Search Results (1,925)

With the increasing occurrence of extreme high-temperature weather events, the traditional bundled operation of wind power (WP), photovoltaic power (PV), and pumped storage hydropower (PSH) is facing dual challenges, namely intensified renewable energy fluctuations and insufficient flexible regulation capability of PSH. Therefore, this paper proposes an optimal scheduling strategy for bundled operation based on capacity interval matching of PSH with WP and PV under extreme high-temperature weather. First, typical scenarios are generated based on a Time-series Generative Adversarial Network (TimeGAN), and an interval matching transaction model is established based on the forecast intervals of WP and PV capacity and the corrected intervals of PSH capacity. Second, considering PSH as an independent market entity, a bi-level optimization model is constructed, in which the upper-level objective is to maximize the revenue of PSH, while the lower-level objective is to minimize the total cost of the joint clearing of the energy and ancillary service markets. Finally, simulation case studies verify that under extreme high-temperature weather, the proposed optimal scheduling method increases the bundled operation capacity by 17.9% and improves the revenue of PSH in the reserve ancillary service market by 14.8%, thereby effectively enhancing the economic performance of PSH while ensuring the safe and stable operation of the system. Full article

Harmful algal blooms (HABs) caused by Karenia brevis (K. brevis) present a persistent ecological and public health challenge across coastal Florida. Reliable bloom forecasting is critical for protecting public health, supporting coastal economies, and enabling timely management responses. This study develops a regionally integrated machine learning framework to predict weekly K. brevis bloom occurrence using environmental data from both the Peace and Caloosahatchee Rivers, combined with coastal bloom records from Southwest Florida and Tampa Bay to enhance the spatial and temporal continuity of the response record. A Random Forest classifier was trained on a multi-decadal dataset incorporating river discharge, nutrient concentrations (total nitrogen and total phosphorus), wind forcing, sea surface temperature, salinity, and sea surface height anomalies as a proxy for Loop Current variability. The model achieved strong predictive performance on a chronologically withheld test set, with an overall accuracy of ~90%, balanced accuracy of 87.6%, and ROC–AUC of 0.972, indicating strong discrimination between bloom and non-bloom conditions with high precision and recall for bloom events. Bloom timing and persistence were captured with strong agreement during ongoing bloom periods, while non-bloom conditions were identified with low false-positive rates. Feature-response analyses indicated that bloom probability increased most sharply under moderate discharge and nutrient conditions, with diminished sensitivity at higher extremes. Learning curve analysis demonstrated robust training performance and stable generalization, with validation accuracy plateauing near 84%, suggesting a data-limited ceiling on forecast skill. By aggregating nutrient inputs across multiple watersheds and integrating spatially aligned bloom observations, this study demonstrates the utility of multi-source machine learning frameworks for regional-scale HAB prediction. The results support the development of early warning tools and provide a reproducible foundation for evaluating how combined watershed loading and physical forcing are associated with K. brevis bloom occurrence in complex estuary systems with watershed and coastal coupling. Full article

As power-electronic-interfaced renewable generation displaces synchronous machines, modern power systems face coupled day-ahead challenges: net-load variability demands peak shaving, while declining inertia necessitates explicit frequency-regulation scheduling. In sequential security-constrained unit commitment (SCUC) and Security-Constrained Economic Dispatch (SCED), the reserve procured in SCUC may lose deliverability after redispatch because the same storage bandwidth is reassigned to energy service. This paper proposes a two-stage day-ahead framework that addresses both challenges for low-inertia systems with high inverter-based resource (IBR) penetration. Stage I embeds Rate-of-Change of Frequency (RoCoF), frequency nadir, and quasi-steady-state (QSS) constraints in SCUC, with a piecewise-linear outer approximation for the non-convex nadir limit. Stage II strictly inherits the SCUC commitment and reserve reservation, and it applies bandwidth deduction to prevent peak-shaving redispatch from consuming committed frequency reserve. A technology-aware partition further assigns fast-response Lithium Iron Phosphate (LFP) batteries to sub-second frequency support and long-duration Vanadium Redox Flow Batteries (VRFBs) to energy shifting. Evaluated under the adopted reduced-order frequency-response framework and disturbance representation, tests on a modified IEEE 39-bus system under an extreme-wind scenario demonstrate that explicit frequency constraints eliminate all post-contingency violations, the inheritance mechanism closes a 23.85 MW reserve gap after redispatch, and heterogeneous storage partitioning preserves essentially the same disturbance sensitivity while increasing the peak-shaving ratio to 45.85%, lowering the day-ahead cost to CNY $10.483\times {10}^6$ and reducing the average system price to 209.33 CNY/MWh. Full article

