Search Results (563)

To meet the demands for real-time and accurate fault warning of wind turbine gear transmission systems, this study proposes an innovative intelligent warning method based on the integration of digital twin and multi-source data fusion. A digital twin system architecture is developed, comprising a high-precision geometric model and a dynamic mechanism model, enabling real-time interaction and data fusion between the physical transmission system and its virtual model. At the algorithmic level, a CNN-LSTM-Attention fault prediction model is proposed, which innovatively integrates the spatial feature extraction capabilities of a convolutional neural network (CNN), the temporal modeling advantages of long short-term memory (LSTM), and the key information-focusing characteristics of an attention mechanism. Experimental validation shows that this model outperforms traditional methods in prediction accuracy. Specifically, it achieves average improvements of 0.3945, 0.546 and 0.061 in Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²) metrics, respectively. Building on the above findings, a monitoring and early warning platform for the wind turbine transmission system was developed, integrating digital twin visualization with intelligent prediction functions. This platform enables a fully intelligent process from data acquisition and status evaluation to fault warning, providing an innovative solution for the predictive maintenance of wind turbines. Full article

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

Reliable estimation of extreme waves is essential for offshore wind turbine system design; however, site-specific conditions limit the application of one-size-fits-all statistical methods. We analyzed extreme wave conditions at potential offshore wind farm sites in South Korea using high-resolution hindcast data (1979–2022) based on the Weather Research and Forecasting (WRF) model. While previous studies have typically relied on a limited combination of distribution types and parameter estimation methods, this study systematically applied various Weibull distribution models and parameter estimation techniques to the environmental contour (EC) method. The results show that the optimal statistical approach varied by site according to the tail characteristics of the wave height distribution. The inverse second-order reliability method (I-SORM) provided the highest accuracy in regions with rapidly decaying tails, achieving root mean square error (RMSE) values of 0.21 in Shinan (using the three-parameter Weibull distribution with maximum likelihood estimation, MLE) and 0.34 in Chujado (with the method of moments, MOM). In contrast, the inverse first-order reliability method (I-FORM) yielded superior performance in areas where the tail decays more gradually, such as Yokjido (RMSE = 0.47 with MLE using the exponentiated Weibull distribution) and Ulsan (RMSE = 0.29, with MLE using the exponentiated Weibull distribution). These findings underscore the importance of selecting site-specific combinations of statistical models and estimation techniques based on wave distribution characteristics, thereby improving the accuracy and reliability of extreme design wave predictions. The proposed framework can significantly contribute to the establishment of reliable design criteria for offshore wind turbine systems by reflecting region-specific marine environmental conditions. Full article

Polar Mesosphere Winter Echoes (PMWEs) are relatively strong radar echoes from 50–80 km altitudes observed at a broad frequency range, at polar latitudes, mainly during equinox and winter seasons. Most PMWEs can be explained by neutral air turbulence creating structures in the mesosphere and enhanced electron density. We have studied the characteristics of PMWEs and their dependence on solar and geophysical conditions using the ESrange RADar (ESRAD) located in northern Sweden during 1996–2021. We found that PMWEs start in mid-August and finish in late May. The mean daily occurrence rate varied significantly during the PMWE season, showing several relative maxima and a minimum in December. The majority of PMWEs were observed during sunlit hours at 60–75 km. Some echoes were detected at 50–60 km. The echo occurrence rate showed a pronounced maximum near local noon at 64–70 km. During nighttime, PMWEs were observed at about 75 km. PMWEs were observed on 47% of days with disturbed conditions (enhanced solar wind speed, K_p index, solar proton, and X-ray fluxes), and on only 14% of days with quiet conditions. Elevated solar wind speed and K_p index each accounted for 30% of the days with PMWE detections. Full article

To overcome the complexity of yarn color measurement using spectrophotometry with yarn winding techniques and to enhance consistency with human visual perception, a yarn color measurement method based on digital photography is proposed. This study employs a photographic colorimetry system to capture digital images of single yarns. The yarn and background are segmented using the K-means clustering algorithm, and the centerline of the yarn is extracted using a skeletonization algorithm. Spectral reconstruction and colorimetric principles are then applied to calculate the color values of pixels along the centerline. Considering the nonlinear characteristics of human brightness perception, the final yarn color is obtained through a nonlinear texture-adaptive weighted computation. The method is validated through psychophysical experiments using six yarns of different colors and compared with spectrophotometry and five other photographic measurement methods. Results indicate that among the seven yarn color measurement methods, including spectrophotometry, the proposed method—based on centerline extraction and nonlinear texture-adaptive weighting—yields results that more closely align with actual visual perception. Furthermore, among the six photographic measurement methods, the proposed method produces most similar to those obtained using spectrophotometry. This study demonstrates the inconsistency between spectrophotometric measurements and human visual perception of yarn color and provides methodological support for developing visually consistent color measurement methods for textured textiles. Full article

