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Title: Detailed investigation of loss prediction of an axial compressor cascade at off-design conditions in the presence of incident free-stream disturbances using large eddy simulations
Author: Leggett, John
ISNI:       0000 0004 7234 3037
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2018
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The prediction of an axial compressor’s loss early on in the design phase is a valuable and important part of the design process. The work presented here focuses on assessing the accuracy of current prediction methods, Reynolds Averaged Navier Stokes (RANS), compared with highly accurate Large Eddy Simulations (LES). The simulations were performed at the challenging running conditions of engine relevant Mach (0.67) and Reynolds (300,000) numbers. The work looks at the effects of off-design incidence and the influence of different free-stream disturbances on loss prediction. From the highly accurate datasets produced by the LES the work is able to show how loss attribution varies under different conditions, and goes on to compare how well RANS captures these changes. It was found that overall loss trends are captured well by RANS but substantial differences exist when comparing individual loss sources, which are shown to vary significantly under different running conditions. The investigation into loss attribution is performed using the Denton (1993) loss breakdown as well as a novel application of the Miller (2013) mechanical work potential. In addition to the discovery of the variation in the sources of loss, the comparison between the loss analyses highlighted some of the limitations of the Denton loss breakdown, which was shown to have increasing error under large off-design incidence or in the presence of discrete disturbances. From the comparison of the loss breakdown analyses and LES and RANS flow field results, new insight into the characteristics, limitations and short comings of current modeling techniques have been found. The variation in the sources of loss under different running conditions was also discovered.
Supervisor: Richardson, Edward Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available