Search Results (125)

Underwater inspection images of hydraulic structures often suffer from haze, severe color distortion, low contrast, and blurred textures, impairing the accuracy of automated crack, spalling, and corrosion detection. However, many existing enhancement methods fail to preserve structural details and suppress noise in turbid environments. To address these limitations, we propose a compact image enhancement framework called Wavelet Fusion with Sobel-based Weighting (WWSF). This method first corrects global color and luminance distributions using multiscale Retinex and gamma mapping, followed by local contrast enhancement via CLAHE in the L channel of the CIELAB color space. Two preliminarily corrected images are decomposed using discrete wavelet transform (DWT); low-frequency bands are fused based on maximum energy, while high-frequency bands are adaptively weighted by Sobel edge energy to highlight structural features and suppress background noise. The enhanced image is reconstructed via inverse DWT. Experiments on real-world sluice gate datasets demonstrate that WWSF outperforms six state-of-the-art methods, achieving the highest scores on UIQM and AG while remaining competitive on entropy (EN). Moreover, the method retains strong robustness under high turbidity conditions (T ≥ 35 NTU), producing sharper edges, more faithful color representation, and improved texture clarity. These results indicate that WWSF is an effective preprocessing tool for downstream tasks such as segmentation, defect classification, and condition assessment of hydraulic infrastructure in complex underwater environments. Full article

In response to the challenges associated with weld treatment during the on-site corrosion protection of hydraulic steel gates, this paper proposes a method utilizing a magnetic adsorption climbing robot to perform corrosion protection operations. Firstly, a magnetic adsorption climbing robot with a multi-wheel independent drive configuration is proposed as a mobile platform. The robot body consists of six joint modules, with the two middle joints featuring adjustable suspension. The joints are connected in series via an EtherCAT bus communication system. Secondly, the kinematic model of the climbing robot is analyzed and a PID trajectory tracking control method is designed, based on the kinematic model and trajectory deviation information collected by the vision system. Subsequently, the proposed kinematic model and trajectory tracking control method are validated through Python3 simulation and actual operation tests on a curved trajectory, demonstrating the rationality of the designed PID controller and control parameters. Finally, an intelligent software system for weld defect detection based on computer vision is developed. This system is demonstrated to conduct defect detection on images of the current weld position using a trained model. Full article

This study develops a synergistic optimization method of multiple gates integrating hydrodynamic simulation and data-driven methods, with the goal of improving the accuracy of water distribution and regulation efficiency. This approach addresses the challenges of large prediction deviation of hydraulic response and unclear synergy mechanisms in the coupled regulation of multiple gates in irrigation areas. The NSGA-II multi-objective optimisation algorithm is used to minimise the water distribution error and the water level deviation before the gate as the objective function in order to achieve global optimisation of the regulation of the complex canal system. A one-dimensional hydrodynamic model based on St. Venant’s system of equations is built to generate the feature dataset, which is then combined with the random forest algorithm to create a nonlinear prediction model. An example analysis demonstrates that the optimal feedforward time of the open channel gate group is negatively connected with the flow condition and that the method can manage the water distribution error within 13.97% and the water level error within 13%. In addition to revealing the matching mechanism between the feedforward time and the flow condition, the study offers a stable and accurate solution for the cooperative regulation of multiple gates in irrigation districts. This effectively supports the need for precise water distribution in small irrigation districts. Full article