Targeting the “Fishtailing Effect” associated with shallow-water, pile-founded column single point mooring (SPM) systems, this study investigates the vessel’s motion characteristics under multiple operational scenarios using a numerical calculation method validated by model tests. A refined classification of combined wind, wave, and current conditions was conducted. The study examines the vessel’s sway and mooring line tension response under both collinear and non-collinear combinations of these environmental forces. Furthermore, methods for suppressing vessel motion were explored. The results indicate that vessel motion leading to the “Fishtailing Effect” is more prone to occur under collinear wind, wave, and current conditions. Wave and wind energy can, to some extent, mitigate the vessel motion. When the current speed exceeds a certain critical threshold, the extreme values of the mooring forces on the swaying vessel undergo an abrupt change. Applying a stern tug force and reducing the mooring line length are both effective in decreasing the vessel motion range and the tension in the mooring lines. The findings shed light on the fishtailing-effect characteristics of tankers moored at pile-founded column SPM systems, providing a valuable reference for the safety and stability design of such mooring systems. Full article

This study presents a comprehensive assessment of historical and projected variability in the wind climate over Ireland and its adjacent marine regions, including the North Atlantic Ocean, Irish Sea, and Celtic Sea. First, the long-term wind characteristics are examined using the ERA5 reanalysis dataset for the historical period (1979–2008), followed by an evaluation of five CMIP6 Global Climate Models (GCMs) to determine their performance in representing regional wind climatology. Based on spatial validation and relative bias analyses, the most suitable model is selected to investigate future wind changes under the SSP2-4.5 and 5-8.5 scenarios. The CMIP6 historical data is also compared locally at seven measurement stations. Two future projections are considered for the near-term (2031–2060) and mid-term (2071–2100), focusing on inter- and intra-annual variability and extreme wind behaviour. The results indicate an overall decrease in mean wind speed across the study area, with more pronounced reductions under SSP5-8.5 and during the mid-term period. In terms of seasonality, reductions are more pronounced during winter and summer than in the transitional seasons. According to the extreme value analysis based on the generalised extreme value distribution, general declines in extreme values are detected at selected critical locations, especially at wind speeds with large return periods. Full article

The Zhuanghe coast in the northern part of the Yellow Sea is one of China’s important fishing and ocean engineering areas. Frequent storm surge events pose a significant threat to residents’ safety and properties. This study used the coupled Finite Volume Coastal Ocean Model (FVCOM) and the Surface Wave Model (FVCOM-SWAVE) to investigate storm surges and wave heights during Typhoons Muifa (1109) and Lekima (1909) in the northern parts of the Yellow Sea and analyze the impact of the typhoon parameters on flood inundation on the Zhuanghe coast. The wind stress comparison in the coupled wave–current model uses synthetic wind field data formed by superimposing ERA5 wind fields with a parameterized typhoon model. The results showed that the simulated and measured tide levels, wave heights, and storm surges were in good agreement, indicating that the coupled model accurately reproduced the dynamics of the storm surges and wave heights during the two typhoons. The maximum significant wave height (H_s) exhibited a right-skewed distribution in the two typhoons’ paths, with extreme values consistently located to the right of the typhoon’s center. The decrease in atmospheric pressure at the center of Typhoon Muifa was significantly, nonlinearly, and positively correlated with the severity of storm surge disasters. A significant correlation was observed between the path of Typhoon Muifa and the disaster intensity. Full article