In response to the challenge of multi-objective optimal scheduling and efficient solution of hydropower stations under large-scale renewable energy integration, this study develops a multi-objective optimization model with the dual goals of maximizing total power generation and minimizing the variance of residual load. Four complementarity evaluation indicators are used to analyze the wind–solar complementarity characteristics. Building upon this foundation, Hyper-dominance Evolutionary Algorithm (HEA)—capable of efficiently solving high-dimensional problems—is introduced for the first time in the context of wind–solar–hydropower integrated scheduling. The case study results show that the HEA performs better than the benchmark algorithms, with the best mean Hypervolume and Inverted Generational Distance Plus across nine Walking Fish Group (WFG) series test functions. For the hydro-wind-solar scheduling problem, HEA obtains Pareto frontier solutions with both maximum power generation and minimal residual load variance, thus effectively solving the multi-objective scheduling problem of the hydropower system. This work provides a valuable reference for modeling and efficiently solving the multi-objective scheduling problem of hydropower in the context of emerging power systems. This work provides a valuable reference for the modeling and efficient solution of hydropower multi-objective scheduling problems in the context of emerging power systems. Full article

The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ’s seasonal variations using the XGBoost machine learning algorithm. The results revealed that wind stress components, sea surface temperature, and wind stress curl serve as the primary drivers of its seasonal dynamics. The XGBoost model demonstrated superior performance in reconstructing WJ’s seasonal variations, achieving coefficients of determination (R²) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. The reconstructed currents exhibited strong consistency with the Ocean Surface Current Analysis Real-time (OSCAR) dataset, showing errors below 0.05 m/s in spring and autumn and under 0.1 m/s in summer and winter. The proposed multi-feature integrated modeling framework delivers a high spatiotemporal resolution analytical tool for tropical Indian Ocean circulation dynamics research, while simultaneously establishing critical data infrastructure to decode monsoon current coupling mechanisms, advancing early warning systems for extreme climatic events, and optimizing regional marine resource governance. Full article

Accurate grid frequency prediction is essential for maintaining the stability and reliability of power systems. However, the complex dynamic characteristics of grid frequency and the nonlinear correlations among massive time series data make it challenging for traditional time series prediction methods to balance efficiency and accuracy. In this paper, we propose a Dynamic Significance–Correlation Weighting (D-SCW) method, which generates dynamic weight coefficients that evolve over time. This is achieved by constructing a joint screening mechanism of feature time series correlation analysis and statistical significance test, combined with the LightGBM gradient-boosting decision tree (GBDT) framework; accordingly, high-precision prediction of grid frequency time series data is realized. To verify the effectiveness of the D-SCW method, this study conducts comparative experiments on two actual grid operation datasets (including typical scenarios with wind/photovoltaic (PV) installations, accounting for 5–35% of the grid); additionally, the Spearman’s rank correlation coefficient method, mutual information (MI), Lasso regression, and the feature screening method of recursive feature elimination (RFE) are selected as the baseline control; root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are adopted as assessment indicators. The results show that the D-SCW-LightGBM framework reduces the root mean squared error (RMSE) by 5.2% to 10.4% and shortens the dynamic response delay by 52% compared with the benchmark method in high renewable penetration scenarios, confirming its effectiveness in both prediction accuracy and computational efficiency. Full article

The spatial layout of residential communities has a significant impact on the local microclimate. These microclimate changes subtly affect the daily feelings and health status of residents. This study takes hypertension as a case to simulate the outdoor microclimate characteristics of different types of communities, and to analyze the potential correlation between spatial design and the health of residents, providing a scientific basis for the design of health-oriented communities. Initially, the microclimate characteristics of communities are obtained through computational fluid dynamics (CFD) simulation. Subsequently, the correlation between the outdoor microclimate and the incidence of hypertension in communities is discussed. The study area covers communities within a 4 km radius of Zhongnan hospital. The results indicate that microclimatic factors, such as temperature, Predicted Mean Vote (PMV), and Universal Thermal Climate Index (UTCI), are significantly negatively correlated with the incidence of hypertension in communities of different building heights. For temperature, the absolute value of the correlation coefficient for multi-story communities is 0.431, slightly lower for mid-rise communities at 0.323, and further drops to 0.296 for high-rise communities. Correspondingly, the values for PMV are 0.434, 0.336, and 0.306, respectively. The values for UTCI are 0.442, 0.310, and 0.303, respectively. This effect gradually weakens as building heights increase. Fluctuations in wind speed appear to weakly influence the incidence of hypertension. These results provide a scientific basis for health-oriented urban planning. Full article