The hydrodynamics characteristics of artificial water diversion canals with long-distance and inter-basin multi-stage sluice gate regulations are prone to sudden increases and decreases, and sluice gate discharge differs from that of natural rivers. Research on the change characteristics of hydrological elements in artificial canals under the control of sluice gates is lacking, as are scientifically accurate calculations of sluice gate discharge. Therefore, addressing these gaps in long-distance artificial water transfer is of great importance. In this study, real-time operation data of 61 sluice gates, pertaining to the period from May 2019 to July 2021, including data on water levels, flow discharge, velocity, and sluice gate openings in the main canal of the Middle Route of the South-to-North Water Diversion Project of China, were analyzed. The discharge coefficient of each sluice gate was calculated by the dimensional analysis method, and the unit-width discharge was modeled as a function of gate opening ($e$), gravity acceleration ($g$), and energy difference ($H$). Through logarithmic transformation of the Buckingham Pi theorem-derived equation, a linear regression model was used. Data within the relative opening orifice flow regime were selected for fitting, yielding the discharge coefficients and stage–discharge relationships. The results demonstrate that during the study period, the water level, discharge, and velocity of the main canal showed an increasing trend year by year. The dimensional analysis results indicate that the stage–discharge response relationship followed a power function ($Q\propto {(\frac{H}{e})}^{constant}$) and that there was a good linear relationship between $l{g}^{(\frac{H}{e})}$ and $l{g}^{(\frac{K}{e})}$ (R² > 0.95, $K={(q^2/g)}^{1/3}$). By integrating geometric, operational, and hydraulic parameters, the proposed method provides a practical tool and a scientific reference for analyzing sluice gates’ regulation and hydrological response characteristics, optimizing water allocation, enhancing ecological management, and improving operational safety in long-distance inter-basin water diversion projects. Full article

This study aimed to reveal the characteristics of returned water in paddy fields at different scales and the rules of its reuse in China’s Ganfu Plain Irrigation District through multiscale (field, lateral canal, main canal, small watershed) observations, thereby optimizing water resource management and improving water use efficiency. Subsequent investigations during the 2021–2022 double-cropping rice seasons revealed that the tillering stage emerged as a critical drainage period, with 49.5% and 52.2% of total drainage occurring during this phase in early and late rice, respectively. Multiscale drainage heterogeneity displayed distinct patterns, with early rice following a “decrease-increase” trend while late rice exhibited “decrease-peak-decline” dynamics. Smaller scales (field and lateral canal) produced 37.1% higher drainage than larger scales (main canal and small watershed) during the reviving stage. In contrast, post-jointing-booting stages showed 103.6% higher drainage at larger scales. Return flow utilization peaked at the field-lateral canal scales, while dynamic regulation of Fangxi Lake’s storage capacity achieved 60% reuse efficiency at the watershed scale. We propose an integrated optimization strategy combining tillering-stage irrigation/drainage control, multiscale hydraulic interception (control gates and pond weirs), and dynamic watershed storage scheduling. This framework provides theoretical and practical insights for enhancing water use efficiency and mitigating non-point source pollution in plain irrigation districts. Full article

IpFlux is a cost-free software developed to provide a simplified, accessible, and accurate solution for hydraulic analysis in open-channel flows. It addresses the need for tools that support rapid decision-making during early design stages, especially when conventional software may be too complex, resource-intensive, or costly. Written in Python, IpFlux features an intuitive interface and implements both explicit and implicit formulations to compute normal and critical depths, hydraulic jumps, flow through weirs and gates, backwater curves, and compound cross-sections. Thanks to its focused interface and direct data entry, IpFlux enables significantly faster estimations than traditional tools used for similar hydraulic calculations, particularly in early project stages. The software’s accuracy and applicability are demonstrated by comparing its outputs against classical references and selected results from established tools such as HEC-RAS and ANSYS Fluent. While IpFlux is not intended to replace advanced simulation software, it offers a reliable and user-friendly alternative for preliminary analyses in engineering projects, as well as for educational purposes in hydraulic engineering. Full article

