CFD Investigation of a high head Francis turbine at speed no-load using advanced URANS models

Decaix, Jean (School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO // University of Applied Sciences Western Switzerland) ; Hasmatuchi, Vlad (School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO // University of Applied Sciences Western Switzerland) ; Titzschkau, Maximilian (Kraftwerke Oberhasli (KWO), Grimsel Hydro, Innertkirchen, Switzerland) ; Münch-Alligné, Cécile (School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO // University of Applied Sciences Western Switzerland)

Due to the integration of new renewable energies, the electrical grid undergoes instabilities. Hydroelectric power plants are key players for grid control thanks to pumped storage power plants. However, this objective requires extending the operating range of the machines and increasing the number of start-up, stand-by, and shut-down procedures, which reduces the lifespan of the machines. CFD based on standard URANS turbulence modeling is currently able to predict accurately the performances of the hydraulic turbines for operating points close to the Best Efficiency Point (BEP). However, far from the BEP, the standard URANS approach is less efficient to capture the dynamics of 3D flows. The current study focuses on a hydraulic turbine, which has been investigated at the BEP and at the Speed-No-Load (SNL) operating conditions. Several “advanced” URANS models such as the Scale-Adaptive Simulation (SAS) SST k − ω and the BSL- EARSM have been considered and compared with the SST k − ω model. The main conclusion of this study is that, at the SNL operating condition, the prediction of the topology and the dynamics of the flow on the suction side of the runner blade channels close to the trailing edge are influenced by the turbulence model.


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEI-VS
Institute:
Institut Systèmes industriels
Subject(s):
Ingénierie
Date:
2018-12
Pagination:
26 p.
Published in:
Applied Sciences
Numeration (vol. no.):
2018, vol. 8, no. 12, article no 2505
DOI:
ISSN:
2076-3417
Appears in Collection:



 Record created 2018-12-18, last modified 2018-12-20

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