Affiliation:
1. Beihang University, 100191 Beijing, People’s Republic of China
Abstract
The present work aims to develop a stall warning method that is reliable even when the compressor encounters inlet distortion. For this purpose, multicorrelation analysis (MCA) is proposed to provide an effective indicator for the stall warning method. The relationship between MCA and correlation measures was discussed. It was demonstrated that MCA can provide an overall evaluation of both temporal and spatial correlations of multiple signals collected by different sensors. An experimental study of MCA was carried out on a low-speed axial compressor. Sensors were mounted on the casing to sample the dynamic pressure signals used for stall warning. The correlation of the signals was found to be influenced by the steady blade loading and the disturbance energy. Autocorrelation and cross-correlation were adequate when the inlet flow was uniform, but the accuracy of these methods could be affected by distortion. In contrast, MCA was robust under uniform and distorted inlet conditions.
Funder
National Science and Technology Major Project
Key Laboratory of Pre-Research Management Centre
National Natural Science Foundation of China
Science Center for Gas Turbine Project
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Subject
Space and Planetary Science,Mechanical Engineering,Fuel Technology,Aerospace Engineering
Cited by
1 articles.
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