Parameter Identification of the Lagrangian-averaged Vorticity Deviation Vortex Detection Method Through the Investigation of Fluid Flow Around Solid Bodies

Authors

  • Kinga Andrea Kovács
    Affiliation
    Department of Fluid Mechanics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
  • Esztella Balla
    Affiliation
    Department of Fluid Mechanics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
https://doi.org/10.3311/PPme.22874

Abstract

The main focus of the current paper is the detection of vortices in fluid flow around a circular cylinder and a square cylinder, with an emphasis on the identification of the parameters used for vortex detection. The authors aim to enhance the practicality of an existing vortex detection method (Lagrangian-averaged vorticity deviation) by providing recommendations for the settings of the vortex detection parameters. The simulations were carried out using ANSYS Workbench 2022 R2, encompassing Reynolds numbers between 12 and 140, and angles of incidence from 0° to 45°. The vortex detection was performed using MATLAB R2020b. The paper provides a comprehensive description of the parameters involved in the detection process and their significance, as well as the implementation of the parameter identification. The study results in the determination of the suggested parameter ranges, and a comparative analysis of different vortex detection methods is also presented for the case of the circular cylinder.

Keywords:

aerodynamics, bluff bodies, circular cylinder, vortex detection, parameter identification, square cylinder

Citation data from Crossref and Scopus

Published Online

2023-10-10

How to Cite

Kovács, K. A., Balla, E. “Parameter Identification of the Lagrangian-averaged Vorticity Deviation Vortex Detection Method Through the Investigation of Fluid Flow Around Solid Bodies”, Periodica Polytechnica Mechanical Engineering, 67(4), pp. 293–302, 2023. https://doi.org/10.3311/PPme.22874

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Articles