Effective end wall profiling rules for a highly loaded compressor cascade

Xiangjun Li, Wuli Chu, Yanhui Wu, Haoguang Zhang, Stephen Spence

Research output: Contribution to journalArticle

4 Citations (Scopus)
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Abstract

This paper presents a numerical study of a linear compressor cascade to investigate the effective end wall profiling rules for highly-loaded axial compressors. The first step in the research applies a correlation analysis for the different flow field parameters by a data mining over 600 profiling samples to quantify how variations of loss, secondary flow and passage vortex interact with each other under the influence of a profiled end wall. The result identifies the dominant role of corner separation for control of total pressure loss, providing a principle that only in the flow field with serious corner separation does the does the profiled end wall change total pressure loss, secondary flow and passage vortex in the same direction. Then in the second step, a multi-objective optimization of a profiled end wall is performed to reduce loss at design point and near stall point. The development of effective end wall profiling rules is based on the manner of secondary flow control rather than the geometry features of the end wall. Using the optimum end wall cases from the Pareto front, a quantitative tool for analyzing secondary flow control is employed. The driving force induced by a profiled end wall on different regions of end wall flow are subjected to a detailed analysis and identified for their positive/negative influences in relieving corner separation, from which the effective profiling rules are further confirmed. It is found that the profiling rules on a cascade show distinct differences at design point and near stall point, thus loss control of different operating points is generally independent.
Original languageEnglish
Number of pages19
JournalProceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy
Early online date18 May 2016
DOIs
Publication statusEarly online date - 18 May 2016

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