Search Results (7,652)

Wind-assisted propulsion using Flettner rotors has gained attention as the shipping sector faces stricter decarbonization regulations. This study compares conventional Fixed Speed Control with Adaptive Speed Control for a 100 m coastal vessel. The proposed Adaptive Speed Control selectively activates the rotor based on relative wind conditions and adjusts rotor speed according to the surge-direction projection of Magnus force. A simulation framework based on the MMG maneuvering model evaluates path-following performance, fuel consumption, and annual performance indicators. Results show that Adaptive Speed Control achieves 18.84% reduction in fuel consumption, corresponding to annual savings of 212.02 tons of fuel, USD 190,823 in OPEX, and 679.76 tons of CO₂ emissions. Selective rotor operation reduces the Fatigue Damage Index by approximately 89%, resulting in 84.48% reduction in annual maintenance costs. Unwanted lateral forces and yaw disturbances are mitigated, improving path-following and maneuvering stability. These findings demonstrate that situationally aware Adaptive Speed Control improves energy efficiency and operational characteristics of Flettner-rotor-assisted propulsion systems while maintaining maneuvering performance, providing practical guidance for wind-assisted ship operation under realistic coastal conditions. Full article

The optimal allocation of Photovoltaic (PV) and wind-based renewable energy sources and Battery Energy Storage System (BESS) capacity is an important issue for efficient operation of a microgrid network (MGN). The impact of the unpredictability of PV and wind generation needs to be smoothed out by coherent allocation of BESS unit to meet out the load demand. To address these issues, this article proposes an efficient Energy Management System (EMS) and Demand Side Management (DSM) approaches for the optimal allocation of PV- and wind-based renewable energy sources and BESS capacity in the MGN. The DSM model helps to modify the peak load demand based on PV and wind generation, available BESS storage, and the utility grid. Based on the Real-Time Market Energy Price (RTMEP) of utility power, the charging/discharging pattern of the BESS and power exchange with the utility grid are scheduled adaptively. On this basis, a Jellyfish Search Algorithm (JSA)-based bi-level optimization model is developed that considers the optimal capacity allocation and power scheduling of PV and wind sources and BESS capacity to satisfy the load demand. The top-level planning model solves the optimal allocation of PV and wind sources intending to reduce the total power loss of the MGN. The proposed JSA-based optimization achieved 24.04% of power loss reduction (from 202.69 kW to 153.95 kW) at peak load conditions through optimal PV- and wind-based DG placement and sizing. The bottom level model explicitly focuses to achieve the optimal operational configuration of MGN through optimal power scheduling of PV, wind, BESS, and the utility grid with DSM-based load proportions with an aim to minimize the operating cost. Simulation results on the IEEE 33-node MGN demonstrate that the 20% DSM strategy attains the maximum operational cost savings of €ct 3196.18 (reduction of 2.80%) over 24 h operation, with a 46.75% peak-hour grid dependency reduction. The statistical analysis over 50 independent runs confirms the sturdiness of the JSA over Particle Swarm Optimization (PSO) and Osprey Optimization Algorithm (OOA) with a standard deviation of only 0.00017 in the fitness function, demonstrating its superior convergence characteristics to solve the proposed optimization problem. Finally, based on the simulation outcome of the considered bi-level optimization problem, it can be concluded that implementation of the proposed JSA-based optimization approach efficiently optimizes the PV- and wind-based resource allocation along with BESS capacity and helps to operate the MGN efficiently with reduced power loss and operating costs. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides a vital means for sea surface wind speed retrieval, yet its application under extreme typhoon conditions remains challenging. Conventional geophysical models (GMFs) saturate in high wind speed regimes (>20 m/s), and deep learning models (e.g., CNNs) are constrained by data sparsity and feature complexity in typhoon environments. To address these issues, we propose a Comparative Learning method of CNN-Transformer with GMF fusion (CLCTG). The CNN branch extracts local coupling patterns, the Transformer branch models global dependencies, and Kullback–Leibler (KL) divergence loss is used for contrastive learning to heighten sensitivity to complex typhoon wind fields. The GMF branch serves as a physical reference/anchor in the low- to moderate-wind-speed range (<20 m/s) to guide the learning of data-driven branches and avoid overfitting by any single data-driven path. The adaptive fusion branch dynamically reweights the three branch outputs, combining local statistical characteristics to improve performance over approximately 0–30 m/s and extending the range of reliable GNSS-R retrieval from about 20 m/s to about 30 m/s; it should be noted that CLCTG exhibits a performance bottleneck in the extreme >30 m/s range. To further improve high-wind-speed predictions, we introduce environmental features based on their correlation with wind speed; ablation experiments demonstrate that the combined use of environmental parameters and CYGNSS features maximizes overall accuracy. Testing on five typhoons from the Eastern and Western Hemispheres confirms CLCTG’s generalization across diverse geographic contexts, and branch-wise comparisons validate its structural advantages. Buoy observations show peripheral errors below 3 m/s and physically consistent wind speed gradients in the core region. These results indicate that multi-source fusion of CYGNSS and environmental data, coupled with contrastive learning and physical reference, offers a reliable and efficient solution for typhoon wind speed retrieval. Full article

