Assessment of pressure field and performance parameter reconstruction from velocity data for ramjet inlet

被引:6
作者
Liu, Shun [1 ]
Xu, Jinglei [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Jiangsu Prov Key Lab Aerosp Power Syst, Nanjing 210016, Peoples R China
基金
中国国家自然科学基金;
关键词
Ramjet inlet; Particle image velocimetry; Pressure reconstruction; Performance parameter measurement; AERODYNAMIC PERFORMANCE; COMPRESSIBLE FLOWS; HYPERSONIC INLET; SCRAMJET INLET; SHOCK TRAIN; FLOWFIELD; PIV;
D O I
10.1016/j.ast.2020.106454
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The current flow measurement devices for ramjet inlet still have many limitations in the supersonic internal flow, such as the qualitative feature of schlieren and the low resolution of pressure tap/probe. In this paper, a non-intrusive measurement scheme for the internal pressure field and performance parameters of ramjet inlet based on particle image velocimetry (PIV) data is established and quantitatively evaluated by using a typical model. This scheme is combined with two important PIV-based pressure reconstruction approaches, namely, the spatial integration in conservative form and the MacCormack method, which are especially suitable for the supersonic flows with shock waves. The feasibility of this scheme is firstly confirmed by using a numerical velocity field, and the turbulence contribution is concluded to be negligible. Then, a simulated PIV experiment is conducted to achieve a synthetic velocity field with various theoretical measurement errors for the comprehensive analyses of the accuracy and robustness of this measurement method. Compared with the traditional equipment, the PIV-based method can obtain the high-resolution velocity and pressure distributions of whole flow field without interference, and the performance parameters are also available along the flow path. The good reconstruction results show the great advantage and attraction of this non-intrusive method in the future wind tunnel test for ramjet inlet. (C) 2020 Elsevier Masson SAS. All rights reserved.
引用
收藏
页数:11
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