Search Results (462)

With the accelerating global transition toward sustainable energy systems, power grids with a high share of renewable energy face increasing challenges due to volatility and uncertainty, necessitating advanced flexibility resource planning and stability optimization strategies. This paper presents a comprehensive distribution network planning framework that coordinates and integrates multiple types of flexibility resources through joint optimization and network reconfiguration to enhance system adaptability and operational resilience. A novel virtual network coupling modeling approach is proposed to address topological constraints during network reconfiguration, ensuring radial operation while allowing rapid topology adjustments to isolate faults and restore power supply. Furthermore, to mitigate the uncertainty and fault risks associated with extreme weather events, a CVaR-based risk quantification framework is incorporated into a bi-level optimization model, effectively balancing investment costs and operational risks under uncertainty. In this model, the upper-level planning stage optimizes the siting and sizing of flexibility resources, while the lower-level operational stage coordinates real-time dispatch strategies through demand response, energy storage operation, and dynamic network reconfiguration. Finally, a hybrid SA-PSO algorithm combined with conic programming is employed to enhance computational efficiency while ensuring high solution quality for practical system scales. Case study analyses demonstrate that, compared to single-resource configurations, the proposed coordinated planning of multiple flexibility resources can significantly reduce the total system cost and markedly improve system resilience under fault conditions. Full article

Oil spill simulation models are essential for predicting the oil spill behavior and movement in marine environments. In this study, we comprehensively reviewed a large and diverse body of peer-reviewed literature obtained from Scopus and Web of Science. Our initial analysis phase focused on examining trends in scientific publications, utilizing the complete dataset derived after systematic screening and database integration. In the second phase, we applied elements of a systematic review to identify and evaluate the most influential contributions in the scientific field of oil spill simulations. Our analysis revealed a steady and accelerating growth of research activity over the past five decades, with a particularly notable expansion in the last two. The field has also experienced a marked increase in collaborative practices, including a rise in international co-authorship and multi-authored contributions, reflecting a more global and interdisciplinary research landscape. We cataloged the key modeling frameworks that have shaped the field from established systems such as OSCAR, OIL-MAP/SIMAP, and GNOME to emerging hybrid and Lagrangian approaches. Hydrodynamic models were consistently central, often integrated with biogeochemical, wave, atmospheric, and oil-spill-specific modules. Environmental variables such as wind, ocean currents, and temperature were frequently used to drive model behavior. Geographically, research has concentrated on ecologically and economically sensitive coastal and marine regions. We conclude that future progress will rely on the real-time integration of high-resolution environmental data streams, the development of machine-learning-based surrogate models to accelerate computations, and the incorporation of advanced biodegradation and weathering mechanisms supported by experimental data. These advancements are expected to enhance the accuracy, responsiveness, and operational value of oil spill modeling tools, supporting environmental monitoring and emergency response. Full article

As global climate change accelerates, the urban heat island (UHI) phenomenon has become increasingly pronounced, posing significant challenges to urban energy balance, atmospheric processes, and public health. This study used the Weather Research and Forecasting (WRF) model to dynamically downscale two CMIP6 scenarios—moderate forcing (SSP245) and high forcing (SSP585)—focusing on Zhengzhou, a rapidly urbanizing city in central China. High-resolution simulations captured fine-scale intra-urban temperature patterns and analyze the spatial and seasonal variations in UHI intensity in 2030 and 2060. The results demonstrated significant seasonal variations in UHI effects in Zhengzhou for both 2030 and 2060 under SSP245 and SSP585 scenarios, with the most pronounced warming in summer. Notably, under the SSP245 scenario, elevated autumn temperatures in suburban areas reduced the urban–rural temperature gradient, while intensified rural cooling during winter enhanced the UHI effect. These findings underscore the importance of integrating high-resolution climate modeling into urban planning and developing targeted adaptation strategies based on future UHI patterns to address climate challenges. Full article

