Wall deflection in supercritical flow induces standing waves which significantly influence the flow field close to the wall. This paper analyses the flow in the converging stepped spillway, using two scale-models with different step heights and convergence angles. Results show that the height and the width of the standing wave increase with the increase of the convergence angle. Air concentration decreases while the air-water mixture velocity and residual energy head increase in the vicinity of the converging wall and gradually attain the values for the undisturbed flow outside the standing wave. Compared to the prismatic chutes of equal upstream width, converging spillways are less efficient energy dissipators. Equations for predicting the maximum flow depth and the width-averaged residual energy are proposed.

Hydraulic jumps exhibit a high degree of free-surface oscillations, triggered by intense turbulence and aeration. These processes are difficult to model numerically and are frequently investigated on a scale model. However, measuring the oscillatory characteristics of the hydraulic jump is not without issues, as the majority of available methods are not intended for tracking instantaneous depth profile or free-surface interface (FSI). Some methods are limited to a selected set of few predetermined points, and are sensitive to variations of secondary characteristics of the hydraulic jump: point gauges are sensitive to aeration rate and rate of the free-surface changes, electroconductive and optical probes require direct contact with the air-water mixture (disrupting the free surface), while the ultrasonic distance measurement accuracy is severely impacted by the shape of the free surface and aeration rate. To alleviate these issues, we propose the application of the non-intrusive method based on image processing techniques to detect the instantaneous FSI. The first step is to record the freesurface region in a series of images, with a predefined constant time shift. Subsequently, the FSI along the hydraulic jump is detected in every image. Presented method was used to reconstruct temporal evolution of the depth profile from the FSI position in the recorded images. The obtained dominant FSI oscillation frequencies along the hydraulic jump show good agreement with previous research. Results also show that the proposed approach is more robust than previously available methodsminor sensitivity to camera shooting angle, rate of the free-surface change, surface aeration variability, etc. Method is also very simple, with only a few tunable parameters, and affordable, as the only required equipment is a camera. The preprocessing and calibration steps needed to obtain reliable data for further processing are also described. Using method presented in this paper, one can gain a better understanding of the characteristics of the hydraulic jump: instantaneous and timeaveraged FSI profile, as well as the frequency spectrum of FSI variations along the hydraulic jump. This can be useful for the design of hydraulic structures, in particularthe hydraulic jump stilling basins.

Increasing renewable energy usage puts an extra pressure on decision-making in river hydropower systems. Decision support tools are used for near-future forecasting of the water available. Model-driven forecasting used for river state estimation often provides bad results due to numerous uncertainties. False inflows and poor initialization are some of the uncertainty sources. To overcome this, standard data assimilation (DA) techniques (e.g., ensemble Kalman filter) are used, which are not always applicable in real systems. This paper presents further insight into the novel, tailor-made model update algorithm based on control theory. According to water-level measurements over the system, the model is controlled and continuously updated using proportional–integrative–derivative (PID) controller(s). Implementation of the PID controllers requires the controllers’ parameters estimation (tuning). This research deals with this task by presenting sequential, multi-metric procedure, applicable for controllers’ initial tuning. The proposed tuning method is tested on the Iron Gate hydropower system in Serbia, showing satisfying results.

High-frequency oscillations and high surface aeration, induced by the strong turbulence, make water depth measurement for hydraulic jumps a persistently challenging task. The investigation of the hydraulic jump behaviour persists as an important research theme, especially with regards to the stilling basin design. Reliable knowledge of time-averaged and extreme values along a depth profile can help develop an adequate design of a stilling basin, improve safety and aid the understanding of the jump phenomenon. This paper presents an attempt of mitigating certain limitations of existing depth measurement methods by adopting a non-intrusive computer vision-based approach to measuring water depth profile of a hydraulic jump. The proposed method analyses video data in order to detect the boundary between the air-water mixture and the laboratory flume wall. This is achieved by coupling two computer vision methods: (1) analysis of the vertical image gradients, and (2) generalpurpose edge detection using a deep neural network model. While the gradient analysis technique alone can provide adequate results, its performance can be significantly improved in combination with a neural network model which incorporates a "human-like" vision in the algorithm. The model coupling reduces the likelihood of false detections and improves the overall detection accuracy. The proposed method is tested in two experiments with different degrees of jump aeration. Results show that the coupled model can reliably and accurately capture the instantaneous depth profile along the jump, with low sensitivity to image noise and flow aeration. The coupled model presented fewer false detections than the gradientbased model, and offered consistent performance in regions of high, as well as low aeration. The proposed approach allows for automated detection of the free-surface interface and expands the potential of computer vision-based measurement methods in hydraulics.

The performance of flat stilling basins can be inadequate for conditions when the tailwater depth is insufficient for hydraulic jump stabilization. In such cases, adverse-slope stilling basins can be used because they reduce the necessary tailwater depth. Sloped basins combined with smooth chutes have been the subject of many studies. However, limited research has been done for basins with stepped chutes, which are characterized by intensive flow aeration and high energy dissipation. Based on our scale-model experimental measurements of depth, velocity, and air concentration, we present a momentum-based method to characterize such hydraulic jump: the sequent depth ratio, the length of hydraulic jump roller, and energy dissipation effectiveness. The proposed method provides better agreement with experimental data when compared to existing methods and can be used for preliminary design.