In the present work, a direct oil cooling strategy using a multi-nozzle configuration is proposed for the thermal management of high-power density electric machines. The stator and winding temperatures, heat transfer coefficient, injection pressure, and power consumption are investigated for different nozzle types, nozzle numbers, heights of nozzle combinations, and oil flow rates. In addition, an artificial neural network (ANN) model based on two algorithms is developed for predicting thermal performance under various operating conditions. The flat jet nozzle shows the lowest maximum winding temperature of 120.3 °C and a superior heat transfer coefficient of 3028.6 W/m²-K compared to both full cone nozzles. The power consumption for the flat jet nozzle is higher at 123.9 W compared to other nozzle types. The combination of four flat jet nozzles shows improved oil spray distribution and enhanced cooling compared to combinations of two and six flat jet nozzles. Further, the thermal performance of oil spray cooling with four flat jet nozzles improves when height and oil flow rate are increased. Oil spray cooling with the best configuration shows a winding temperature, heat transfer coefficient, and injection pressure of 98.9 °C, 3408.6 W/m²-K and 4.86 bar, respectively, at a flow rate of 20 LPM. The proposed neural network model with a Levenberg–Marquardt (LM) training variant and logarithmic–sigmoidal (Log) transfer function shows the lowest prediction error within ±2%. Full article

This study investigates the effects of nitrogen application and sprinkler irrigation on winter wheat growth, water use efficiency (WUE), and yield formation under dry-hot wind stress. The primary aim was to understand how nitrogen levels influence canopy structure, soil water–nitrogen coupling, and yield components under varying irrigation conditions. Field experiments were conducted with different nitrogen rates (N1, N2, N3, N4, N5) and sprinkler irrigation under heat stress. Plant height, leaf area index (LAI), canopy interception, and stemflow were measured, along with soil moisture and nitrogen content in the root zone. Results indicate that moderate nitrogen application (212 kg N ha⁻²) optimized yield and WUE, with a significant enhancement in canopy structure and water interception. High nitrogen levels resulted in increased water consumption but decreased nitrogen use efficiency (NUE), while lower nitrogen treatments showed reduced yield stability under heat stress. The findings suggest that balanced nitrogen management, in combination with timely irrigation, is essential for improving winter wheat productivity under climate stress. This study highlights the importance of optimizing water and nitrogen inputs to achieve sustainable wheat production in regions facing increasing climate variability. Full article