Weathering steel (WS) is widely used in bridge construction due to its high corrosion resistance, durability, and low maintenance requirements. This paper reviews the performance of WS bridges in Canadian climates, focusing on the formation of protective patina, influencing factors, and long-term maintenance strategies. The protective patina, composed of stable iron oxyhydroxides, develops over time under favorable wet–dry cycles but can be disrupted by environmental aggressors such as chlorides, sulfur dioxide, and prolonged moisture exposure. Key alloying elements like Cu, Cr, Ni, and Nb enhance corrosion resistance, while design considerations—such as drainage optimization and avoidance of crevices—are critical for performance. The study highlights the vulnerability of WS bridges to microenvironments, including de-icing salt exposure, coastal humidity, and debris accumulation. Regular inspections and maintenance, such as debris removal, drainage system upkeep, and targeted cleaning, are essential to mitigate corrosion risks. Climate change exacerbates challenges, with rising temperatures, altered precipitation patterns, and ocean acidification accelerating corrosion in coastal regions. Future research directions include optimizing WS compositions with advanced alloys (e.g., rare earth elements) and integrating climate-resilient design practices. This review highlights the need for a holistic approach combining material science, proactive maintenance, and adaptive design to ensure the longevity of WS bridges in evolving environmental conditions. Full article

The influence of the copper (Cu) content on the corrosion resistance of 3.5%Ni low-carbon weathering steel was investigated using periodic dry–wet cycle accelerated corrosion tests. The mechanical properties of the steels were assessed via tensile and low-temperature impact tests, while corrosion resistance was evaluated based on weight loss measurements. Surface oxide layers were characterized using three-dimensional laser confocal microscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and electrochemical methods. Electron probe microanalysis (EPMA) was employed to examine the cross-sectional morphology of the oxide layer after 72 h of accelerated corrosion tests. The results indicate that the solution state of Cu increased the strength of 3.5%Ni steels but significantly damaged the low-temperature toughness. As the Cu content increased from 0.75% to 1.25%, the corrosion rate decreased from 4.65 to 3.74 g/m² h. However, when there was a further increase in the Cu content to 2.15%, there was little decrease in the corrosion rate. With the increase in the Cu content from 0.75% to 2.15%, the surface roughness of 3.5%Ni weathering steel after corrosion decreased from 5.543 to 5.019 μm, and the corrosion behavior was more uniform. Additionally, the α/γ protective factor of the oxide layer of the surface layer increased from 2.58 to 2.84 with an increase in the Cu content from 0.75% to 1.25%, resulting in the oxide layer of the surface layer being more protective. For 1.25%Cu steel, the corrosion current density of rusted samples is lower (ranging from 1.2609 × 10⁻⁴ A/cm² to 3.7376 × 10⁻⁴ A/cm²), and the corrosion potential is higher (ranging from −0.85544 V to −0.40243 V). Therefore, the rusted samples are more corrosion resistant. The Cu in the oxide layer of the surface layer forms CuO and CuFeO₂, which are helpful for increasing corrosion resistance, which inhibits the penetration of Cl⁻. Full article

Under accelerating global warming, the northern slope of the Middle Kunlun Mountains in Xinjiang, China, has seen a marked rise in extreme rainfall, posing increasing challenges for flood risk management and water resources. To improve our predictive capabilities and deepen our understanding of the driving mechanisms, we combine the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) reanalysis, regional observations, and high-resolution Weather Research and Forecasting model (WRF) simulations to dissect the 14–17 June 2021, extreme rainfall event. A deep Siberia–Central Asia trough and nascent Central Asian vortex established a coupled upper- and low-level jet configuration that amplified large-scale ascent. Embedded shortwaves funnelled abundant moisture into the orographic basin, where strong low-level moisture convergence and vigorous warm-sector updrafts triggered and sustained deep convection. WRF reasonably replicated observed wind shear and radar echoes, revealing the descent of a mid-level jet into an ultra-low-level jet that provided a mesoscale engine for storm intensification. Momentum–budget diagnostics underscore the role of meridional momentum transport along sloping terrain in reinforcing low-level convergence and shear. Together, these synoptic-to-mesoscale interactions and moisture dynamics led to this landmark extreme-precipitation event. Full article

This study focuses on the influence of prolonged ultraviolet (UV) irradiation on the mechanical properties and surface microstructure of glass fiber-reinforced plastics (GFRPs) and basalt fiber-reinforced plastics (BFRPs), which are widely used in construction and transport infrastructure. The relevance of the research lies in the need to improve the reliability of composite materials under extended exposure to harsh climatic conditions. Experimental tests were conducted in a laboratory UV chamber over 2000 h, simulating accelerated weathering. Mechanical properties were evaluated using three-point bending, while surface conditions were assessed via profilometry and microscopy. It was shown that GFRPs exhibit a significant reduction in flexural strength—down to 59–64% of their original value—accompanied by increased surface roughness and microdefect depth. The degradation mechanism of GFRPs is attributed to the photochemical breakdown of the polymer matrix, involving free radical generation, bond scission, and oxidative processes. To verify these mechanisms, FTIR spectroscopy was employed, which enabled the identification of structural changes in the polymer phase and the detection of mass loss associated with matrix decomposition. In contrast, BFRP retained up to 95% of their initial strength, demonstrating high resistance to UV-induced aging. This is attributed to the shielding effect of basalt fibers and their ability to retain moisture in microcavities, which slows the progress of photo-destructive processes. Comparison with results from natural exposure tests under extreme climatic conditions (Yakutsk) confirmed the reliability of the accelerated aging model used in the laboratory. Full article

