Application of the Smoothed Particle Hydrodynamics (SPH) in solving the Shallow Water Equations (SWE) holds a great potential, particularly in the field of ocean numerical simulations, but the issues of the SWE-SPH method related to open boundaries remain unresolved. This study addresses the problem of reflection at an open boundary in previous SWE-SPH models, and describes a new method for treating non-reflective boundaries based on the Flather condition of gravity wave open boundaries. We evaluate the performance of this new boundary condition using three classical numerical simulation cases－steady flow over a bump, wave propagation of sea level perturbations in a flat-bottom channel with two open ends, and flow around a circular cylinder－and compare it with the conditions in the original method. Results indicate that for an open boundary, the new condition reduces its reflections effectively, allows propagating disturbances to cross it outward, and facilitates implementation of its external flow conditions specified. We have applied the improved SWE-SPH model to simulations of the Okushiri tsunami, and compared the results with those of the finite volume method. This proves it is applicable and effective in handling complicated depth and trans-critical flow simulations, thus expanding the scope of ocean numerical simulations.

The application of rock-filled concrete (RFC) technology in high dams and other large-scale projects is on the rise. Given the elevated seismic fortification intensity at certain project sites is relatively high, there emerges a pressing need for enhanced structural design consideration for RFC dams, necessitating comprehensive research on the dynamic performance of RFC. This study employs a scaled-down testing approach to conduct a series of uniaxial and triaxial compression tests on RFC specimens subjected to varying confining pressures and strain rates. The results indicate that the uniaxial strength of RFC is positively correlated with the strain rate, while it will decline to some extent as the size of coarse aggregates or the dimension of the specimen increases. Confining pressure significantly increases the dynamic strength of RFC. Under the same confining pressure, specimens containing larger aggregates demonstrate lower uniaxial strength but exhibit higher triaxial strength. This observation suggests the mesoscopic characteristics of the self-sustaining skeleton formed by coarse aggregates play a crucial role in the strength of RFC. Based on the test results, a failure criterion is established using the octahedral stress, thereby providing experimental evidence for the seismic design of RFC structures.

Reinforced concrete liner serves as a critical component in the structural integrity of artificially excavated underground gas storage caverns. Its cracking and deformation characteristics in response to the high internal gas pressure significantly affect the design and operational performance of the cavern’s sealing structure. This study adopts a fixed crack model for concrete simulations to evaluate the design of the reinforced concrete liner for a JQ high-pressure gas storage cavern in Gansu province. We first validate it against a four-point bending beam tests in literature, then conduct a series of numerical simulations of the non-linear fractures in the cavern liner, focusing on the progressive development of multiple cracks and their opening widths in response to gradually increase of gas pressure in the cavern. An analysis is also made on the effect of omitting the reinforcing measure and the effect of accounting for the hysteresis effect of concrete cracking on the liner￠s fracture response during the fluctuating cycles of internal pressure. The results show that the liner’s progressive cracking is characterized by a multi-crack distribution, predominantly distributed radially and extending through the entire liner thickness. Based on the design parameters for concrete and reinforcement, we attain the liner’s maximum crack opening controlled below 1 mm under an internal pressure of 18 MPa, and demonstrated the cycling of pressurization and depressurization in the cavern does not significantly deteriorate the crack opening in a reinforced concrete liner. The results deepen the understanding of liner cracking for the design of support structures and sealing layers of gas storage caverns, and help predict cavern liner cracking for service performance.

This study develops a new method for in-depth analysis of the deformation mechanism during the construction and operation of rockfill dams, using a hierarchical multiscale computational approach that couples the Material Point Method (MPM) with the Discrete Element Method (DEM). As a critical type of structure in hydraulic engineering, a rockfill dam features deformation that involves complicated interaction between its particle-scale behavior and the overall response of its body; Understanding the mechanism of its micromechanical deformation is crucial for safety assessment, design, and construction. Aiming at a comprehensive capture of the deformation characteristics of a rockfill dam, we first discretize its body into multiple representative volume elements (RVEs), then simulate its behavior at the particle scale and capture variations in local stress and particle contact force in its body. For the different stages of dam construction, analysis of the spatial distribution patterns of stress and deformation reveals a significant impact of reservoir water level changes on its deviatoric stress and particle contact behavior. In the completion stage, the maximum settlement feature points exhibit significant contact anisotropy, with denser force chains in the vertical direction, indicating a higher efficiency of stress transfer in this direction. As the water level rises, significant changes occur in the contact state and force chain distribution in the dam body, resulting in an increased horizontal anisotropy at the maximum settlement point, thereby enhancing the dam's resistance to water pressure. During the normal reservoir stage, particle contact on the upstream side of the main rockfill zone weakens, and the coordination number decreases. The multiscale method developed in this study overcomes the limitations of traditional analytical approaches, providing a new quantitative framework for the deformation prediction, long-term stability evaluation, and design optimization of rockfill dams.