This study presents a multiscale, uncertainty-aware hybrid deep learning approach addressing the short-term wind speed prediction problem, which is critical for the reliable planning and operation of wind energy systems. Wind signals are decomposed using adaptive variational mode decomposition (VMD), and the resulting wind components are processed together with meteorological data through a dual-stream CNN–BiLSTM architecture. Based on this multiscale representation, probabilistic forecasts are generated using quantile regression to capture best- and worst-case scenarios for decision-making purposes. Unlike fixed prediction intervals, the proposed approach produces adaptive prediction bands that expand during unstable wind conditions and contract during calm periods. The developed model is evaluated using four years of meteorological data from the Afyonkarahisar region of Türkiye. While the proposed model achieves competitive point forecasting performance (RMSE = 0.700 m/s and MAE = 0.54 m/s), its main contribution lies in providing reliable probabilistic forecasts through well-calibrated uncertainty quantification, offering decision-relevant information beyond single-point predictions. The proposed method is compared with a classical CNN–LSTM and several structural variants. Furthermore, SHAP-based explainability analysis indicates that seasonal and solar-related variables play a dominant role in the forecasting process. Full article

Extreme weather conditions, specifically typhoons and strong gusts, create a highly transient environment for wind power data collection, leading to performance degradation that significantly impacts the safety and stability of the wind power system. To accurately predict wind power trends under these conditions, this paper proposes a prediction model integrating Singular Spectrum Analysis (SSA), Temporal Convolutional Network (TCN), Convolutional Neural Network (CNN), and a global average pooling layer, combined with inverse adaptive transfer learning. First, SSA is applied to reduce noise in the collected wind power operation data and extract key information. Subsequently, a prediction model is constructed based on TCN, CNN, and global average pooling. The model employs dilated causal convolutions to capture long-term dependencies and uses two-dimensional convolution kernels to extract local mutation features. Furthermore, a domain-adaptive transfer learning module is designed to adjust the model’s parameter weights via backward optimization based on the Maximum Mean Discrepancy (MMD) between the source and target domains. Experimental validation is conducted using real-world wind power operation data from a wind farm in Guangxi, containing 3000 samples sampled at 10 min intervals specifically during severe typhoon periods. Experimental results demonstrate that even with only 60% of the target data, the proposed method outperforms the traditional TCN neural network, reducing the Root Mean Square Error (RMSE) by 58.1% and improving the Coefficient of Determination (R²) by 32.7%, thereby verifying its effectiveness in data-scarce extreme scenarios. Full article

Water-lubricated bearings are critical components in marine propulsion systems, necessitating materials with exceptional tribological properties to ensure reliability. Filament-winding technology is an effective molding method for enhancing the comprehensive properties of polymers, and the selection of fiber materials has a significant impact on the performance of polymers. In this study, three types of polyurethane (PU) matrix filament-winding composites were fabricated via filament-winding technology. Under water-lubricated conditions, a friction test (disk-to-disk) with a duration of 2 h was performed, followed by systematic observations of the resultant wear behavior. The results indicate that aramid fibers exhibited the superior reinforcing effect on the PU matrix, effectively suppressing wear while enhancing mechanical properties. Specifically, under the conditions of 0.5 MPa-250 r/min (0.314 m/s), the minimum friction coefficient of the aramid fiber-wound composite material was 0.093, which was 57.73% lower than that of pure polyurethane. Under the conditions of 0.7 MPa-50 r/min (0.0628 m/s), the wear mass of the sample was limited to only 1.5 mg, which was 12% lower than that of polyurethane. This research can provide a practical reference for the application of filament-wound composite materials in water-lubricated bearings. Full article

Renewable energy sources account for approximately one-quarter of the total electric power generating capacity in the United States. These sources increase system complexity, with potential negative impacts caused by their inherent variability. A microgrid, a decentralized local grid, offers an excellent solution for integrating these sources into the system’s generation mix in a cost-effective and efficient manner. This paper presents a comprehensive fault tree analysis for the reliability assessment of microgrids, ensuring their safe operation. In this work, fault tree analysis of a microgrid in grid-tied mode with solar, wind, and battery energy storage systems is performed, and the results are reported. The analyses and calculations are performed using the Relyence software suite. The fault tree analysis was performed using various calculation methods, including exact (conventional fault tree analysis), simulation (Monte Carlo simulation), cut-set summation, Esary–Proschan, and cross-product. Once these analyses were completed, the results were compared with the ‘exact’ method as the base case. Critical risk measures, such as unavailability, conditional failure intensity, failure frequency, mean unavailability, number of failures, and minimal cut-sets, were documented and compared. Importance measures, such as marginal or Birnbaum, criticality, diagnostic, risk achievement, and risk reduction worth, were also computed and tabulated. Details of all cut-sets and the probability of failure are presented. The calculated importance measures would help microgrid operators focus on events that yield the greatest system improvements and maintain an acceptable range of risk levels to ensure safe operation and improved system reliability. Full article