In the context of irrigation canal flow, numerical models developed to accurately estimate canal behavior based on gate trajectories are often highly complex. Consequently, hardware limitations make it significantly more challenging to implement these models locally at gate devices. In this regard, one of the most significant contributions of this paper is the concept of the hydraulic influence matrix (HIM) and its application as a linear model to estimate the water surface flow in irrigation canals, integrated within an irrigation canal controller to facilitate real-time operations. The HIM model provides a significant advantage by quickly and accurately computing water level and velocity perturbations in open-flow canals. This capability empowers watermasters to apply this linear free-surface model in both unsteady and steady flow conditions, enabling real-time applications in control algorithms. The HIM model was validated by comparing water-level estimates under various perturbations with results from software using the full Saint-Venant equations. The test involved introducing a 10% perturbation in gate movement over a specified time period in two different test cases, resulting in a flow discharge increase of more than 10% in each test case. The results showed maximum absolute errors below 7 cm and 0.2 cm, relative errors of 0.7% and 0.023%, root mean square errors ranging from 2.4 to 0.07 cm, and Nash–Sutcliffe efficiency values of approximately 0.95 in the first and second test cases, respectively, when compared to the full Saint-Venant equations. This highlights the high precision of the HIM model, even when subjected to significant disturbances. However, larger gate movement disturbances (exceeding 10%) should be planned in advance rather than managed in real time. Full article

The Vietnamese Mekong Delta (VMD) faces increasing challenges due to upstream hydrological fluctuations and climate change, necessitating optimized water management strategies. Sluice gates play a critical role in regulating water levels, yet their effectiveness under different operational modes remains insufficiently assessed. This study examines water level fluctuations under three sluice gate operation scenarios implemented along the West Sea dike in the Long Xuyen Quadrangle, Kien Giang Province, using the MIKE 11 hydrodynamic model. The model was calibrated and validated using the observed data, yielding high accuracy at key sluice gates, including Kien River and Ba Hon. Three sluice gate management scenarios were tested: (1) the current automatic and partially forced operation, (2) fully automatic gate control, and (3) fully forced hydraulic operation. The simulation results indicate that Scenario 3 maintained water levels above +0.6 m more frequently, ensuring better water availability for irrigation and domestic use, while Scenarios 1 and 2 resulted in lower water levels at certain locations. Additionally, forced operation led to higher gate opening and closing frequencies at key sluices, allowing for more adaptive control over water levels. These findings emphasize the benefits of proactive sluice gate management in improving water regulation and mitigating the water scarcity risks. This study is among the first to provide empirical, scenario-based evidence comparing fully forced, automatic, and mixed sluice gate strategies under varying hydrological conditions in the Long Xuyen Quadrangle. Full article

The PCHE, as an efficient heat exchanger, plays a crucial role in the storage and regasification of LNG. However, among the existing studies, those that integrate this field with deep learning are scarce. Moreover, research on explainability remains insufficient. To address these gaps, this study first constructs a dataset of heat transfer coefficients (h) through numerical simulations. Pearson correlation analysis is employed to screen out the most influential features. In terms of predictive modeling, the study compares five traditional machine learning models alongside deep learning models such as long short-term memory neural networks (LSTMs), gated recurrent units (GRUs), and Transformer. To further enhance prediction accuracy, three attention mechanisms—self-attention mechanism (SA), squeeze-and-excitation mechanism (SE), and local attention mechanism (LA)—are incorporated into the deep learning models. The experimental results demonstrate that the artificial neural network achieves the best performance among the traditional models, with a prediction accuracy for straight-path h reaching 0.891799 (R²). When comparing deep learning models augmented with attention mechanisms against the baseline models, both LSTM–SE in the linear flow channel and Transformer–LA in the hexagonal flow channel exhibit improved prediction accuracy. Notably, in predicting the heat transfer coefficient of the hexagonal channel, the determination coefficient (R²) of the Transformer–LA model reaches 0.9993, indicating excellent prediction performance. Additionally, this study introduces the SHAP interpretable analysis method to elucidate model predictions, revealing the contributions of different features to model outputs. For instance, in a straight flow channel, the hydraulic diameter (Dh) contributes most significantly to the model output, whereas in a hexagonal flow channel, wall temperature (Tinw) and heat flux (Qw) play more prominent roles. In conclusion, this study offers novel insights and methodologies for PCHE performance prediction by leveraging various machine learning and deep learning models enhanced with attention mechanisms and incorporating explainable analysis methods. These findings not only validate the efficacy of machine learning and deep learning in complex heat exchanger modeling but also provide critical theoretical support for engineering optimization. Full article

