JOURNAL ARTICLE

Aerodynamic instability of brush seals in gas turbine engines based on a fluid-structure interaction method.

  • Published In: Physics of Fluids, 2024, v. 36, n. 12. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Liu, Yuxin; Dong, Wenlei; Yue, Benzhuang; Kong, Xiaozhi; Liu, Cunliang 3 of 3

Abstract

This article investigates the circumferential slip instability of brush seal bristles—mechanical seals composed of flexible bristles used in gas turbine engines—under high swirling flow and pressure conditions. Using a two-way fluid-structure coupling method combining three-dimensional computational fluid dynamics and mechanical analysis, the study identifies the ratio of normal to axial aerodynamic forces (Fn/Fax) on the first row of bristles as the key parameter governing slip instability, with slip occurring when this ratio exceeds approximately 0.91. The research shows that higher pressure differentials and downstream pressures influence this force ratio and the critical swirl velocity for slip, with increased downstream pressure reducing axial force but increasing normal force, thereby lowering the swirl velocity threshold for instability. Additionally, the presence and length of a front plate covering the bristles alter upstream flow patterns, increasing Fn/Fax and promoting earlier slip rather than preventing it. These findings provide quantitative criteria for predicting bristle slip and offer insights for the design and stability improvement of brush seals in aeroengine applications.

Additional Information

  • Source:Physics of Fluids. 2024/12, Vol. 36, Issue 12, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:1070-6631
  • DOI:10.1063/5.0243909
  • Accession Number:181974140
  • Copyright Statement:Copyright of Physics of Fluids is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.