The prediction of river runoff in a small or medium-sized catchment is constrained by the spatial distribution and density of its rain gauges and record length historical rainfall data. To enhance the accuracy of flash flood early warning and forecasting for such catchments, this study redefines the data structure of an hourly rainfall-runoff model based on the graph theory and the 2000-2014 data of the Shaxi River basin. We use graph neural networks (GNNs) to construct an end-to-end dynamic mapping model for its rainfall-runoff data, and predict its future hydrographs at different forecast periods, using Graph Convolutional Neural Network (GCN), Graph Attention Network (GAT), and Chebyshev Graph Neural Network (Chebnet) models. Mean Absolute Error (EMAE) is used as an evaluation indicator to compare the predictions for the next two hours with those by the Long Short-Term Memory (LSTM) models, Gated Recurrent Unit (GRU), and Artificial Neural Networks (ANNs). The results indicate that for this basin, the Chebnet and GAT models are superior in nonlinear data fitting capability for rainfall-runoff predictions at the forecast periods of one and two hours, improving prediction accuracy by 37.3% to 64.7% compared to LSTM and GRU. The Chebnet model exhibits stable performance in its runoff prediction of the next 15 hours, significantly reducing the impact of timeliness while improving accuracy and applicability. This study has achieved highly reliable predictions of river runoff, useful for early flood warning in small and medium-sized catchments.

Water dams are prone to various types of damage under the coupled effects of external erosion and complex loads, particularly their underwater structural damage, which is often difficult to detect and requires timely monitoring to mitigate safety hazards. Previous deep learning-based methods for such damage detection suffer from limitations such as high computational demands and significant manual intervention, while commonly-used detecting devices tend to possess inadequate computational capabilities, leading to certain incompatibility. This paper presents a new intelligent detection model based on the YOLOv7 algorithm for multi-class underwater damage to water dams under the conditions of low computational capabilities. This model enhances the detection accuracy by integrating three intelligent modules－deformable convolution, SE attention mechanism, and MPDIoU loss function－and provides strong robustness for application in complicated underwater environments. It achieves lightweight operation through a structured pruning strategy at a ratio of 0.4, and reduces significantly computational power requirements. Analysis of engineering examples and comparison with the previous algorithms in literature shows that its floating-point computation and the number of its parameters are reduced by 48% and 61% respectively. It improves detection accuracy for exposed reinforcement bars and voids significantly by 18.7% and 11.9% respectively, and enhances the average detection accuracy for various types of damage by 8.3%, achieving the goal of accurate detection under the conditions of low computational resources.

As key water resource projects, reservoirs and their efficient operation are crucial to the society. However, most reservoirs, domestic or international, have been formulating the operating rules based on historical statistical data, and the static rules for planning and design limit their capability of proactive responding to floods and droughts. Recent advancements in meteorological and hydrological forecasting have made forecast informed reservoir operation (FIRO) a research hotspot. This paper discusses the main factors that impact the accuracy of numerical weather forecasts, commonly used hydrological models, and rapidly developing artificial intelligence forecasting techniques. The review also covers the FIRO methods and their applications. Finally, we suggest the comprehensive use of various meteorological and hydrological forecasting products to achieve efficient water resource utilization－including global navigation satellite system (GNSS)-based precipitable water vapor, the development of FIRO methods focusing on the vapor-precipitation-runoff three lines of defense, and the construction of deep reinforcement learning operation models that account for the multi-blocking effects of cascade reservoirs.