This work presents an experimental aerodynamic study of a Mars rover model, aimed at characterizing its flow behavior under Martian environmental conditions. Due to the extremely low Reynolds numbers associated with Mars’ thin atmosphere, the experiments were conducted using a scaled model of the rover manufactured via additive techniques. The study first focuses on understanding how the geometry of the rover influences the overall flow field, identifying key aerodynamic features such as separation zones, vortical structures, and flow reattachment regions driven by the complexity of the vehicle. A comprehensive investigation of the flow around the model was performed using both a hydrodynamic towing tank with dye injection for qualitative visualization, and particle image velocimetry (PIV) for quantitative flow field analysis in wind tunnel tests. After the general flow characterization, a more detailed local analysis was conducted using laser Doppler anemometry (LDA). This phase of the study targeted precise velocity measurements at specific locations corresponding to the MEDA (Mars Environmental Dynamics Analyzer) wind sensors onboard the rover. Quantitative results indicate that the central body induces a local flow acceleration of 20% to 40% relative to the free stream while severe turbulence was recorded in specific angular sectors, with velocity fluctuations reaching up to 120% for Sensor 1 and 90% for Sensor 2. Full article

Lake Erhai is an important plateau freshwater lake in China. It serves not only as a crucial drinking water source for the local region but also as the core area of the Cangshan Erhai National Nature Reserve. Consequently, Lake Erhai plays an extremely significant role in the local economy, society, and ecology. Since 2000, the water quality of Lake Erhai has continuously deteriorated, showing a eutrophic trend. To identify the primary driving forces behind these water quality changes, this study employed stepwise regression analysis. Climate conditions, socio-economic development within the basin, and implementation of environmental protection measures (IEPMs) were considered influencing factors for a comprehensive and systematic analysis of Lake Erhai’s water quality. The results indicate that rising air temperature may increase total phosphorus (TP) concentration, while rainfall may elevate both TP and total nitrogen (TN) levels. In contrast, higher wind speed may reduce chemical oxygen demand (COD_Mn), TP, and TN concentrations. Socio-economic development, meanwhile, may contribute to increased COD_Mn concentration. Based on these findings, this paper proposes recommendations focusing on formulating more effective non-point source pollution control measures and strengthening water quality monitoring in Lake Erhai during summer. By identifying the key natural and anthropogenic drivers of water quality changes in Lake Erhai, this study provides a scientific basis for the development of targeted pollution control strategies and directly contributes to the protection of clean water sources. Moreover, its revelation of the coupled impacts of climate change and socio-economic activities enhances understanding of plateau lake ecosystem resilience. This insight is critical for ensuring regional ecological security and serves as a model for advancing sustainable development goals in similar lake systems worldwide. Full article

Hybrid renewable energy systems, which combine photovoltaic panels, wind turbines, batteries, generators, and grid connections, require careful sizing to balance economic performance, renewable integration, and supply reliability. In this context, this study proposes a deep reinforcement learning (DRL)-based sensitivity analysis framework in which the admissible energy contributions from the diesel generator and the grid are treated as explicit design-control parameters. The objective is to simultaneously minimize the levelized cost of energy, minimize the loss of power supply probability, and maximize the renewable energy fraction. A sensitivity analysis was conducted across different HRES configurations, load profiles, and tau/gamma values. The performance of the DRL approach was compared with that of multi-objective particle swarm optimization and the non-dominated sorting genetic algorithm II under the same study setting. The results indicate that DRL can identify competitive trade-offs, especially under standard load conditions, while also providing insight into how admissible backup-energy constraints reshape techno-economic and reliability compromises. The best trade-offs were observed around intermediate tau and gamma values, suggesting that moderate backup-energy margins are more favorable than extreme values. These findings should be interpreted within the scope of a simulation-based study and provide comparative design-oriented evidence rather than universally transferable design rules. Full article

Under intensifying climate change and increasingly frequent extreme heat events, improving outdoor thermal environments has become critical for sustainable human settlements. While prior studies have mainly focused on urban contexts, systematic investigations of rural microclimates—particularly regarding the regulatory mechanisms of landscape configurations—remain limited. This study examines a mountain-adjacent village in the Loess Tableland region of China, integrating field measurements with ENVI-met simulations to analyze thermal characteristics of rural road spaces and the effects of vegetation and paving materials on human thermal comfort. The results show that village boundary areas experience the largest fluctuations in air temperature and relative humidity during midday and evening, indicating higher thermal sensitivity. Model validation demonstrates satisfactory accuracy, with RMSE values of 0.39–3.62 °C for air temperature, 1.32–3.22% for relative humidity, and 1.35–2.24 m/s for wind speed, and MAPE ranging from 0.80% to 9.05%. Furthermore, Basalt Brick and Populus alba show the best cooling performance, but when considering multiple factors such as temperature, humidity, and wind speed, Ligustrum lucidum has the most significant effects in improving thermal comfort and increasing humidity. Analysis based on Physiological Equivalent Temperature (PET) further indicates that vegetation configurations play a more substantial role in thermal comfort regulation than paving materials, and that different landscape elements exhibit synergistic and trade-off relationships in terms of cooling, humidification, and ventilation. This study provides quantitative reference for vegetation configuration and material selection in rural roads within the Loess Tableland region and similar semi-arid areas, enriches the research scope of rural microclimate studies, and offers scientific support for climate-adaptive rural planning and optimization of rural living environments. Full article

