Search Results (31,051)

Machine learning technologies are increasingly being used in data analysis as potential tools for assessing natural resources. The goal of this study is to determine how successfully the recently released Time Series Modeller (MATLAB R2026a) predicts wind conditions in the Black Sea basin’s nearshore zones. Ten years of hourly ERA5 data (2016–2025) were considered, with the predicted parameters being related to wind speed (at 100 m height) and the corresponding wind direction. A number of deep learning models were evaluated, and the findings were presented using statistical metrics such as RMSE (Root Mean Squared Error), relative error, and the Weibull distribution. The ERA5 and anticipated wind speed showed fair agreement, with the exception of extremely low (<3 m/s) and high wind speeds (>18 m/s), which may have been inflated by the statistical indicator used. The wind dispersion was examined on a monthly and hourly basis. In terms of wind direction, it was discovered that deep learning models are unable to replicate wind conditions from the north sector; nevertheless, this appears to be an issue with models based on RMSE prediction. Full article

The study of hybrid multiterminal high-voltage direct current (HVDC) systems revealed different outcomes regarding their performance under fault conditions. Previous research showed that under the fault conditions, the high-voltage direct current (HVDC) voltage experienced a drop to zero, accompanied by a reverse overshoot. In this paper, a model that integrates hybrid multiterminal line commutated converters (LCCs) and a voltage source converter (VSC) with an HVDC network is presented. The model has been mathematically formulated and implemented using Matlab/Simulink (R2018b) software to examine its fault behaviors and locations, particularly on the DC line to ground fault, line to line fault, and single line to ground fault across various fault resistance levels. The system consists of a wind energy conversion system, a photovoltaic array, protection scheme, LCC rectifier station, VSC inverter station, inverter control, AC filters, a distributed parameter transmission line for the HVDC transmission line, a three-phase inductor-capacitor (LC) filter, and a three-phase transformer. The findings indicated that during the scenario of an 8 ohms of resistance under a single line to ground fault, phase A of the inverter AC grid voltage decreased from its operational level. Meanwhile, the voltage across the DC line increased, accompanied by a rise in DC line current. Additionally, calculations of the fault location were determined to be 1.1561 km from the point of reference. This study contributes to the understanding of fault dynamics in HVDC systems, providing essential insights that enhance operational reliability and efficiency in power transmission networks. Full article

Accurate, real-time temperature monitoring of traction motors, battery packs, and power electronic modules is a critical requirement for the safety, efficiency, and long-term reliability of electric vehicles (EVs), particularly on the curved, space-constrained, and vibration-prone surfaces found on motor end-windings, battery-module casings, and busbar assemblies. To meet the needs of such automotive curved-surface applications, as well as wearable devices and flexible electronic skin, this paper presents complementary metal-oxide-semiconductor (CMOS) temperature sensor. Its core includes a CMOS sensor chip with an integrated bandgap reference circuit and dual-path electrostatic discharge (ESD) protection, combined with flexible printed circuit board (FPCB/FPC) for conformal mounting on curved surfaces. Circuit-level simulation predicts a typical reference-voltage temperature coefficient of 20 ppm/°C over −40 to 125 °C, while experimental temperature characterization yields a temperature output (TEMP) sensitivity of approximately 5.0 mV/°C over the reported temperature range. Absolute temperature error and linearity are not claimed as independently verified performance metrics in the present revision because the currently available experimental documentation does not preserve the reference-temperature calibration traceability, repeated-measurement information, measurement-uncertainty analysis, or calculation definitions required to substantiate the previously reported ±1 °C and 0.99% values. After flexible integration and 100 bending cycles at a 10 mm radius, the reference-voltage variation remains below 0.1%, while the reported temperature-equivalent TEMP-output shift remains within ±0.5 °C under the tested laboratory conditions. These results demonstrate short-term laboratory bending stability and temperature-sensing performance under the tested conditions rather than long-term fatigue or automotive vibration qualification. The proposed sensor therefore demonstrates potential for curved and space-constrained thermal-monitoring applications, including permanent magnet synchronous motor (PMSM) stator windings and battery-module surfaces, while validation on actual EV components and formal automotive qualification remain necessary for production deployment. Full article