Accurate, energy-efficient climate regulation is crucial for scaling smart greenhouse production. While nonlinear model predictive control (NMPC) can co-optimize yield and resource use, its efficacy hinges on short-range weather information and real-time solver feasibility. This paper investigates the performance of advanced NMPC strategies for smart greenhouse climate control, with particular emphasis on the roles of AI-driven disturbance prediction and warm-start initialization for real-time optimization. Six controller configurations, including feedback-only, LSTM-based forecast, and ideal disturbance models, each with and without warm-start, were tested in a 40-day simulation of a lettuce smart greenhouse. Performance metrics included final biomass, constraint violations, resource costs, profit, and solver time. Results show that feedback-only controllers maximize yield and profit, incurring higher CO₂ costs but lower heating costs, alongside greater constraint violations compared to the predictive strategies. Predictive and ideal disturbance-aware controllers effectively reduce resource consumption and improve constraint compliance at the expense of lower yields. Importantly, warm-start initialization significantly accelerates computation without affecting control quality. The study also demonstrates that penalty parameters, rather than economic weight settings, predominantly determine aggregate constraint violation. The findings provide actionable insights for designing and deploying NMPC-based greenhouse controllers, highlighting the importance of warm-start techniques and the trade-offs between productivity, resource efficiency, and environmental compliance. Full article

Thermal modification applies to a technique for the enhancement of biological durability, stability, and appearance of wood. Much is known about its effects on the chemical and physical attributes of wood. However, there is a knowledge gap concerning the effects of heat treatment on surface coating performance of different wood species. The focus of this research is heat treatment regulation of 160 °C, 180 °C, and 200 °C for three commercially important wood species which are Populus (poplar), Quercus (oak), and Pinus (pine). These treatments were evaluated in relation to coating performance indicators adhesion, integrity, and visual stability during and after natural and artificial weathering. It was revealed that specific responses among species differences exist. Populus behaved differently and exhibited a steady loss in mass and volume. Quercus demonstrated gradual degradation alongside enhanced lignin stability. Pinus exhibited maintenance of volume and mass until 180 °C after which accelerated degradation was observed. Coating durability and adhesion exhibited dependence on thermal condition, wood species, porosity, surface chemistry and microstructural variations that occurred. The research results can be used to streamline finishing processes for thermally modified wood while underscoring the critical nature of precise treatment parameter adjustments guided by species-specific responses to ensure long-term stability. Full article

Coal-fired power plants exacerbate hazy weather under low winter temperatures, while sulphate ions (SO₄²⁻) in condensable particulate matter (CPM) emitted from ultra-low emission coal-fired power plants accelerate sulphate formation. The transformation of gaseous precursors (SO₂, NOx, NH₃) is the main pathway for sulphate formation by homogeneous or non-homogeneous reactions. For the sustainability of the world, in this paper, the effects of condensation temperature, H₂O, NO_X and NH₃ on the SO₄²⁻ generation characteristics under low-temperature rapid condensation conditions are investigated. With lower temperatures, especially from 0 °C cooling to −20 °C, the concentration of SO₄²⁻ was as high as 26.79 mg/m³. With a greater proportion of H₂SO₄ in the aerosol state, and a faster rate of sulphate formation, H₂O vapour condensation can provide a reaction site for sulphuric acid aerosol generation. SO₄²⁻ in CPM is mainly derived from the non-homogeneous reaction of SO₂. SO₃ is an important component of CPM and provides a reaction site for the formation of SO₄²⁻. SO₂ and SO₃, in combination with Stefan flow, jointly play a synergistic role in the generation of SO₄²⁻. The content of SO₄²⁻ was as high as 36.18 mg/m³. While NO_X sometimes inhibits the formation of SO₄²⁻, NH₃ has a key role in the nucleation process of CPM. NH₃, SO₂ and NO_X have been found to rapidly form sulphate with particle sizes up to 5 µm at sub-zero temperatures and promote the formation of sulphuric acid aerosols. Full article