Angular misalignment of spherical roller bearings in wind turbine main shafts is a known cause of premature failure. Manufacturing and assembly tolerances introduce unavoidable variability in this misalignment—a source of uncertainty typically neglected in deterministic life models, thereby creating a gap between installation quality and system reliability. A probabilistic framework combining a Hertzian contact model, the Ioannides–Harris fatigue theory, and Monte Carlo simulation is developed to predict the fatigue life of double-row spherical roller bearings under uncertain misalignment. The sensitivity of eight geometric parameters, selected based on manufacturing tolerances, is quantified using Sobol indices for global sensitivity analysis, allowing their relative importance to be ranked. Application to a 950-series wind turbine main bearing under nominal and extreme loads shows that even with centered installation a non-negligible failure probability persists under nominal conditions. The strongly asymmetric bearing response requires asymmetrical installation tolerances to ensure high reliability. Global sensitivity analysis identifies the misalignment angle as the dominant source of uncertainty, followed by the roller contour radius. Under extreme loads, the bearing is under-dimensioned relative to the 20-year design life required for wind turbine main bearings, leading to a fatigue failure probability that approaches unity regardless of installation quality. The interaction between misalignment and radial clearance becomes pronounced under extreme overloads. Overall, the proposed framework provides a quantitative basis for reliability-based tolerance specification and emphasizes the necessity of considering the full load spectrum—including assembly variability—in bearing design. Full article

Air pollution forecasting is particularly challenging in basins with frequent winter seasons and temperature inversions. In this study, we developed and rigorously evaluated deep learning models to forecast PM₁₀ and the Air Quality Index (AQI) in Igdır, Türkiye, using a five-year, hourly dataset (2020–2024) from the Igdır/Central station (PM₁₀, NO₂, O₃, SO₂; meteorology: pressure, temperature, wind speed, relative humidity, precipitation, cloud cover). Using linear interpolation and Z-score normalization, sine/cosine features (hour, month) were used to encode temporal periodicity, and a 72-h lookback → 24-h look-ahead design was employed. LSTM, GRU, BiLSTM, and CNN-LSTM models were compared under a three-stage ablation (meteorology only; +cyclic encoders; +lagged targets), and their hyperparameters were tuned via Bayesian optimization. The deep learning results were further contextualized against a Multiple Linear Regression (MLR) baseline serving as a snapshot persistence model to evaluate the specific advantage of LSTM’s temporal memory in short-horizon forecasting. Multi-output forecasting is central to the proposed design, featuring a multi-task learning (MTL) framework based on a single shared temporal encoder with two task-specific regression heads that simultaneously predict PM₁₀ and AQI. Compared with separate single-task models, the multi-output setup exploits cross-target covariance (AQI’s dependence on pollutant loads under meteorology), improves data efficiency and generalization through shared representations, and promotes coherent, horizon-stable forecasts across targets, which is particularly valuable when winter stagnation regimes couple PM₁₀ and AQI dynamics. Moreover, this study introduces a structured ablation design to explicitly evaluate the added value of multi-output forecasting under inversion-dominated basin conditions. The results show stepwise gains from cyclic encoders and, most strongly, from lagged target histories. Under the optimized 24-h setting, LSTM performs best (R²_{PM₁₀} = 0.7989, RMSE = 48.74 µg/m³; R²_{AQI} = 0.6626, RMSE = 37.81), marginally surpassing GRU and clearly outperforming BiLSTM and CNN-LSTM. Horizon sensitivity confirms the benefit of nowcasting: when retrained for shorter horizons, LSTM attains R² = 0.9991 for PM₁₀ (MAE = 2.44; RMSE = 3.30 µg/m³) and 0.9535 for AQI (MAE = 4.87; RMSE = 14.03) at 1 h, and R² = 0.9792 (PM₁₀; MAE = 9.70; RMSE = 15.67) and 0.8849 (AQI; MAE = 11.19; RMSE = 22.08) at 6 h. Residual diagnostics reveal heteroskedastic, regime-dependent errors peaking near 0 °C and low winds, as well as a conservative bias that underpredicts extremes. Collectively, the findings show that multi-output, temporally aware deep models enable accurate operational forecasting in Igdır. The proposed framework provides real-time air quality alerts and daily planning, providing decision support for sustainable air quality management, public health protection, and evidence-based urban policy and is transferable to similar continental basin environments. Full article

