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Loss prediction in an axial compressor cascade at off-design incidences with free stream disturbances using large Eddy simulation

Loss prediction in an axial compressor cascade at off-design incidences with free stream disturbances using large Eddy simulation
Loss prediction in an axial compressor cascade at off-design incidences with free stream disturbances using large Eddy simulation
Axial compressors may be operated under off-design incidences due to variable operating conditions. Therefore, a successful design requires accurate performance and stability limits predictions under a wide operating range. Designers generally rely both on correlations and on RANS, the accuracy of the latter often being questioned. The present study investigates profile losses in an axial compressor linear cascade using both RANS and wall-resolved Large Eddy Simulation (LES), and compares with measurements. The analysis concentrates on "loss buckets", local separation bubbles and boundary layer transition with high levels of free stream turbulence, as encountered in real compressor environment without and with periodic incoming wakes. The work extends previous research with the intention of furthering our understanding of prediction tools and improving our quantification of the physical processes involved in loss generation. The results show that while RANS predicts overall profile losses with good accuracy, the relative importance of the different loss mechanisms does not match with LES, especially at off-design conditions. This implies that a RANS based optimisation of a compressor profile under a wide incidence range may require a thorough LES verification at off-design incidence.
0889-504X
1-13
Leggett, J.
31ece775-d118-4dab-a6ae-c66317e012ac
Priebe, S.
bf2e6c75-7e6e-4e71-8ce3-6f53f0e36d96
Shabbir, A.
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Michelassi, V.
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Sandberg, R.D.
65156e9e-12ce-4709-8a83-f5f8879944cf
Richardson, E.
a8357516-e871-40d8-8a53-de7847aa2d08
Leggett, J.
31ece775-d118-4dab-a6ae-c66317e012ac
Priebe, S.
bf2e6c75-7e6e-4e71-8ce3-6f53f0e36d96
Shabbir, A.
0ea0da57-8989-4119-a8ed-09cc0db0ec79
Michelassi, V.
b596e629-2057-4a5b-afbc-24f891a61638
Sandberg, R.D.
65156e9e-12ce-4709-8a83-f5f8879944cf
Richardson, E.
a8357516-e871-40d8-8a53-de7847aa2d08

Leggett, J., Priebe, S., Shabbir, A., Michelassi, V., Sandberg, R.D. and Richardson, E. (2018) Loss prediction in an axial compressor cascade at off-design incidences with free stream disturbances using large Eddy simulation. Journal of Turbomachinery, 1-13. (doi:10.1115/1.4039807).

Record type: Article

Abstract

Axial compressors may be operated under off-design incidences due to variable operating conditions. Therefore, a successful design requires accurate performance and stability limits predictions under a wide operating range. Designers generally rely both on correlations and on RANS, the accuracy of the latter often being questioned. The present study investigates profile losses in an axial compressor linear cascade using both RANS and wall-resolved Large Eddy Simulation (LES), and compares with measurements. The analysis concentrates on "loss buckets", local separation bubbles and boundary layer transition with high levels of free stream turbulence, as encountered in real compressor environment without and with periodic incoming wakes. The work extends previous research with the intention of furthering our understanding of prediction tools and improving our quantification of the physical processes involved in loss generation. The results show that while RANS predicts overall profile losses with good accuracy, the relative importance of the different loss mechanisms does not match with LES, especially at off-design conditions. This implies that a RANS based optimisation of a compressor profile under a wide incidence range may require a thorough LES verification at off-design incidence.

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TURBO-17-1228-pdf - Accepted Manuscript
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Accepted/In Press date: 12 March 2018
e-pub ahead of print date: 29 March 2018

Identifiers

Local EPrints ID: 420106
URI: http://eprints.soton.ac.uk/id/eprint/420106
ISSN: 0889-504X
PURE UUID: f84f8eaf-6d32-46d9-b1eb-3f4e96ef88c6

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Date deposited: 26 Apr 2018 16:30
Last modified: 25 Nov 2021 17:26

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Contributors

Author: J. Leggett
Author: S. Priebe
Author: A. Shabbir
Author: V. Michelassi
Author: R.D. Sandberg
Author: E. Richardson

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