The energy sector in all countries around the world is undergoing a transformation, with the main trend being the increasing share of renewable sources. Some countries, such as those in the European Union, have set themselves the goal of completely phasing out fossil fuels by 2050. Currently, the energy systems of European countries are far from this goal, and fossil fuels play a key role in balancing energy systems. This article presents a one-year simulation of a hypothetical Polish energy system based solely on renewable sources and utilizing biomethane, synthetic ammonia, and solid biomass as sources to ensure energy supply in the event of unfavorable weather conditions, which means a lack of wind and solar radiation. Six variants of these systems were analyzed, demonstrating the feasibility of such a system using only biogas as a stabilizing fuel. The required generating capacities of wind turbines, photovoltaic panels, and installations for converting biomethane, ammonia, and solid biomass into electricity were determined. Calculations were based on historical data recorded in 2024 in the Polish energy system. It was found that by increasing currently installed PV and wind turbines by a factor of 4.8 and installing 24 GW of ICE engines fueled with biomethane and an additional 10 GW of ORC modules, current electricity demand would be covered 100% by renewable energy sources. The same goal can be achieved without ORC modules by increasing the installed power of PV and wind turbines by a factor of 6.8. The novelty of this research is the application of the fully renewable concept of electricity generation systems to Polish reality using real-life data. Full article

The rapid expansion of wind energy in Southeast Europe has raised concerns about its long-term impacts on bird populations, particularly through collisions with wind turbines. Here, we analyze systematic collision monitoring data collected between 2010 and 2024 within the Integrated System for Protection of Birds in the Kaliakra Protected Area (northeast Bulgaria). Monitoring covered 52 wind turbines until 2017 and 114 turbines from 2018 onwards, using daily carcass searches within standardized 200 × 200 m plots around each turbine. Collision rate was analyzed using effort-normalized statistical models and spatial (GIS-based) analyses to assess temporal trends and habitat context derived from land-cover data. Effort-normalized analyses indicate that collision rate per turbine varied over time and exhibited a pronounced long-term decline, together with clear spatial heterogeneity. Turbines located in open steppe landscapes were associated with consistently higher collision rates compared to turbines situated in other habitat types. These results provide long-term empirical evidence from an operational wind farm area, contributing robust baseline information for cumulative impact assessment and spatial planning. From a sustainability perspective, long-term, effort-standardized collision monitoring represents a critical tool for balancing renewable energy expansion with biodiversity conservation. By providing empirical evidence on how collision occurrence evolves under sustained operational conditions, this study supports adaptive mitigation, cumulative impact assessment, and spatial planning frameworks essential for the sustainable development of wind energy in ecologically sensitive regions. Full article

The growing depletion of fossil fuel resources and rising energy costs underscore the need for efficient renewable energy technologies, such as unglazed transpired solar collectors (UTSCs). UTSCs harness solar energy to preheat outdoor air, thereby improving building energy efficiency and reducing reliance on conventional heating systems. This study presents a computational fluid dynamics (CFD) analysis of UTSC performance under Lithuanian winter conditions (ambient air temperature −2.64 °C, solar irradiance 733.45 W/m², wind speed 1.93 m/s) using two- and three-dimensional models developed in ANSYS FLUENT. The 3D model simulates a realistic wall fragment with multiple repeating sheet metal profiles and an air gap, while the 2D model represents a longitudinal section applicable to generic UTSC configurations. Both models were validated against experimental data and used to evaluate airflow velocity, pressure distribution, and air temperature rise. The results indicate overall thermal efficiencies of 54.32% for the 3D model and 54.07% for the 2D model, demonstrating that simplified 2D models can achieve comparable accuracy while significantly reducing computational cost. These findings highlight the potential of high-resolution CFD modelling for optimizing UTSC design and enabling faster, more reliable assessments for integration in industrial and commercial building applications. Full article

