Hydraulic axial turbines are more frequently utilized for grid regulation purposes. Sometimes, they must be operated at speed-no-load (SNL) conditions, which is characterized for some machines by a varying number of large vortical flow structures extending from the vaneless space to the draft tube, introducing detrimental pressure pulsations throughout the turbine. A recent study shows that the vortices can be mitigated by individually controlling the guide vanes. Since optimization of the distributor layout is linked with a large degree-of-freedom, machine learning is deployed to assist in finding an optimal setup cost-effectively. A reduced numerical computational-fluid-dynamics (CFD) model is built and used to generate input for Gaussian process regression surrogate models by performing 2000 steady-state simulations with varying distributor layouts. The surrogate models suggest that the optimal layout is to open seven out of 20 guide vanes in succession while keeping the remaining ones closed. However, this configuration induces large radial forces on the runner, and after implementing some modifications by trial and error, detailed time-dependent CFD simulations show that placing 4 + 3 opened guide vanes on opposite sides of the runner axis is better; it reduces the pressure peaks corresponding to a two- and three-vortex configuration, and the maximal pressure pulsations by as much as 88% in the vaneless space compared to regular SNL operation. Meanwhile, the radial force on the runner is reduced by more than 83%, and pressure pulsations on the runner blades by more than 55%, compared to the surrogate models' optimal layout prediction.

Axial hydraulic turbines are operated more frequently at standby mode operation, also known as Speed-No-Load (SNL), to connect rapidly to the grid when required. This operation is harmful to the turbine and is sometimes characterized by large rotating vortical flow structures extending from the vaneless space to the draft tube, introducing detrimental pressure pulsations. A previous study showed that the flow structures can be mitigated with individual control of the guide vanes. The present study is a continuation that aims to find the best distributor layout for mitigation of the flow structures by introducing machine learning. A reduced CFD model, without runner blades and spiral casing, is developed to generate steady-state input data for a Gaussian process regression model. The surrogate model predicts that the best distributor layout is to open seven guide vanes out of 20 in a row while keeping the remaining ones closed. A cross-validation with a transient simulation on the reduced CFD model shows that the flow structures are mitigated, resulting in a stable and predictable flow field. The pressure pulsations in the vaneless space have been reduced by up to 94%. In addition, there is no risk for cavitation in the distributor domain by not opening all guide vanes, and the forces on the runner hub are less compared to regular SNL operation. The proposed distributor setup can potentially increase the turbine’s life span and make it better suited for grid regulation purposes.

Hydraulic turbines are operated more frequently at no-load conditions, also known as speed-no-load (SNL), to provide a spinning reserve that can rapidly connect to the electrical grid. As intermittent energy sources gain popularity, turbines will be required to provide spinning reserves more frequently. Previous studies show vortical flow structures in the vaneless space and the draft tube and rotating stall between the runner blades of certain axial turbines operating at SNL conditions. These flow phenomena are associated with pressure pulsations and torque fluctuations which put high stress on the turbine. The origin of the instabilities is not fully understood and not extensively studied. Moreover, mitigation techniques for SNL must be designed and explored to ensure the safe operation of the turbines at off-design conditions. This study presents a mitigation technique with independent control of each guide vane. The idea is to open some of the guide vanes to the best efficiency point (BEP) angle while keeping the remaining ones closed, aiming to reduce the swirl and thus avoid the instability to develop. The restriction is to have zero net torque on the shaft. Results show that the flow structures in the vaneless space can be broken down, which decreases pressure and velocity fluctuations. Furthermore, the rotating stall between the runner blades is reduced. The time-averaged flow upstream of the runner is changed while the flow below the runner remains mainly unchanged.

Global renewable energy requirements rapidly increase with the transition to a fossil-free society. As a result, intermittent energy resources, such as wind- and solar power, have become increasingly popular. However, their energy production varies over time, both in the short- and long term. Hydropower plants are therefore utilized as a regulating resource more frequently to maintain a balance between production and consumption on the electrical grid. This means that they must be operated away from the design point, also known as the best-efficiency-point (BEP), and often are operated at part-load (PL) with a lower power output. Moreover, some plants are expected to provide a spinning reserve, also referred to as speed-no-load (SNL), to respond rapidly to power shortages. During this operating condition, the turbine rotates without producing any power.

During the above mentioned off-design operating conditions, the flow rate is restricted by the closure of the guide vanes. This changes the absolute velocity of the flow and increases the swirl, which is unfavorable. The flow field can be described as chaotic, with separated regions and recirculating fluid. Shear layer formation between stagnant- and rotating flow regions can be an origin for rotating flow structures. Examples are the rotating-vortex-rope (RVR) found during PL operation and the vortical flow structures in the vaneless space during SNL operation, which can cause the flow between the runner blades to stall, also referred to as rotating stall. The flow structures are associated with pressure pulsations throughout the turbine, which puts high stress on the runner and other critical parts and shortens the turbine's lifetime.

Numerical models of hydraulic turbines are highly coveted to investigate the detrimental flow inside the hydraulic turbines' different sections at off-design operating conditions. They enable the detailed study of the flow and the origin of the instabilities. This knowledge eases the design and assessment of mitigation techniques that expand the turbines' operating range, ultimately enabling a wider implementation of intermittent energy resources on the electrical grid and a smoother transition to a fossil-free society.