Hydraulic machinery operating on sediment-laden rivers suffers from sediment erosion, which causes the deformation of flow passages, reduction of hydraulic efficiency, and even shutdown of the unit. By taking advantage of a rotary jet device, this study conducts a sediment abrasion and erosion experiment on materials used for hydraulic turbines under different impact velocities and impact angles, and investigated the measured sediment abrasion characteristics using the weight loss method along with microstructure analysis. The results indicated that for three types of materials, the weight loss and the facilitation of cavitation on sediment abrasion take an exponential increase with impact velocity, along with the increase in the surface roughness, the size of cutting scratches as well as cavitation pits. For all materials, the impact angles that maximize weight loss are roughly 40° and 45° for abrasion and erosion respectively. The vertical impact effect and horizontal removal of particles match best at 45° impact angle, and the particle impact forms disc-shaped pits of which the surface lip edges are removed by horizontal cutting, resulting in significant weight loss. At 45° impact angle, cavitation induced by the jet nappe is the most severe, and the combined abrasion-erosion damage causes relatively deep pits on the material surface, which leads to the weight loss twice of that in abrasion test.

This study presents an improved YOLOv8n-based detection method to address the issue of false detections of dam cracks that is caused by low-quality surveillance images, limited effective samples, and interference from complex backgrounds. This model is trained using a dataset comprising 193 real-world crack images featuring complex engineering backgrounds, and enhanced by modifying the mosaic data augmentation mechanism and incorporating negative sample training targeted at the objects that were often falsely detected. Numerical experiments demonstrate that under small-sample training conditions, the YOLOv8n model achieves a mean Average Precision (mAP) of 89.2%, meeting the requirements of general engineering applications. After negative sample training, the mAP increases to 92.5%, and the false detection rate is reduced by 10.1%, providing an effective solution to the false detection problem in complex background scenarios. Our findings indicate that the YOLOv8n model is well-suited for dam surveillance images of suboptimal quality, and that the negative sample training strategy significantly improves detection accuracy. This approach offers a novel solution to crack identification in hydraulic projects, practically significant for engineering applications.

Missing value processing is an important foundation for analysis of dam safety monitoring data. Traditional methods for handling the missing values of a dam often use a single type of machine learning models for prediction and interpolation, ineffective in integrating the advantages of multiple types of machine learning models. This article integrates multiple classic machine learning and deep learning algorithms into a strong learner within the framework of ensemble learning. To address the issue of weight allocation to each model, we develop a new critic stacking (CS) weight allocation method so that we can construct a dam monitoring data interpolation hybrid model based on CS ensemble learning. The results show that compared to single base learners and traditional Stacking ensemble models, this CRITIC-Stacking ensemble learning method reduces the RMSE index by an average of 72.7% and 58%. This indicates that the method can fully leverage the predictive advantages of various machine learning models, and the improvement of weight allocation can also improve the predictive accuracy of ensemble learning models, thus providing a new solution for handling missing values in dam monitoring data and constructing prediction models.

The formation of cascaded hybrid pumped storage through expanding pumped storage units is an effective means to enhance the regulating capacity of traditional hydropower and promote new energy consumption. To consider the correlation and uncertainty of wind and solar power outputs, this paper develops a short-term optimal dispatch model for the operation of a cascaded hybrid pumped storage-wind-solar complementary system, with the Copula function used to construct scenarios for the two outputs combined. Aimed at maximizing the expected return of this system, this model raises the level of large-scale wind and solar grid-connected consumption through introducing a penalty cost for power abandonment. In addition to the conventional hydropower constraints, it accounts for the more complicated hydropower-electricity coupling relationship of the hybrid storage. With the unit as a scheduled object, various new constraints are modeled including the number of fluctuation-regulating runs for a pumped storage unit, the operating states of the hybrid storage station and unit mutual exclusion, and switching between a unit’s pumping and generating conditions. To solve the dispatch model, we construct a Mixed-Integer Linear Programming model by adding new 0-1 variables and auxiliary variables, and use the Java-language Cplex solver. Application in a case study of the Wujiang River cascaded hydropower stations and related wind and solar energy demonstrates that our new dispatch model fully utilizes the flexible regulation capability of the hybrid storage stations, and it succeeds in increasing the utilization rate of the transmission channel, reducing power abandonment, and further enhancing the level of wind and solar energy consumption.