Exposure modeling plays a crucial role in disaster risk assessments by providing geospatial information about assets at risk and their characteristics. Detailed exposure data enhances the spatial representation of a rapidly changing environment, enabling decision-makers to develop effective policies for reducing disaster risk. This work proposes and demonstrates a methodology linking volunteered geographic information from OpenStreetMap (OSM), street-level imagery from Google Street View (GSV), and deep learning object detection models into the automated creation of exposure datasets for power grid transmission towers, assets particularly vulnerable to strong wind, and other perils. Specifically, the methodology is implemented through a start-to-end pipeline that starts from the locations of transmission towers derived from OSM data to obtain GSV images capturing the towers in a given region, based on which their relevant features for risk assessment purposes are determined using two families of object detection models, i.e., single-stage and two-stage detectors. Both models adopted herein, You Only Look Once version 5 (YOLOv5) and Detectron2, achieved high values of mean average precision (mAP) for the identification task (83.67% and 88.64%, respectively), while Detectron2 was found to outperform YOLOv5 for the classification task with a mAP of 64.89% against a 50.62% of the single-stage detector. When applied to a pilot study area in northern Portugal comprising approximately 5.800 towers, the two-stage detector also exhibited higher confidence in its detection on a larger part of the study area, highlighting the potential of the approach for large-scale exposure modeling of transmission towers. Full article

The precise control of Mach numbers in supersonic and hypersonic compressor wind tunnel systems is a critical challenge in aerodynamic research. Although existing studies have improved prediction accuracy to some extent through machine learning methods, they generally neglect the multi-mode characteristics of complex wind tunnel systems, limiting the generalizability of the models. To address this issue, the present study proposes a multi-mode dynamic fusion Mach number prediction framework that integrates strategies of segmented modeling and cross-modal information fusion. First, single-mode segmented prediction models are constructed on the basis of Multi-output Support Vector Regression (MSVR), with hyperparameters optimized to capture the characteristics of individual modes. Second, the Partial Least Squares (PLS) method is employed to explore the correlations between historical and new modes, dynamically selecting the optimal prediction model and updating the historical mode repository. Experimental results demonstrate that the multi-mode dynamic fusion framework reduces the Root Mean Square Error (RMSE) by 70.57%, 56.4%, and 63.64% compared to Support Vector Regression (SVR), PLS, and Long Short-term Memory (LSTM) networks across six operating conditions. The framework proposed in this paper enhances Mach number prediction accuracy while improving model generalizability. Full article

High-precision wave data serve as a foundation for investigating the wave characteristics of the East China Sea (ECS) and wave energy development. Based on the simulating waves nearshore (SWAN) model, this study uses the ERA5 (ECMWF Reanalysis v5) reanalysis wind field data and ETOPO1 bathymetric data to perform high-precision simulations at a resolution of 0.05° × 0.05° for the waves in the area of 25–35° N and 120–130° E in the ECS from 2009 to 2023. The simulation results indicate that the application of the whitecapping dissipation parameter Komen and the bottom friction parameter Collins yields an average RMSE of 0.374 m and 0.369 m when compared to satellite-measured data, demonstrating its superior suitability for wave simulation in shallow waters such as the ESC over the other whitecapping dissipation parameter, Westhuysen, and the other two bottom friction parameters, Jonswap and Madsen, in the SWAN model. The monthly average significant wave height (SWH) ranges from 0 to 3 m, exhibiting a trend that it is more important in autumn and winter than in spring and summer and gradually increases from the northwest to the southeast. Due to the influence of the Kuroshio current, topography, and events such as typhoons, areas with significant wave heights are found in the northwest of the Ryukyu Islands and north of the Taiwan Strait. The wave energy flux density in most areas of the ECS is >2 kW/m, particularly in the north of the Ryukyu Islands, where the annual average value remains above 8 kW/m. Because of the influence of climate events such as El Niño and extreme heatwaves, the wave energy flux density decreased significantly in some years (a 21% decrease in 2015). The coefficient of variation of wave energy in the East China Sea exhibits pronounced regional heterogeneity, which can be categorized into four distinct patterns: high mean wave energy with high variation coefficient, high mean wave energy with low variation coefficient, low mean wave energy with high variation coefficient, and low mean wave energy with low variation coefficient. This classification fundamentally reflects the intrinsic differences in dynamic environments across various maritime regions. These high-precision numerical simulation results provide methodological and theoretical support for exploring the spatiotemporal variation laws of waves in the ECS region, the development and utilization of wave resources, and marine engineering construction. Full article