With the rapid development of high-resolution satellite remote sensing technology, wind turbine detection based on remote sensing imagery has emerged as a crucial research area in renewable energy. However, accurate identification of wind turbines remains challenging due to complex geographical backgrounds and their typical appearance as small objects in images, where limited features and background interference hinder detection performance. To address these issues, this paper proposes CGA-YOLO, a specialized network for detecting small targets in high-resolution remote sensing images, and constructs the SDWT dataset, containing Gaofen-2 imagery covering various terrains in Shandong Province, China. The network incorporates three key enhancements: dynamic convolution improves multi-scale feature representation for precise localization; the Convolutional Block Attention Module (CBAM) enhances feature convergence through channel and spatial attention mechanisms; and GhostBottleneck maintains high-resolution details while strengthening feature channels for small targets. Experimental results demonstrate that CGA-YOLO achieves an F1-score of 0.93 and an mAP50 of 0.938 on the SDWT dataset, and obtains an mAP50 of 0.9033 on both RSOD and VEDAI public datasets. CGA-YOLO establishes its superior accuracy over multiple mainstream detection models under identical experimental conditions, confirming its potential as a reliable technical solution for accurate wind turbine identification in complex environments. Full article

In recent years, new methods to reuse, repurpose, recycle, and recover decommissioned wind turbine blades (dWTBs) have actively been developed in the wind industry. In this study, the authors address the scientific challenge of repurposing decommissioned wind turbine blades for earthwork applications, particularly as part of retaining structures. A gravity retaining structure made entirely from recycled materials is introduced, consisting of glass fibre-reinforced polymer (GFRP) composite modular units derived from dWTBs. To improve the structure’s sustainability, a mixture of typical sand and lightweight waste materials is considered for filling and backfilling of the GFRP units. In particular, two waste materials are examined—a polymer foil derived from recycled laminated glass and tyre-derived aggregate (TDA) in the form of rubber powder—which are incorporated into the sand matrix in typical dry mass proportions ranging from 2% to 32% and 5% to 20%, respectively, reflecting practical ranges considered in geotechnical backfill applications. The research involved material testing of all recyclates and their mixtures with standard sand, as well as two-dimensional finite-element (2D FE) analysis of a retaining structure using the determined material properties. To facilitate the real-world implementation of this novel technology, a structure was designed to account for ground conditions at a specific site to protect against an existing landslide. In summary, this study presents the concept of a sustainable retaining structure along with results from material tests and an initial design for implementation, supported by FE analysis of overall stability. Full article

In response to the critical demand for improved characterization of atmospheric stability effects in wind turbine wake prediction, this study proposes and systematically validates a new analytical wake model that incorporates atmospheric stability effects. In recent years, research on wake models with atmospheric stability effects has primarily followed two approaches: incorporating stability through high-fidelity numerical simulations or modifying classical analytical wake models. While the former offers clear mechanical insights, it incurs high computational costs, whereas the latter improves efficiency yet often suffers from near-wake prediction biases under stable stratification, lacks a unified framework covering the entire wake region, and relies heavily on case-specific calibration of key parameters. To overcome these limitations, this study introduces a stability-dependent turbulence expansion term with a square of a cosine function and the stability sign parameter, enabling the model to dynamically respond to varying atmospheric conditions and overcome the reliance of traditional models on neutral atmospheric assumptions. It achieves physically consistent descriptions of turbulence suppression under stable conditions and convective enhancement under unstable conditions. A newly developed far-field decay function effectively coordinates near-wake and far-wake evolution, maintaining computational efficiency while significantly improving prediction accuracy under complex stability conditions. The Present model has been validated against field measurements from the Scaled Wind Farm Technology (SWiFT) facility and the Alsvik wind farm, demonstrating superior performance in predicting wake velocity distributions on both vertical and horizontal planes. It also exhibits strong adaptability under neutral, stable, and unstable atmospheric conditions. This proposed framework provides a reliable tool for wind turbine layout optimization and power output forecasting under realistic atmospheric stability conditions. Full article