加权
马赫数
空气动力学
涡轮机械
航程(航空)
机械
数学
平面(几何)
喷嘴
流量(数学)
物理
工程类
几何学
声学
机械工程
航空航天工程
作者
Daniel Burdett,Thomas Povey
出处
期刊:Journal of turbomachinery
[ASME International]
日期:2022-01-28
卷期号:144 (5)
被引量:4
摘要
Abstract A common objective in the analysis of turbomachinery components (nozzle guide vanes (NGVs) or rotor blades, for example) is to calculate performance parameters, such as total pressure or kinetic energy (KE) loss coefficients, from measurements in a nonuniform flow-field. These performance parameters can be represented in a range of ways. For example, line-averages used to compare performance between different radial sections of a 3D component; plane-averages used to assess flow (perhaps loss coefficient) development between different axial planes; and fully mixed-out values used to determine the total loss associated with a component. In the literature, the weighting method used for line- and plane-averaging (e.g., area, volume flow, mass flow, or entropy-flux) is sometimes regarded as an unimportant issue. Indeed, many authors neglect to even state which weighting method was used in their work. In certain low-speed test cases, or where measurements are made a long distance from the component, the nonuniformity in the flow will be relatively small and the practical difference between different weighting methods may be negligible. However, in high-speed applications or for measurements close to a component trailing edge, this becomes increasingly unlikely. In this paper, we compare a range of methods for calculating aerodynamic performance parameters—for example, the kinetic energy loss coefficient—including plane-average methods with different weighting schemes and several mixed-out methods. We analyze the sensitivities of the different methods to the axial location of the measurement plane, the radial averaging range, and the exit Mach number. We use high-fidelity experimental data taken in several axial planes downstream of a cascade of engine parts: high-pressure (HP) turbine NGVs operating at transonic Mach number. The experimental data are complemented by computational fluid dynamics (CFD). We discuss the underlying physical mechanisms which give rise to the observed sensitivities. The objective is to provide guidance on the accuracy of each method in a relevant, practical application.
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