Spiral vortex ropes in the draft tube under certain partial load conditions could be frequently observed in the model tests of Francis turbines. In addition to typical vortex rope rotation frequencies fv, pressure signals recorded at the draft tube wall monitor points often exhibit the rotational frequency fn and its higher-order harmonics (1 ~ 5)fn as distinct spectral components. Under certain operating conditions, these special frequency components show significant amplitudes, yet their underlying generation mechanisms remain controversial. This study examines the internal flow characteristics of a Francis turbine operating under typical vortex rope conditions. By combining the data from high-speed imaging with pressure fluctuation measurements, this study presents a systematic analysis on the effects of unit discharge, unit speed, and cavitation number on the vortex rope structure and pressure pulsation characteristics, focusing on analyzing the possible sources of the harmonics (1 ~ 5)fn. The results indicate that unit discharge significantly influences the amplitude of the vortex rope rotation at fv when the spiral vortex rope is visible, pressure pulsation amplitudes at fv increase substantially. In contrast, weaker effects of cavitation number and unit speed are cast on the amplitude at fv. Additionally, under certain specific conditions, a slender and stable cavitating spiral vortex rope forms in the draft tube, where the amplitude at fn sharply increases. This phenomenon is believed to be closely related to hydraulic system resonance triggered by this cavitating rope. The special harmonics at (1 ~ 5)fn are likely attributed to the elliptical cross-section of the vortex rope and its self-rotation, which is characterized by frequencies close to (1 ~ 5)fn.

Ecological operation of hydropower stations is an important means to achieve a harmony between hydropower resource utilization and ecological environment protection. As more new energy bases are built, the joint operation of large-scale hydro-photovoltaic will significantly change the operations of hydropower stations, potentially affecting their ecological operation in ecological periods. This paper develops a multi-objective, two-layer nested ecological operation model for cascade hydropower stations, and explores the impact of such joint operation on the ecological operation of the stations. In this model, the upper-level simulates the dispatching process of hydropower stations for multi-day continuous water level rising, while the lower-level simulates the intra-day dispatching process considering photovoltaic integration. We have applied it as a case study to the Xiluodu-Xiangjiaba cascade hydropower stations located on the lower Jinsha River mainstream. Its simulations show that in different typical hydrological years, the joint operation does not significantly affect the multi-day rising required by drift eggs-producing fish species, and it can achieve more than two effective water rising events, each lasting up to 6 days. Compared with single hydropower generation, it can reduce outflow fluctuations by 8.9% to 28.3%. The findings help formulate ecological regulation and optimization schemes for cascade hydropower stations and integrated new energy sources.

Satellite altimetry data offer a promising approach to the hydrological study of data-scarce regions, but the long revisit cycles limit its capability to collect daily data and its applicability to hydrological modeling and forecasting. By assimilating altimetry data into the Xin’anjiang hydrological model, this study presents a new high-accuracy, low-cost method for estimating the daily flow at river cross sections. We first retrieve the river’s water levels using the Sentinel-3A altimetry data and evaluate them against concurrent in-situ data, then integrate the empirical stage-discharge relationship to constrain the Xin’anjiang model. A case study of the Wujiang River basin demonstrates a high correlation coefficient (0.94) exists between the satellite-based water levels and their in-situ measurements. Under the constraint of altimetry data, the Xin’anjiang model has achieved the Kling-Gupta Efficiency values of 0.87 and 0.69 for calibration and validation, respectively. The Xin’anjiang model accuracy for high flow simulations constrained by altimetry water levels is considerably higher than that constrained by in-situ observed discharges. This study offers a new approach to help flood and drought disaster control and water resources management in data-scarce regions.

To study the cavitation characteristics of a centrifugal pump under the condition of large flow rates with a head decrease by 3%, the numerical simulation result of flow characteristics and pressure pulsation in the impeller section was discussed, and the reason of pressure on the blades fluctuates was analyzed. The results show that under cavitation, significant differences occur in the vortex structure in the impeller channel, especially obvious flow separation at the diaphragm, and an increase in the flow rate reduces the nonuniformity of the impeller channel flow. Under different cavitation conditions, pressure pulsation at the impeller outlet is dominated by the blade frequency, and its low-frequency and wide-frequency components vary with an increasing flow rate. Under the rated working conditions, pressure pulsations in the low band of 0-290 Hz are significant at each monitoring point. An increase in the flow significantly inhibits the low frequency and broadband pulsations, and fluctuations in other frequencies such as double and triple blade passing frequencies also weaken with the increasing flow. Through a certain orthogonal decomposition of the blade load, the dynamic and static interference between the tongue and the impeller can be considered the main cause of large pressure fluctuations on the blades, and vortex shedding in the flow channel is also a factor that strengthens the pulsations. An in-depth analysis of the characteristics of flows in the impeller section of a centrifugal pump under large flow conditions is provided, which is useful for improvement and optimization of its structure.

