Advancing buffet onset prediction: a deep learning approach with enhanced interpretability for aerodynamic engineering
Abstract The interaction between the shock wave and boundary layer of transonic wings can trigger periodic self-excited oscillations, resulting in transonic buffet. Buffet severely restricts the flight envelope of civil aircraft and is directly related to their aerodynamic performance and safety. De...
Saved in:
Main Authors: | Jing Wang, Wei Liu, Hairun Xie, Miao Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2024-11-01
|
Series: | Complex & Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s40747-024-01612-y |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Evaluation of Prediction Performances of Deep Learning Models for the Aerodynamic Characteristics of Flettner Rotors
by: Seo Janghoon, et al.
Published: (2024-12-01) -
Deep learning modeling of manufacturing and build variations on multistage axial compressors aerodynamics
by: Giuseppe Bruni, et al.
Published: (2025-01-01) -
Small Radial Compressors: Aerodynamic Design and Analysis
by: K. A. R. Ismail, et al.
Published: (1998-01-01) -
Advanced Compressor Loss Correlations, Part II: Experimental Verifications
by: M. T. Schobeiri
Published: (1997-01-01) -
Explainable AI for DeepFake Detection
by: Nazneen Mansoor, et al.
Published: (2025-01-01)