Sustainable groundwater resource management necessitates dependable and precise predictions of groundwater head fields under fluctuating climatic conditions. The substitution of original simulation models with efficient surrogates presents a challenge in simultaneously accounting for correlations among multiple time series outputs and maintaining overall prediction accuracy. This study develops a novel surrogate modelling approach, DSCNN-GRU, incorporating a deep separable convolutional neural network (DSCNN) and a gated recurrent unit (GRU), to efficiently capture temporal and spatial variations in groundwater head fields from transient groundwater flow models using input hydraulic conductivity field data. The applicability and performance of the proposed method are evaluated for predicting groundwater head fields in a practical research area under three scenarios with different hydraulic conductivity fields. The performance of the DSCNN-GRU model is compared to the traditional convolutional neural network (CNN), CNN-LSTM, and DSCNN-LSTM models to further test its applicability. The numerical study demonstrates that optimizing hyperparameters can result in reasonably accurate performance of the proposed model, and the “simplest” DSCNN-GRU outperforms CNN, CNN-LSTM, and DSCNN-LSTM in both prediction accuracy and time-to-solution. Full article

Accurate monitoring of ground deformation is crucial for ensuring the safety and stability of hydraulic structures. Current deformation monitoring techniques often face challenges such as limited accuracy and robustness, particularly in complex environments. In this study, we propose a comprehensive method for Global Navigation Satellite System (GNSS) deformation monitoring in hydraulic structures by integrating the strengths of Gated Recurrent Units (GRUs) and Autoregressive Attention mechanisms. This approach enables efficient modeling of long-term dependencies while focusing on critical time steps, thereby enhancing prediction accuracy and robustness, especially in multi-step forecasting tasks. Experimental results show that the proposed GRU–Attention model achieves millimeter-level multi-step prediction accuracy, with predictions closely matching actual deformation data. Compared to the traditional method, the GRU–Attention model improves prediction accuracy by approximately 37%. The model’s attention mechanism effectively captures both short-term variations and long-term trends, ensuring accurate predictions even in complex scenarios. This research advances the field of GNSS deformation monitoring for hydraulic structures, providing valuable insights for engineering decision-making and risk management, ultimately contributing to enhanced infrastructure safety. Full article

Over the past few years, reducing energy consumption in hydraulic excavators has gained increasing attention, driving significant research in this field. One effective strategy involves integrating hydrostatic transmission (HST) and hydraulic pump/motor (HPM) systems into hydraulic excavators. However, challenges like disturbances, throttling-induced pressure drops, and fluid leakage often hinder both positional accuracy and energy efficiency. To tackle these issues, our study proposes a sophisticated dynamic forecasting model for positional control, integrating beluga whale optimization (BWO), long short-term memory (LSTM), and random forest (RF) techniques. The approach begins with dynamic data evaluation using Pearson’s correlation analysis to identify tuning parameters that have moderate to strong correlations with control variables, which are then used as inputs for predictive modeling. Initially, a standalone LSTM framework is developed to estimate the system’s positional output, with BWO optimizing four key tuning parameters. Subsequently, a hybrid BWO-enhanced LSTM-RF system is deployed to capture complex nonlinear patterns, improving the accuracy of motion trajectory predictions. Simulations and experiments confirm that our approach achieves a positional error below 3 mm, ensuring precise tracking and providing reliable data for operators. Compared to conventional proportional–integral–derivative (PID) controllers, standalone LSTM-RF, and a hybrid controller combining particle swarm optimization (PSO), LSTM, a gated recurrent unit (GRU), and PID (PSO-LSTM-GRU-PID), our method achieves superior tracking precision and energy savings of 12.46%, 8.98%, and 3.97%, respectively. Full article

