtained a dual bachelor's degree from Tongji University and Politecnico di Milano. In addition to his primary appointment, Dr. Duan is an adjunct professor at Nanjing University of Aeronautics and Astronautics. He co-organized the Artificial Intelligence for Fluid Dynamics and Turbomachinery Conference and chaired sessions at major academic conferences, including ASME TurboExpo, GPPS, and CITC. His research focuses on aircraft engine optimization, multiscale computational fluid dynamics (CFD) for aeronautics, computational grid generation, and CAE software development. Dr. Duan has been recognized as a recipient of China’s National High-level Talent Program (Qi Ming Project). Under his supervision, a student received the Distinguished Student Program Award at the American Physical Society (APS) Global Physics Summit. Over the past two years, he has secured over 10 million HKD in research funding, including competitive grants from the Hong Kong RGC, the NSFC and the Natural Science Foundation of Guangdong Province and research collaborations with leading institutions and industry partners such as Chinese Academy of Sciences, Southern University of Science and Technology, China Unicom, etc. Beyond academia, Dr. Duan is actively involved in entrepreneurship. He founded a national high-tech enterprise. The company is developing TurboThing (http://www.turbothing.com), a cloud-based aeroengine blade design software, which attracts nearly 1,500 daily visits on average.

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