Active management making forests more resistant to wildfire and more resilient afterwards is a widely held assumption that is rarely tested against comparable fires. In March 2023, two wildfires ignited three days and 54 km apart under the same synoptic weather in southern Korea, one in a naturally regenerating forest of Jirisan National Park and one in a pine-dominated production forest. Treating the pair as a natural experiment, we compared burn severity and three-year recovery using Sentinel-2 imagery, hourly ERA5-Land weather, the 1:5000 national stand map and a composition-weighted unburned reference. Both fires burned under light mean winds over nearly equal areas (117 and 122 ha), yet high severity covered 0.4% of the park fire and 11.4% of the managed-forest fire. Broadleaved stands burned alike at the two sites. Pine stands burned far more severely in the managed forest (mean differenced Normalised Burn Ratio 0.37) than in the park (0.25), and pine made up 57% of the managed-forest burn, whereas conifers, mostly pine, made up only 14% of the park burn. The park’s steeper and more sun-exposed terrain would, if anything, have favoured more severe fire. The 2025 stand map reclassified 98% of the managed forest’s pine but classified 19% of its broadleaved stands as unstocked, consistent with the post-fire clearing of pine. Three years later, the park had regained at least 80% of its pre-fire levels for all three spectral indices, whereas the managed forest remained below 80% for NBR and NDMI. Broadleaved and mixed stands showed similar spectral recovery at both sites; pine showed slower spectral recovery at both sites, and the managed forest’s lightly burned pine regained about half the share of its loss observed in the park’s pine, which remained classified as stocked forest, although the intervals of the two estimates overlapped. The managed regime was associated with greater burn severity and slower spectral recovery; the latter coincided with its pine-dominated composition and the post-fire reclassification of most pine stands as unstocked. Full article

To investigate the coupling effect of sand erosion and dynamic icing on ice adhesion strength of wind turbine blades in alpine regions, this study experimentally examines the single-factor and coupling effects of four key factors: separation temperature, icing temperature, loading rate, and surface roughness of eroded blade coatings. Single-factor analyses reveal that dynamic ice adhesion strength is generally lower than static ice but follows the same variation trend. It increases linearly with decreasing separation temperature and increasing sand-eroded roughness, and decreases linearly with increasing loading rate. A nonlinear relationship exists with icing temperature: when the separation temperature is lower than the icing temperature, the dynamic ice adhesion strength increases linearly with the decrease in icing temperature; when the separation temperature is higher than the icing temperature, the dynamic ice adhesion strength decreases linearly with the decrease in icing temperature. Orthogonal array testing demonstrates the hierarchy of influence, separation temperature > loading rate > icing temperature > surface roughness, with the first three factors exerting statistically significant effects. Furthermore, a regression equation of dynamic ice adhesion strength is established, which can provide an estimation basis for ice adhesion strength under untested parameters. These findings provide critical data support for the optimized design of wind turbine blade de-icing systems in alpine regions. Full article

The Modular Multilevel Converter-based High-Voltage Direct Current (MMC-HVDC) system has emerged as a promising technology to integrate offshore wind farms (OWFs). However, onshore AC faults and resulting DC-link voltage escalation can challenge the reliability of MMC-HVDC systems. This paper proposes an integrated dual energy control with energy dissipation scheme (DEC-EDS) to improve the AC fault ride-through (FRT) in OWF MMC-HVDC systems. The proposed DEC-EDS scheme leverages the short-term overload capacity of MMC half-bridge submodule (SM) capacitors to store part of the surplus power during onshore AC faults while dissipating any excess power into a DC energy dissipation device (EDD). During fault conditions, the MMC SM capacitors and the DC EDD operate in parallel to manage surplus energy, with the capacitors providing temporary buffering and the EDD dissipating the excess power. The proposed scheme is tested on a ±320 kV/420 MW MMC-HVDC system. The results show that the proposed control scheme effectively maintains DC-link voltages, ensuring the connection of OWFs. Full article