Aiming at the issue of the random motion characteristics of water droplets in a wind field, we formulate a hypothesis that the steady-state deformation of water droplets is ellipsoidal, and uses white noise to describe the randomness of the windward area caused by changes in relative wind speed and water droplet motion posture. A stochastic differential equation is worked out for the motion of ellipsoidal water droplets; their motion shape and drift distance are tested to verify the correctness of this new model. We apply it to calculations of the dispersion coefficients of water droplets under different conditions. The results show that for ellipsoidal droplets in motion, their random effects－produced by the random forces of a large number of air molecules and the changes in their windward area－are directly proportional to their Froude number.

During the operation of a concrete dam, various uncertainties－such as sudden events, natural disasters, and changes in human management－are possible to impose an impact on it, potentially deviating its structure deformation from the conventional patterns. An accurate identification of such changes is crucial for raising the level of concrete dam warning and forecasting. This paper presents an intelligent method for identifying dam deformation under uncertainties. First, we use a spatial clustering method to categorize measurement points that are located in different regions of the concrete dam structure but share certain similarity. Then, a fuzzy clustering (Gath-Geva) algorithm is used to segment a multivariate time series into different phases, allowing its data points to belong to multiple periods based on the membership degree, to measure the homogeneity of segments and detect changes in its hidden structure. Last, we use a fuzzy decision algorithm based on the cluster compatibility criteria to determine the number of segments required, and adopts the principal component analysis (PCA) to identify the number of principal components, further improving the accuracy of the Gath-Geva algorithm. This intelligent method has been applied in a case study of a concrete arch dam structure to identify the changes hidden in the time series of its displacement measurements. Comparison of its results with those of single-period data shows that it is effective in extracting sudden anomalous changes during the operational phase of the dam, and that it is a valuable approach for assessing the operational conditions of concrete dams.

Cracking, as one of the most common defects in concrete dams, weakens the integrity and durability of dam structures; crack detection has been a crucial task in the operation and maintenance management of concrete dams. Aimed at the drawbacks of traditional image-processing techniques in crack detection－such as substantial manual intervention and limited generalization ability, this paper presents a semantic segmentation model of dam cracks that incorporates multi-scale features and attention mechanisms. This model uses ResNet-50 as its backbone network for integrating the Path Aggregation Network to recycle shallow features, and makes use of the mechanisms of channel attention and spatial attention. These mechanisms enhance the model's ability to identify critical features, thus effectively improving its segmentation accuracy. Then, based on its semantic segmentation results, the digital image technology is adopted to quantify the geometric characteristics of cracks, including area, length, average width, and maximum width. Tests on a crack image dataset show this new model achieves a crack segmentation Intersection over Union of 82.02% and an F1 score of 90.12%; Quantification results of geometric characteristics exhibit an excellent agreement with the real values and a satisfactory accuracy. Thus, our method demonstrates significant potential for application in crack detection and geometric characteristics quantification for concrete dams.

During hydraulic transition, the highest water level in a pump station forebay determines its design top elevation, and the lowest determines its design bottom elevation. If the designs are not correct, it will overflow or be empty. Determining its extreme water levels is crucial to ensuring the station’s safe operation for the water supply project. Based on the KBM method, this paper derives explicit formulas for calculating the forebay’s extreme water levels through solving a nonlinear dynamic equation that describes the variations in its water level in a long-distance water supply system. We verify the accuracy of these formulas using numerical calculations, and conduct a sensitivity analysis. The results show that relative to the MOC numerical simulations, the error is less than 1.0% for the highest level formula, and less than 2.5% for the lowest level formula. These formulas have simple forms and are convenient for engineering design.

Focusing on the application of forecast-based scheduling in optimal decision-making for complex urban water supply network systems, this study formulates a conceptual framework for integrating hydrological and rainfall forecast information. And we develop an optimal decision-making model for complex urban water supply networks, targeting at dual objectives－to maximize reservoir group safety and economic efficiency, and considering holistically the practical constraints, such as reservoir structural integrity, downstream flood control requirements, water treatment plant intake capacity, maximum allowable pipeline flow rates, and urban water supply reliability. This model has achieved a success in application to Ningbo's municipal water supply network system, validating its operational effectiveness. This case study demonstrates it can effectively adjust scheduling strategies and coordinate reservoir operations based on the hydrological conditions forecasted prior to flood events. It generates scheduling schemes that maintain a high operational safety level (100% water supply guarantee rate) while achieving an water resource utilization rate of 41.3% in flood periods, meeting the design requirements completely. This study helps design and optimize the complex urban water supply network systems.