JOURNAL ARTICLE

Vortex and energy characteristics in the hump region of pump-turbines based on the rigid vorticity and local hydraulic loss method.

  • Published In: Physics of Fluids, 2025, v. 37, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Xu, Lianchen; Zhang, Yuquan; Xu, Junhui; Zhang, Desheng; Feng, Chen; Zhang, Zhi; Zheng, Yuan 3 of 3

Abstract

This article focuses on investigating the hydraulic losses and vortex dynamics of pump-turbines operating in the hump region, a critical unstable zone affecting the safety and efficiency of pumped-storage hydropower units in power grids. Using three-dimensional numerical simulations based on computational fluid dynamics (CFD), the local hydraulic loss rate method, the Rortex vortex identification technique, and Particle Image Velocimetry (PIV) experiments, the study identifies that energy losses predominantly occur in the guide vane (GV) passage and draft tube (DT), with shear vorticity dominating losses in the vaneless space (VS) and both shear and rigid vorticity influencing the GV and DT. The research further reveals that rotating stall phenomena vary with flow rate and that the pseudo-Lamb term in the enstrophy transport equation plays a key role in vortex evolution and dissipation. These findings contribute to understanding flow instabilities in pump-turbines, which is essential for improving their structural durability and operational stability during energy transition efforts.

Additional Information

  • Source:Physics of Fluids. 2025/03, Vol. 37, Issue 3, p1
  • Document Type:Article
  • Subject Area:Power and Energy
  • Publication Date:2025
  • ISSN:1070-6631
  • DOI:10.1063/5.0264328
  • Accession Number:184176488
  • 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.