Håkan Nilsson

This study employs highly resolved CFD to analyze cavitation near cavitation breakdown in a Kaplan turbine model known as U9-400. Both BEP (Best Efficiency Point) and PL (Part-Load) conditions are examined. A comparison is made with data from experiments, provided by Vattenfall, which include high-speed camera recordings of cavitation regions between the blades, and the associated efficiency loss caused by cavitation breakdown. It is shown that the numerical results successfully replicate the experimentally observed cavitation and accurately predict the efficiency reduction. Additionally, we explore the underlying causes of the efficiency reduction near cavitation breakdown, identifying it as a consequence of the diminished pressure difference between the suction and pressure sides of the runner blades.

Physics-informed neural networks (PINNs) are introduced to solve the linear wave problem described by potential flow theory. In the proposed PINN framework, both soft and hard enforcing of boundary conditions (BCs) at the bottom and sides of the wave domain are proposed. Two scenarios for solving the linear wave problem are investigated to find suitable PINN architectures. In the first scenario, the free surface wave is considered to be completely defined, and in the second scenario, the wave angular frequency is considered an unknown parameter. With a completely defined free surface wave and incorporating both the free surface and bottom BCs as soft constraints, the average velocity distribution error is less than 3%. Incorporation of a periodic BC (PBC) as a soft constraint reduces the average error to 0.10%. A hard constraint PBC gives an average error of 0.16%, with a strict representation of the PBC. This study also explores the design of trial functions to impose the kinematic bottom BC (KBBC) as a hard constraint. While these trial functions strictly satisfy the KBBC, they limit the solution space, increasing the average error up to almost 15 times. When the angular frequency of the wave is considered as an unknown parameter, to be estimated by the PINN, its value is estimated with an average error of 0.03%. Since linear wave theory underpins many wave simulation approaches, the results of this study lay the groundwork for extending the PINN framework to more complex wave models.

In an effort to make pumped hydropower storage (PHS) technology feasible for regions with a flat topography, recent research shows promising results using a contra-rotating reversible pump-turbine at low-head. In this study, the impact of dual variable speed and inlet valve control is analyzed to evaluate the effect of these three degrees of freedom (DOFs) on the system efficiency and operating range. To this end, analytical models are described to assess pump-turbine performance, conduit losses and electromechanical losses. Methodologically, optimal efficiency maps are computed for every combination of the three DOFs to evaluate individual and combined effects on the overall efficiency. Furthermore, three energy storage cycles are analyzed to further study the performance in realistic use-cases. Key conclusions include an increase in round-trip efficiency by combining variable speed ratio and inlet valve control of 5.6% and 2.0% compared with only variable speed ratio control and variable inlet valve control, respectively. Furthermore, it is shown that using only 1 DOF significantly limits the operating range, with the addition of a variable inlet valve granting a higher impact than a variable speed ratio. Combining inlet valve and speed ratio control leads not only to the highest efficiency, but also the largest operating range, with a maximum round-trip efficiency of 67.5% and an energy storage capacity of 58.6 Wh/m². The results confirm that exploiting both dual variable speed operation and inlet valve control yields the maximum efficiency and operating range, and is thus the preferred topology for contra-rotating reversible pump-turbines in low-head operation.