To investigate the corrosion resistance of 60Si2MnA spring steel coated with Zn-Al in a domestic atmospheric environment containing harmful salts, the corrosion environmental factors (temperature, humidity, deposited salts, and pH) were obtained through field research. The deliquescence and weathering behavior of harmful salts were studied using impedance methods to establish their characteristic curves. Additionally, a self-designed salt deposition test apparatus was employed to conduct accelerated atmospheric corrosion tests under constant salt deposition (10 g/m²) and controlled temperature and humidity conditions (20 °C/75% RH and 40 °C/75% RH) over different corrosion periods. The results show that noticeable red rust appeared on the samples after one month of corrosion. As the temperature increased, the consumption of the coating accelerated. XRD and Raman analyses reveal that the main corrosion products of the coating materials were ZnO, Zn(OH)₂, and Zn₅(CO₃)₂(OH)₆, while the red rust primarily consisted of iron oxides and hydroxides. In the early stages of corrosion, the self-corrosion current density was relatively low due to the protective effects of the coating and the corrosion product layer, indicating good corrosion resistance. However, in the later stages, the integrity of the coating and the corrosion product layer deteriorated, leading to a significant increase in the self-corrosion current density and a decline in corrosion resistance. This study provides a data foundation for understanding the corrosion behavior of Zn-Al-coated spring steel in atmospheric environments and offers theoretical insights for developing more corrosion-resistant coatings and optimizing anti-corrosion measures. Full article

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. Full article

As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities. Full article

This study addresses the dynamic response control of deep-water jacket offshore platforms under typhoon and misaligned wave loads by proposing a Tuned Mass Damper (TMD)-based vibration suppression strategy. Typhoon loading is predicted using the Weather Research and Forecasting (WRF) model to simulate maximum wind speed and direction, a customized exponential wind profile fitted to WRF results, and a spectral model calibrated with field-measured data. Correspondingly, typhoon wave loading is calculated using stochastic wave theory with the Joint North Sea Wave Project (JONSWAP) spectrum. A rigorous Finite Element Model (FEM) incorporating soil–structure interaction (SSI) and water-pile interaction is implemented in the Opensees platform. The SSI is modeled using nonlinear Beam on Nonlinear Winkler Foundation (BNWF) elements (PySimple1, TzSimple1, QzSimple1). Numerical simulations demonstrate that the TMD effectively mitigates dynamic platform responses under aligned typhoon and wave conditions. Specifically, the maximum deck acceleration in the X-direction is reduced by 26.19% and 31.58% under these aligned loads, with a 17.7% peak attenuation in base shear. For misaligned conditions, the TMD exhibits pronounced control over displacements in both X- and Y-directions, achieving reductions of up to 29.4%. Sensitivity studies indicated that the TMD’s effectiveness is more significantly impacted by stiffness detuning than mass detuning. It should be emphasized that the effectiveness verification of linear TMD is limited to the load levels within the design limits; for the load conditions that trigger extreme structural nonlinearity, its performance remains to be studied. This research provides theoretical and practical references for multi-directional coupled vibration control of deep-water jacket platforms in extreme marine environments. Full article

In recent years, the increasing frequency of extreme weather events has led to a rise in unplanned unit outages, posing significant risks to the safe operation of power systems and underscoring the critical need for accurate prediction and effective mitigation of wind and solar power ramp events. Unlike traditional power forecasting, ramp event prediction must capture the abrupt output variations induced by short-term meteorological fluctuations. This review systematically examines recent advancements in the field, focusing on three principal areas: the definition and detection of ramp event characteristics, innovations in predictive model architectures, and strategies for precision optimization. Our analysis reveals that while detection algorithms for ramp events have matured and the overall predictive performance of power forecasting models has improved, existing approaches often struggle to capture localized ramp phenomena, resulting in persistent deviations. Moreover, current research highlights the necessity of developing evaluation systems tailored to the specific operational hazards of ramp events, rather than relying solely on conventional forecasting metrics. The integration of artificial intelligence has accelerated progress in both event prediction and error correction. However, significant challenges remain, particularly regarding the interpretability, generalizability, and real-time applicability of advanced models. Future research should prioritize the development of adaptive, ramp-specific evaluation frameworks, the fusion of physical and data-driven modeling techniques, and the deployment of multi-modal systems capable of leveraging heterogeneous data sources for robust, actionable ramp event forecasting. Full article