Computational prediction of flow around highly loaded compressor-cascade blades with non-linear eddy-viscosity models

W. L. Chen, F. S. Lien, M. A. Leschziner

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

A computational study is presented which examines the predictive performance of two variants of a cubic low-Re eddy-viscosity model when applied to the flow around two highly loaded compressor-cascade blades. Particular challenges are posed by the transitional nature of the flow and the presence of laminar leading-edge separation in off-design conditions. Comparisons are presented for local as well as integral flow parameters, including turbulence intensity and losses. The study demonstrates that the cubic model is able to predict, in accord with experimental data and in contrast to a linear base-line model, the influential leading-edge separation proceding transition, due to the suppression of turbulence-energy generation in the impinging zone ahead of the leading edge. This attribute and a greater sensitivity to streamline curvature enable the model to give, in some flow conditions, a more realistic description of the development of the boundary layer after transition and a better prediction of the loss at off-design incidence. However, the model also exhibits predictive weaknesses, among them an inappropriate delay in reattachment and transition to fully developed turbulence, resulting in insufficient sensitivity to adverse pressure gradient once transition has occurred.

Original languageEnglish
Pages (from-to)307-319
Number of pages13
JournalInternational Journal of Heat and Fluid Flow
Volume19
Issue number4
DOIs
Publication statusPublished - 1998 Aug

All Science Journal Classification (ASJC) codes

  • Condensed Matter Physics
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

Fingerprint Dive into the research topics of 'Computational prediction of flow around highly loaded compressor-cascade blades with non-linear eddy-viscosity models'. Together they form a unique fingerprint.

Cite this