基于改进的k-ω-γ转捩模式预测高超声速飞行器气动特性

Translated title of the contribution: Prediction of aerodynamic characteristics of hypersonic vehicle by improved k-ω-γ transition model

Liang Wang, Ling Zhou*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

In order to verify the influence of boundary layer transition on the aerodynamic characteristics of hypersonic vehicles, an improved k-ω-γ transition model was used to predict the boundary layer transition of the X-51A-like hypersonic vehicle. This study not only analyzed the effects of angle of attack and Reynolds number on the boundary layer transition, but also studied the effects of boundary layer transition on the aerodynamics and the inlet performance of the vehicle. It is found that boundary layer transition has little influence on the lift coefficient and pitching moment coefficient of the vehicle, but has a great effect on the drag coefficient. For the case studied, the drag coefficient predicted by the full laminar flow simulation is 20%~30% lower than that by the transition model. In addition, boundary layer transition can reduce the separation at the corner of the compression surface of the hypersonic vehicle forebody, decrease the Mach number of the throat section, and increase the pressure ratio. Current research results provide a technical reference for the design of control and propulsion systems of hypersonic vehicles, and show that the improved k-ω-γ transition mode has great potential in engineering applications.

Translated title of the contributionPrediction of aerodynamic characteristics of hypersonic vehicle by improved k-ω-γ transition model
Original languageChinese (Traditional)
Pages (from-to)51-61
Number of pages11
JournalKongqi Donglixue Xuebao/Acta Aerodynamica Sinica
Volume39
Issue number3
DOIs
Publication statusPublished - Jun 2021

Fingerprint

Dive into the research topics of 'Prediction of aerodynamic characteristics of hypersonic vehicle by improved k-ω-γ transition model'. Together they form a unique fingerprint.

Cite this