This thesis presents the numerical study of the Porjus U9 model, a scaled-down version of the 10 MW prototype Kaplan turbine located along the Luleå river in northern Sweden. The distributor contains 20 guide vanes, 18 stay vanes and the runner is 6-bladed. The numerical model is a geometrical representation of the model turbine located at Vattenfall Research and Development in Älvkarleby, Sweden. The commercial software ANSYS CFX 2020 R2 is used to perform the numerical simulations.

Firstly, the draft tube cone section of the U9 model is numerically studied to investigate the sensitivity of a swirling flow to the GEKO (generalized kω) turbulence model. The GEKO model aims to consolidate different eddy viscosity turbulence models. Six free coefficients are changeable to tune the model to flow conditions and obtain results closer to an experimental reference without affecting the calibration of the turbulence model to basic flow test cases. Especially, the coefficients affecting wall-bounded flows are of interest. This study aims to analyze if the GEKO model can be used to obtain results closer to experimental measurements and better predict the swirling flow at PL operation compared to other eddy viscosity turbulence models. Results show that the near-wall- and separation coefficients predict a higher swirl and give results closer to experimentally obtained ones.

Secondly, a simplified version of the U9 model is investigated at BEP and PL operating conditions and includes one distributor passage with periodic boundary conditions, the runner and the draft tube. The flow is assumed axisymmetric upstream of the runner, hence the single distributor passage. Previous studies of hydraulic turbines operating at PL show difficulties predicting the flow's tangential velocity component as it is often under predicted. Therefore, a parametric analysis is performed to investigate which parameters affect the prediction of the tangential velocity in the runner domain. Results show that the model predicts the flow relatively well at BEP but has problems at PL; the axial velocity is overpredicted while the tangential is underpredicted. Moreover, the torque is overpredicted. The root cause for the deviation is an underestimation of the head losses. Another contributing reason is that the runner extracts too much swirl from the flow, hence the low tangential velocity and the high torque. Sensitive parameters are the blade clearance, blade angle and mass flow.

Finally, the full version of the U9 model is analyzed at SNL operation, including the spiral casing, full distributor, runner and draft tube. During this operating condition, the flow is not axisymmetric; vortical flow structures extend from the vaneless space to the draft tube and the flow stalls between the runner blades. A mitigation technique with independent control of each guide vane is presented and implemented in the model. The idea is to open some of the guidevanes to BEP angle while keeping the remaining ones closed. The aim is to reduce the swirl and prevent the vortical flow structures from developing. Results show that the flow structures are broken down upstream the runner and the rotating stall between the runner blades is reduced, which decreases the pressure- and velocity fluctuations. The flow down stream the runner remains mainly unchanged.

Numerical simulations of axial hydraulic turbines away from the best efficiency point are challenging. Previous studies especially show difficulties predicting the tangential velocity at Part Load (PL) operating conditions, where the swirl is high, in comparison to experiments. This is a reoccurring problem, and it is essential to understand, as the high tangential velocity is a fundamental characteristic of the flow in hydraulic turbines and is directly related to the swirling flow stability and the turbine's power output. The objective of this study is to numerically investigate and understand the origin of the tangential velocity deviation from experimental results by performing simulations with the finite volume method of an axial turbine operated at PL. A parametric study is performed to address the abovementioned. Specifically, the effects of the blade clearance, blade angle, flow rate, and different turbulence models are studied on this issue. Results are analyzed by comparing the predicted axial and tangential velocity profiles and torque to experimentally obtained values. Primarily the runner inter-blades flow is studied as there is a knowledge gap. In addition, the physical phenomena responsible for head losses are studied in detail. Results show that the model can predict the flow relatively well at optimal flow conditions with low swirl but has problems at part load; the tangential velocity between the runner blades is underestimated by ∼20%. The undervalued head losses are the root cause. They result in an overestimated torque and an underestimated tangential velocity as the runner extracts too much energy from the fluid. A small modeling error of 0.5° in the blade angle and a change of 3% in the flow rate significantly affect the tangential velocity and torque prediction. The studied parameters must be considered carefully when building a numerical model.

Accurate numerical models for hydraulic turbine applications are highly coveted. They must be able to correctly capture the swirling flow found at off-design operating conditions in the turbine draft tube. The GEKO model is a relatively fast and flexible eddy viscosity turbulence model with adjustable coefficients to tune the model to different flow scenarios. In this study, the GEKO model is tested on a swirling flow inside a diffusor similar to the flow conditions found at part-load operation of a propeller turbine. The diffusor investigated corresponds to the Porjus U9 draft tube cone section, including the runner cone. Results showed that the near-wall coefficient, with a value of 2, increased the wall shear stress and moved the separation point from the runner cone further downstream. Moreover, with a value of 0.7, the separation coefficient increased the eddy viscosity, which also moved the separation point from the runner cone further downstream. Both coefficients gave velocity profiles closer to experimental values and increased the swirl number at the outlet of the diffusor by up to 36.9 % compared to the GEKO default model. Overall, the near-wall coefficient with a value of 2 gave the best results. The GEKO model provides an opportunity to tweak numerical models to swirling flow.