Micro-scale counter-rotating wind turbines (CRWTs) offer enhanced potential for wake energy recovery. This study proposes an integrated cascade–coupling design framework for high-solidity CRWTs, in which rear rotor geometry and rotor coupling are co-designed based on stereoscopic particle image velocimetry measurements of the front rotor wake. Experiments are conducted at a tip-speed ratio of $\lambda =1.0$, solidity $\sigma =1.25$, spacing ratios of $d=0.6R_T$, $1.0R_T$, and $3.0R_T$, and a tip radius of $R_T=70$ mm. At the physical limit spacing of $d=0.6R_T$, the integrated design increases the system power coefficient by 24.1% while limiting front rotor power reduction to 17.2%, compared to a 10.3% system gain and 34.5% front rotor suppression for the baseline mirrored configuration. Wake measurements confirm near-complete absorption of rotational kinetic energy from the front rotor wake without exacerbating upstream interference. These results demonstrate that cascade-based energy extraction and coupling-based interference mitigation can operate synergistically, enabling compact, high-performance micro-scale CRWTs suitable for space-constrained and urban energy applications. Full article

This study investigates microclimatic variation across four environmentally and socially vulnerable neighborhoods in Philadelphia, utilizing ground-based measurements to assess urban heat (UH) and heat stress (HS). HS metrics, specifically Wet-Bulb Globe Temperature (WBGT) and heat index (HI), were calculated from UH measurements, including dry bulb and globe temperature, relative humidity, and wind speed. The methodology incorporates statistical modeling to identify significant predictors of HS, with street orientation (north–south and east–west) emerging as a key determinant, while categorical shade conditions were not statistically significant. Notably, Kingsessing exhibited lower HS and a unique humidity profile, whereas temperatures in Point Breeze and Grays Ferry and Hunting Park were consistently elevated. The research demonstrates that neighborhood-scale measurements can reveal critical spatial differences in UH and HS that are helpful in customizing mitigation strategies to specific communities. The approach is adaptable for integration with public health and emergency response initiatives, supporting data-driven decision-making for local governments and community-based organizations. Although assessment of physiological metrics and sampling during peak heat periods were not possible, overall, the study provides a practical framework for addressing urban heat vulnerability and underscores the importance of context-specific, community-engaged solutions to protect at-risk populations from extreme heat impacts. Full article

This study presents a comparative investigation of continuous and discrete design optimization for the fairlead chain stopper of large-scale 10 MW floating offshore wind turbines. The fairlead chain stopper plays a key role in ensuring mooring integrity, rapid port evacuation, and efficient maintenance under extreme weather conditions driven by global warming. The objective is to minimize structural weight while maintaining safety in accordance with the international classification rules of Det Norske Veritas. Three representative design load scenarios covering mooring and towing conditions are defined, and finite element analysis confirmed that the baseline design satisfies allowable stress limits. In the optimization stage, the thicknesses of nine principal components are selected as design variables. Continuous and discrete formulations are solved using particle swarm optimization, a non-dominated sorting genetic algorithm, and an evolutionary algorithm, and their convergence behavior and computational efficiency are compared. The results show that discrete optimization, which reflects actual manufacturing plate thicknesses, achieves nearly the same weight reduction as the continuous approach while offering superior practical applicability. Among the three techniques, the evolutionary algorithm provided the best convergence characteristics and attained up to 3.73 percent weight reduction. The proposed comparative methodology offers a useful guideline for rational weight-efficient design of core mooring equipment on large floating offshore wind power platforms. Full article