Aerodynamic Characteristics of Morphing Supersonic Cascade Under Low-Upstream-Mach-Number Condition

Author:

Li Chenzhang1,Pan Tianyu1,Yan Zhaoqi1,Zheng Mengzong1,Li Qiushi1,Dowell Earl H.2

Affiliation:

1. Beihang University, 100083 Beijing, People’s Republic of China

2. Duke University, Durham, North Carolina 27708

Abstract

For fans of a variable-cycle engine, a good aerodynamic performance over a wide range of rotating speeds is essential. However, when supersonic cascades designed for a high upstream Mach number operate under a low upstream Mach number, a starting problem may occur, which significantly decreases the aerodynamic performance of fan blades. As a result, the operating range of the supersonic cascades is severely limited. To solve the starting problem caused by the mismatch between geometry of supersonic cascades and upstream Mach number, a morphing supersonic cascade is developed. Thus, this study mainly focuses on the effect of the static deformation of supersonic cascades driven by smart materials on aerodynamic characteristics under a low upstream Mach number. To investigate this issue, numerous simulations are conducted on an S-type cascade by finite element method and computational fluid dynamics. As demonstrated by flow structure analysis, the morphing cascades are started under certain morphing configurations while the original cascade operates at nonstarted state. The results show that the deformation driven by smart materials alters the shock wave structures under a low upstream Mach number by adjusting key cascade geometric parameters. Specifically, the morphing cascades achieve 80% reduction of detached shock loss compared with the original cascades.

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

National Science and Technology Major Project

Advanced Jet Propulsion Creativity Center

Publisher

American Institute of Aeronautics and Astronautics (AIAA)

Subject

Aerospace Engineering

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. AbbottAE: An Autoencoder for Airfoil Aerodynamics;AIAA AVIATION 2023 Forum;2023-06-08

2. Machine learning in aerodynamic shape optimization;Progress in Aerospace Sciences;2022-10

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