Vertical axis wind turbines (VAWTs) have become an attractive option for use in cities and other built environments because they can capture wind from different directions. This gives them an advantage over horizontal axis wind turbines in locations where wind direction changes frequently. However, VAWTs still face several challenges, including unstable airflow, dynamic stall, flow separation, and negative torque during certain parts of their rotation cycle. These issues can reduce overall performance and efficiency. This systematic literature review focuses on three closely related areas: VAWT aerodynamic performance; the use of flow deflectors to improve airflow; and the application of artificial intelligence for prediction, optimization, and control. The review followed a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-based method and collected studies published between 2008 and 2026 from Scopus, Web of Science, ScienceDirect, and Google Scholar. Following PRISMA guidelines, 1127 records were identified, 792 remained after duplicate removal, 136 full texts were assessed, and 21 studies were included in the final review. The selected studies were organized according to turbine type, deflector design, operating conditions, research methods, and the role of artificial intelligence. An assessment was also carried out to compare evidence from experiments, validated simulations, optimization studies, and emerging AI applications. The findings show that well designed deflectors can improve the aerodynamic performance of VAWTs when compared with their original configurations. However, the level of improvement depends on factors such as turbine design, wind speed, Reynolds number, tip speed ratio, and deflector shape. Because of these differences, reported performance gains should be considered specific to each study rather than a general result. Artificial intelligence has mainly been used for performance prediction, optimization, surrogate modelling, and turbine control. Reinforcement learning appears promising for adaptive deflector control. However, very few studies have tested a complete system that combines sensors, an adjustable deflector, artificial intelligence, and real-time closed-loop control. Based on the reviewed studies, a five-layer research framework is proposed. The framework includes aerodynamic and mechanical design, data collection and processing, AI model development, real-time control and actuation, and experimental validation. This framework is presented as a research direction for future investigation. Future studies should compare reinforcement learning with traditional control methods, evaluate energy consumption, address the gap between simulation and real-world operation, and conduct more experimental testing. Full article

Pile–bucket foundation is a newly developed offshore wind turbine supporting structure created by combining a monopile and a bucket foundation. Current research mainly focuses on its bearing characteristics in homogeneous soil layers (e.g., pure sand and pure clay); however, field stratigraphy is normally in a layered distribution, which may alter the bearing mechanism of the pile–bucket foundation and lead to inconsistency in the prediction of the bearing capacity. In this study, three-dimensional finite element simulations are performed, and the static lateral bearing characteristics of pile–bucket foundation in layered sand–clay strata are investigated considering the impact of monopile size, bucket size, and sand/clay thickness ratio (H_s/H_c). Numerical results indicate that an increase in sand layer thickness could help increase the bearing capacity of the pile–bucket foundation, especially as H_s/H_c increases from 0.5 to 1. Then, hyperbolic p-y models are introduced to calculate the lateral load–displacement curves of the pile–bucket foundation, which is divided into three sections: the bucket section and pile section in sand and clay, respectively. The two controlling factors of the p-y model, the initial stiffness and the ultimate lateral resistance, are modified based on numerical results. The modified p-y model provides satisfactory predictions of the pile deflection and the moment distribution of pile–bucket in layered sand–clay strata and could serve as a framework in more complicated soil deposits, such as clay-over-sand or multi-layer. Full article

Offshore wind power cables may experience drag and lift loading where submarine landslides or related density flows cross a cable corridor. To prioritise conditions for detailed analysis, a leakage-controlled ensemble-learning workflow was developed from multi-source submarine landslide–pipeline/cable impact data and evaluated by source-grouped cross-validation. Separately predicted peak drag and peak lift were converted to empirical percentile ranks and averaged to form a dataset-relative peak-load severity index. The resulting Low–Extreme categories achieved accuracy of 0.748, macro F1 of 0.690 and High/Extreme recall of 0.758; binary High/Extreme identification achieved accuracy of 0.903, F1 of 0.800 and ROC-AUC of 0.947. Missingness analyses gave four-category accuracy of 0.717–0.771 across full, low-missingness and complete-case settings, whereas weight and threshold changes showed that category boundaries remain calibration choices rather than universal limits. Scenario and feature-group results indicate combined associations with hydrodynamic forcing, cable geometry, rheology and exposure/cover conditions. The output is intended to rank candidate conditions for computational fluid dynamics, physical modelling or structural verification. It is not a design load, a code check or a full risk estimate, which would additionally require occurrence probability, cable vulnerability, consequence and project-specific validation. Full article

This research proposes an optimization model for the scheduling of power generation expansion in countries with high hydrological variability, to be implemented in the Colombian system for the 2027–2041 period. The method combines a Vector Autoregressive (VAR) model, evaluated using daily series of inflows, demand, and exports for 2010–2024, with a two-stage stochastic optimization model. Unlike previous methods, the model maintains the contemporaneous covariance calculated when creating the planning scenarios. Hydrological inflows cause demand (p = 0.0003), which reflects the possible effect of the El Niño–Southern Oscillation (ENSO). Their contemporaneous correlation of −0.1018 comes from the dry months, which coincide with the months of higher consumption. The main contribution of this research lies in measuring the cost of disregarding this dependence. This is performed using two similar optimization problems that differ only in whether they maintain the covariance. In the base case, the plan based on the independence hypothesis adds 45% more capacity, replaces wind power with solar power, and requires an additional 950 million dollars compared with the scenario that maintains the correlation, representing 1.97% of the system’s optimal cost. Full article