Sand screenout is a critical challenge in hydraulic fracturing, affecting both the construction process and operational safety. This paper proposes a sand screenout warning model that integrates a combinatorial neural network and physical approaches to enhance both the speed and accuracy of sand screenout warnings. Firstly, the combined neural network uses a Transformer to capture key features during fracturing construction from historical data, and the extracted features are input to the Gated Recurrent Unit (GRU) for temporal prediction and the Crested Porcupine Optimizer (CPO) to further optimise the GRU-Transformer hyperparameters of the model. Additionally, the physical model improves the conventional inverse slope method by incorporating a threshold and sliding module, which enhances slope calculation and warning accuracy. The results showed that for fracturing pressure prediction, the proposed CPO-GRU-Transformer model obtained an RMSE value of 0.842 MPa, MAE of 0.613 Mpa, and $R^2$ of 0.971, a smaller RMSE and MAE and a larger $R^2$ than the three pressure prediction models, namely LSTM, GRU, and CPO-GRU. The proposed sand screenout warning model has been applied in the field construction of the U shale gas area in the Sichuan Basin. The warning points of the model proposed in this study were advanced by 73.5 s on average compared with the manual warning points in the three validated fracturing segments, with a successful warning rate of 85.71%, which greatly avoids the possibility of sand screenout and provides a method of fast calculation speed and high prediction accuracy, providing an early warning of sand screenout. Full article

Steady-state and transient hydraulic characteristics of a novel three-plate vertical rotary gate were analysed through physical model experiments and numerical simulations. An experimental gate system was built to analyse the flow characteristics of the gate, and a steady-state flow prediction model was proposed. Steady-state numerical simulations of the gate were conducted to analyse flow field distribution characteristics. A transient numerical model of the gate was established to analyse the flow field distribution characteristics during opening and closing. The discharge coefficient evolution law under different speed conditions was revealed. Under various water levels, the steady-state discharge coefficient of the gate was similar. Within a 0–90° opening, the discharge coefficient grew exponentially. A steady-state flow prediction model for the gate revealed a prediction error of <7%. The discharge coefficient of the gate increased with decreasing opening speed; when the gate was closed, it exhibited asymmetric variation characteristics. The flow hysteresis effect was more evident at higher speeds. Plate 2 experienced the maximum flow force. In the transient state, the flow force acting on the plates exhibited a periodic fluctuation pattern, and the maximum flow force increased with the gate speed. A reference for the design and application of fast opening and closing gates is provided. Full article

Understanding flow dynamics around hydraulic structures is essential for optimizing water management systems and predicting flow behavior in real-world applications. In this study, we simulate a 3D flow control system featuring a sluice gate and a weir, commonly used in hydraulic engineering. The focus is on accurately incorporating modified dynamic boundary conditions (mDBCs) and viscosity treatment to improve the simulation of complex, turbulent flows. We assess the performance of the Smoothed Particle Hydrodynamics (SPH) method in handling these challenging conditions. Especially when the boundary conditions and applicability to industry are two of the SPH method’s grand challenges. Simulations were conducted on a Graphics Processing Unit (GPU) using the DualSPHysics code. The results were compared to theoretical predictions and experimental data found in the literature. Key hydraulic characteristics, including 3D flow effects, hydraulic jump formation, and turbulent behavior, are examined. The combination of mDBCs with the Laminar plus sub-particle scale turbulence model achieved the correct simulation results. The findings demonstrate agreement between simulations, theoretical predictions, and experimental results. This work provides a reliable framework for analyzing turbulent flows in hydraulic structures and can be used as reference data or a prototype for larger-scale simulations in both research and engineering design, particularly in contexts requiring robust and precise flow control and/or environmental management. Full article