A stilling basin is a critical hydraulic structure designed to dissipate excess energy from high-velocity flow exiting a spillway, preventing erosion to downstream channels. Despite its significant role in dam safety, lining damage of stilling basins occurs frequently, due to gaps and offsets between slabs and undersized underdrains. This study employs a CFD approach to examine how these factors affect flow dynamics and uplift forces. Different scenarios are examined, combining varying gap widths, offset heights, and vent configurations, under three flow rates for each. Results reveal that gap width minimally influences uplift forces. Offset heights considerably enhance upliftpressures, with a 10% increase when offset height doubles from 1.5 cm to 3 cm. Venting reduces uplift pressures effectively by facilitating water escape beneath slabs, with larger vent sizes yielding negative uplift pressures. However, venting intensifies pressure fluctuations, with the pressure coefficient rising substantially, particularly at higher flow rates. This study contributes to a deeper understanding of damage mechanisms and offers valuable insights for upgrading and rehabilitating such structures.

Structural integrity for energy dissipation of spillways is essential to maintain a high dam safety level. In some Swedish low-head facilities (< 15 m), damages appear also where flow velocities are expected to be too low to cause cavitation. To better understand the reason for damages a study including desktop survey, hydraulic scale model tests and numerical modelling with CFD has been initiated at Vattenfall R&D. Most common damages found in an inventory were erosion of bedrock in stilling basins, often in or adjacent to weak zones (clay filled cracks or crushed rock). Sometimes the damages progressed under concrete structures. A flow velocity above 15 m/s atthe end of the crest or at the intersection with the downstream water level seems to be a limit for when damages occur in the studied spillways. Scale model tests (1:17.5) of a case with damages has been performed to study the energy dissipation and potential causes for initiation of the damages. A CFD-model (OpenFOAM) of the same case, in model scale, has been set up to make a comparison. Measurements of flow velocities give that the tested scenario could cause flow velocities above 15 m/s in prototype scale. The pressures given by the CFD-model where in line with the ones from the model tests. From the model test the pressures, near the transition from the crest to the stilling basin, were low but not sub atmospheric. In the CFD-model the indicated risk for cavitation was high and in the same area as the real damages. Excluded from both model tests and CFD-model is the fact that cracks where visible in the real structure. Therefore, it cannot be ruled out that the initiation of damages also was stagnation pressure in the cracks. The study will continue with further tests with more pressure gauges and simulations. and the validation of the numerical model and conclusions presented are therefore preliminary.

Understanding cylinder-induced wake is pivotal in fluid dynamics, providing essential insights for the design and analysis of various structures, including offshore platforms, bridges, and buildings. To achieve fast and accurate modeling, this study introduces a novel reduced-order model (ROM) utilizing dynamic mode decomposition (DMD) and an advanced deep learning framework, specifically an attention-enhanced convolutional neural network-long short-term memory networks model (CNN-LSTM), for predicting cylinder-induced unsteady wake flows. The DMD efficiently simplifies complex fluid systems while retaining key dynamics, thus significantly saving computational costs. By leveraging its combined strengths, the CNN-LSTM with an attention mechanism effectively captures complex spatiotemporal features. The resulting ROM accurately reproduces the wake processes around a cylinder (group), demonstrating high consistency with computational fluid dynamics (CFD) solutions (coefficient of determination > 0.98), and showcases satisfactory resilience to a (Gaussian) noise level of up to 25 %. This study contributes a robust ROM capable of handling spatiotemporal dynamics, facilitating swift prediction of future outcomes using historical data, which is particularly critical for efficient real-time analysis and informed decision-making in dynamic settings, e.g., digital twins and predictive maintenance.

The soil water retention curve (SWRC) is a vital soil property used to evaluate the soil’s water holding capacity, a critical factor in various applications such as determining soil water availability for plants, soil conservation and management, climate change adaptation, and mitigation of flood risks. Estimating SWRC directly in the field and laboratory is a time-consuming and laborious process and requires numerous instruments and measurements at a specific location. In this context, various estimation approaches have been developed, including pedotransfer functions (PTFs), over the past three decades to estimate soil water retention and its associated properties. Despite the efficiencies, PTFs and semi-physical approach-based models often have several limitations, particularly in the dry range of the SWRC. PTFs-based modeling has become a key research topic due to readily available soil data and cost-effective methods for deriving essential soil parameters, which enable more efficient decision-making in sustainable land-use management. Therefore, advancement and adjustment are necessary for reliable estimations of the SWRC from readily available data. This article reviews the evaluation of the current and past PTFs for estimating the SWRC. This study aims to evaluate PTF techniques and semi-physical approaches based on soil texture, bulk density, porosity, and other related factors. Additionally, it also assesses the performance and limitations of various common semi-physical models proposed and developed by Arya and Paris, Haverkamp and Parlange, the Modified Kovács model by Aubertin et al., Chang and Cheng, Meskini-Vishkaee et al., Vidler et al., and Zhai et al. This assessment will be effective for researchers in this field and provide valuable insight into the importance of new PTFs for modeling SWRC.