LAPSE:2023.20657v1
Published Article
LAPSE:2023.20657v1
Simulating Fish Motion through a Diagonal Reversible Turbine
Phoevos (Foivos) Koukouvinis, John Anagnostopoulos
March 20, 2023
Abstract
Utilization of unharnessed hydro-power necessitates designing fish-friendly hydraulic machinery. Towards this effort, the present work investigates various methods for tracking fish motion, ranging from particle tracking methods to accurate, but computationally expensive, body tracking methods, such as immersed boundaries and overset meshes. Moreover, a novel uncoupled 6-Degree of Freedom tracking technique is proposed, based on an approximated pressure field around the tracked body of interest, using steady-state flow field data, and including collision detection to walls. The proposed method shows promising results in terms of accuracy, being comparable to the more computationally expensive fully coupled methods at a tiny fraction of the execution time. The new method reveals location of fish−blade impact, as well as statistics of forces, pressure and flow shear that a passing fish is subjected to, both in the normal and reverse operation of the turbine. The low computational cost of the proposed method renders it attractive for optimization studies.
Keywords
body tracking, collision detection, fish-friendly hydropower, fluid–structure interaction
Suggested Citation
Koukouvinis P(, Anagnostopoulos J. Simulating Fish Motion through a Diagonal Reversible Turbine. (2023). LAPSE:2023.20657v1
Author Affiliations
Koukouvinis P(: Laboratory of Hydraulic Turbomachines, Department of Mechanical Engineering, National Technical University of Athens, Heroon Polytechniou 9, Zografou, 157 80 Athens, Greece
Anagnostopoulos J: Laboratory of Hydraulic Turbomachines, Department of Mechanical Engineering, National Technical University of Athens, Heroon Polytechniou 9, Zografou, 157 80 Athens, Greece
Journal Name
Energies
Volume
16
Issue
2
First Page
810
Year
2023
Publication Date
2023-01-10
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en16020810, Publication Type: Journal Article
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LAPSE:2023.20657v1
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https://doi.org/10.3390/en16020810
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