Tea plantations of the Northeastern Himalayan region of India represent major tea-producing landscapes and support a large workforce. Their warm, humid climate, dense vegetation, and intensive processing activities generate diverse bioaerosols, including pollen, fungal spores, bacteria, and organic dust, with potential occupational respiratory health implications. This review synthesizes evidence on the aerobiological characteristics of tea-growing ecosystems and their association with respiratory allergy and other occupational respiratory disorders among tea workers. Literature from India and other major tea-producing regions was critically evaluated, focusing on the diversity, seasonal dynamics, and environmental determinants of airborne pollen and fungal spores, particularly in Assam, Darjeeling, Dooars, Terai, and adjoining Himalayan tea-growing areas. Fungal genera including Cladosporium, Aspergillus, Penicillium, Alternaria, and Curvularia, along with allergenic pollen from grasses, weeds, and surrounding vegetation, frequently constitute important components of the bioaerosol burden. Their abundance varies with temperature, humidity, rainfall, wind, and seasonal vegetation. Processing environments may further increase exposure through respirable tea dust and “tea fluff.” Available evidence from tea-worker studies indicates respiratory symptoms, ventilatory impairment, and allergic or sensitization responses associated with occupational tea-related exposures. However, direct evidence linking specific airborne pollen or fungal taxa detected in tea-growing environments to specific respiratory outcomes in tea workers remains limited. The available literature therefore supports a distinction between direct aerobiological observations, occupational exposure evidence, and clinical evidence, rather than assuming a direct exposure–disease relationship and highlights the need for standardized monitoring, respiratory health surveillance, engineering controls, and evidence-based workplace policies. Full article

ERA5 100 m wind speed provides long-term and spatially continuous information for regional wind-resource assessment, but its grid-scale representation may introduce terrain- and wind-regime-dependent biases in complex terrain. This study evaluated ERA5 using 459,630 valid hourly observation–reanalysis pairs from 64 wind masts and developed a segmented residual-correction framework incorporating wind-regime, terrain, location, and temporal predictors. Three statistical correction methods and four tree-based models were compared under identical training and validation samples. The main analysis used a stratified random holdout, supplemented by chronological and tower-level spatial holdouts and repeated high-wind experiments. In the random validation subset, raw ERA5 yielded R = 0.669, R² = 0.307, RMSE = 2.261 m s⁻¹, MAE = 1.664 m s⁻¹, and ME = −0.904 m s⁻¹. Random forest achieved the best paired hourly performance, increasing R to 0.850 and reducing RMSE and MAE to 1.433 and 1.078 m s⁻¹, respectively. Its RMSE reductions remained positive but decreased to 14.15% and 14.47% under chronological and spatial holdouts. Terrain relief was strongly associated with tower-level raw ERA5 errors, although its controlled inclusion produced only a modest additional RMSE reduction of approximately 0.7%. Pooling the >9 m s⁻¹ training tail improved high-wind stability and outperformed a separately trained >12 m s⁻¹ model in 17 of 20 experiments. Random forest was preferable for paired hourly reconstruction, whereas quantile mapping more closely reproduced pooled theoretical wind-energy indicators. These results show that ERA5 bias correction should be selected according to the intended application and evaluated using complementary temporal, spatial, distributional, and high-wind diagnostics. Full article

