Optimal design of supersonic inlet considering uncertainty

Hai Fang; Suyun Liu; Xiaobing Shang; Xu Duan; Shenming Quan

doi:10.1117/12.3032420

16 August 2024 Optimal design of supersonic inlet considering uncertainty

Hai Fang, Suyun Liu, Xiaobing Shang, Xu Duan, Shenming Quan

Proceedings Volume 13218, First Aerospace Frontiers Conference (AFC 2024); 132180C (2024) https://doi.org/10.1117/12.3032420
Event: First Aerospace Frontiers Conference (AFC 2024), 2024, Xi’an, China

Abstract

The inlet performance of an air-breathing aircraft plays a key role in its cruise flight. The uncertainty and random disturbances in inflow conditions pose a challenge to the inlet performance. This paper conducts research on the optimal design of the supersonic inlet taking into account the uncertainty of the inflow to improve the robustness of the inlet performance. The polynomial chaos expansion method is used to conduct uncertainty analysis, and the expansion coefficient is obtained through the sparse grid point allocation method. Sensitivity analysis of the inflow uncertainty parameters is further conducted to determine the key influencing parameters. Finally, combined with uncertainty analysis and gradient-based optimization method, the supersonic inlet is optimized considering uncertainty. The results show that under the condition of inflow disturbance, in order to meet the flow requirements of the engine, the capture area of the uncertainty optimal design is larger than that of the deterministic optimal design. The optimization design method considering uncertainty can effectively improve the robustness of the inlet performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hai Fang, Suyun Liu, Xiaobing Shang, Xu Duan, and Shenming Quan "Optimal design of supersonic inlet considering uncertainty", Proc. SPIE 13218, First Aerospace Frontiers Conference (AFC 2024), 132180C (16 August 2024); https://doi.org/10.1117/12.3032420

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available