OpenFOAM for Francis turbine transients
Journal article, 2021

The flexibility and fast responsiveness of hydropower systems make them a reliable solution to overcome the intermittency of renewable energy resources and balance the electrical grid. Therefore, investigating the complex flow fields during such operation is essential to increase the reliability and lifetime of future hydropower systems. The current article concerns the utilization of OpenFOAM for the numerical study of Francis turbines during transient load change operations. The details of employed models and numerical schemes are thoroughly explained. The Laplacian smoothing algorithm is applied for the deformation of the guide vane domain. The impact of different mesh diffusivity parameters on both load rejection and acceptance operations is studied. It is shown that general slip boundary conditions cannot be used for slipping points on the guide vane upper and lower surfaces. Instead, different alternatives are introduced and compared. The developed framework is tested on a high-head Francis turbine. Different transient operations are simulated and results are compared to the experimental data. It is shown that OpenFOAM can be employed as a trustworthy CFD solver for numerical investigation of Francis turbines transient operations.

OpenFOAM

Transient operations

Mesh motion

Slip condition

Francis turbine

Author

Saeed Salehi

Chalmers, Mechanics and Maritime Sciences (M2), Fluid Dynamics

Håkan Nilsson

Chalmers, Mechanics and Maritime Sciences (M2), Fluid Dynamics

OpenFOAM® Journal

2753-8168 (ISSN)

Vol. 1 47-61

Unsteady flow and cavitation during off-design and transients in water turbines

Svenskt Vattenkraftcentrum (2018-2022), 2021-10-01 -- 2022-12-31.

Energiforsk AB (VKU14164), 2021-10-01 -- 2022-12-31.

Chalmers, 2021-10-01 -- 2022-12-31.

Subject Categories

Energy Engineering

Fluid Mechanics and Acoustics

Areas of Advance

Energy

DOI

10.51560/ofj.v1.26

Related datasets

Replication Data for: OpenFOAM® FOR FRANCIS TURBINE TRANSIENTS [dataset]

DOI: 10.7910/dvn/31jgom

More information

Latest update

9/25/2023