Due to its complex mechanism of action, the wind-resistant design of square cross-section structures against vortex-induced vibration (VIV) still presents significant challenges. The angle of the wind direction is an important factor affecting the VIV characteristics of square cylinders. A series of stationary model pressure tests were performed and an elastic supporting model was used in the present study. The effects of the wind direction angle on parameters corresponding to fluid–structure interaction were analyzed with reference to the Strouhal number, range of “lock-in”, amplitude, and aerodynamic forces. The Strouhal number of the square cylinder was greatest at a 16° wind direction angle. When the wind direction angle was 10°, the wind speed range of vortex-induced vibration (VIV) of the square cylinder was the greatest, and the corresponding value was the smallest when the wind direction angle ranged from 20° to 45°. Within the vibration interval, the extreme value of the amplitude was smallest when the wind direction angle was 10°, and the extreme value of the amplitude was greatest when the wind direction angle was 30°. The vibration state had a minimal influence on the mean lift coefficient and a relatively large influence on the mean drag coefficient. Full article

PM_2.5 pollution poses significant risks to human health and the environment, underscoring the importance of accurate PM_2.5 simulation. This study simulated a representative PM_2.5 pollution event using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), incorporating the assimilation of infrared atmospheric motion vector (AMV) data from the Fengyun-4A (FY-4A) satellite. A comprehensive analysis was conducted to examine the meteorological characteristics of the event and their influence on PM_2.5 concentration simulations. The results demonstrate that the assimilation of FY-4A infrared AMV data significantly enhanced the simulation performance of meteorological variables, particularly improving the wind field and capturing local and small-scale wind variations. Moreover, PM_2.5 concentrations simulated with AMV assimilation showed improved spatial and temporal agreement with ground-based observations, reducing the root mean square error (RMSE) by 8.2% and the mean bias (MB) by 15.2 µg/m³ relative to the control (CTL) experiment. In addition to regional improvements, the assimilation notably enhanced PM_2.5 simulation accuracy in severely polluted cities, such as Tangshan and Tianjin. Mechanistic analysis revealed that low wind speeds and weak atmospheric divergence restricted pollutant dispersion, resulting in higher near-surface concentrations. This was exacerbated by cooler nighttime temperatures and a lower planetary boundary layer height (PBLH). These findings underscore the utility of assimilating satellite-derived wind products to enhance regional air quality modeling and forecasting accuracy. This study highlights the potential of FY-4A infrared AMV data in improving regional pollution simulations, offering scientific support for the application of next-generation Chinese geostationary satellite data in numerical air quality forecasting. Full article

With the increase of natural gas consumption year by year, the shortage of urban natural gas reserves leads to the increasingly serious gas supply–demand imbalance. It is particularly important to establish a correct and reasonable gas daily load forecasting model to ensure the realization of forecasting function and the accuracy and reliability of calculation results. Most of the current prediction models are combined with the characteristics of gas data and prediction models, and the influencing factors are often considered less. In order to solve this problem, the basic concept of multiple weather parameter (MWP) was introduced, and the influence of factors such as the average temperature, solar radiation, cumulative temperature, wind power, and temperature change of the building foundation on the daily load of urban gas were analyzed. A multiple weather parameter–daily load prediction (MWP-DLP) model based on System Thermal Days (STD) was established, and the genetic algorithm was used to solve the model. The daily gas load in a city was predicted, and the results were analyzed. The results show that the trend between the predicted value of gas daily load obtained by the MWP-DLP model and the actual value was basically consistent. The maximum relative error was 8.2%, and the mean absolute percentage error (MAPE) was 2.68%. The feasibility of the MWP- DLP prediction model was verified, which has practical significance for gas companies to reasonably formulate and decide peak shaving schemes to reserve natural gas. Full article