Energetic neutral atoms (ENAs) are generated by charge exchange process during collisions between energetic ions and neutral particles. Due to their electrical neutrality, ENAs are unaffected by electromagnetic fields and can travel along ballistic trajectories. Compared with in situ ion measurements, ENA observations enable large-scale remote imaging and provide an important means of understanding the interactions between solar wind plasma and planets. However, their angular resolution and field of view are generally very limited, which severely restricts research based on ENA observations. In this study, we propose a scanning observation and data-processing method that provides finer angular sampling of ENA measurements during spacecraft maneuvering and use it to investigate the angular distribution of H-ENAs. Using hydrogen ENA (H-ENA) data from the Mars Ion and Neutral Particle Analyzer (MINPA) aboard China’s Tianwen-1 spacecraft, we obtained an angular sampling interval of approximately ${0.1}^{\circ }$ during scanning, compared with the instrument’s intrinsic polar angular response width of ${9.7}^{\circ }$. The results show that the H-ENA population exhibits a velocity of $342.2\pm 81$ km/s and a temperature of $4.2\pm 0.5$ eV, whereas the upstream solar wind ${\mathrm{H}}^{+}$ have a velocity of $352.2\pm 3.2$ km/s and a temperature of $3.3\pm 0.02$ eV. The angular distribution of H-ENAs is highly consistent with that of the upstream solar wind ${\mathrm{H}}^{+}$. These observations are consistent with solar wind ${\mathrm{H}}^{+}$ as an important source of H-ENAs at Mars and with charge exchange as the production process. This study obtained a finely sampled angular distribution of solar wind H-ENAs and provides a method for future planetary ENA observations. Full article

Bisphenol A (BPA) epoxy resins are widely used because of their excellent thermal and mechanical properties but are difficult to recycle owing to their highly crosslinked network. This study investigated the effect of curing-agent chemistry on epoxy resin degradation and BPA recovery, with emphasis on chemical recycling of legacy thermoset waste. Three amine curing agents, ethylenediamine (EDA), 1,2-diaminocyclohexane (DCH), and p-phenylenediamine (PPD), together with methyl tetrahydrophthalic anhydride (MTHPA), were evaluated, and a commercial wind blade epoxy resin was used as a representative waste material. Ethanol was selected as the degradation solvent because it combined effective degradation under the tested conditions with lower toxicity than several screened alternatives. The highest BPA recovery from the wind blade resin reached 31.79% at 170 °C after 24 h. MTHPA- and DCH-cured resins achieved BPA recoveries of 45.38% and 44.38%, respectively, whereas EDA and PPD exhibited lower degradability because of network and bond-stability effects. By linking curing-agent structure to depolymerization and monomer recovery, the study supports waste prevention and resource circularity objectives relevant to Sustainable Development Goal 12. Full article

Microbursts can cause severe low-level wind shear, posing a threat to aviation safety. However, due to their small scale, they are difficult to capture by conventional equipment such as surface Automated Weather Observing Systems (AWOS) and Doppler weather radar. On 25 June 2022, a microburst event occurred at Urumqi Diwopu International Airport. Based on multi-source and high-resolution observation data, including AWOS data, microwave radiometer data and Doppler wind lidar data, an analysis was conducted on the characteristics of this microburst event. The results indicate that before the occurrence of this microburst, the atmosphere was in an unstable state, where the lower atmosphere exhibited a significant dry adiabatic lapse rate, and the temperature and humidity profiles displayed a typical “inverted-V” thermodynamic structure. Approximately 10 min prior to the occurrence of the microburst, the Convective Available Potential Energy (CAPE), Downdraft Convective Available Potential Energy (DCAPE), Microburst Wind Potential Index (MWPI), and WINDEX increased rapidly and reached their peak values, with CAPE and DCAPE reaching 4967 J·kg⁻¹ and 1004 J·kg⁻¹, respectively, MWPI reaching 3.0, and WINDEX reaching 26.2 m·s⁻¹, before subsequently decreasing rapidly. During the transition from rapid accumulation to release of the unstable energy, precipitation particles from aloft passed through the dry and warm low-level region, undergoing evaporative cooling that enhanced negative buoyancy. This effect, combined with precipitation loading, facilitated the accelerated descent and ground impact of the dry-cold air intruding from the mid-to-lower troposphere. The Doppler wind lidar data showed that the downdraft first formed at an altitude of approximately 1.5 km and rapidly extended toward the ground, reaching a maximum descent speed of 4.78 m·s⁻¹. After impinging on the ground, the downdraft produced a pronounced divergent outflow, with the maximum surface wind speed reaching 25.5 m·s⁻¹, which exceeded the operational downburst gust threshold of 17.9 m·s⁻¹. This evolution of the microburst was consistent with the observed ground pressure surge, rapid temperature drop, and downward momentum transport aloft. Integrated observations from microwave radiometers and Doppler wind lidar can effectively reveal the thermodynamic stratification and dynamic characteristics of microburst, offering valuable reference for airport microburst monitoring and nowcasting early warning. Full article