Search Results (235)

Unmanned sailboats rely on wind and solar energy for long-duration marine missions, yet their navigation performance is strongly constrained by sail aerodynamics, hull hydrodynamics, and time-varying wind fields. Due to their underactuated nature and the critical influence of sailing speed on safety and efficiency, this paper addresses the time-optimal global path planning problem for unmanned sailboats in complex marine environments. A customized velocity prediction program (VPP) is developed to accurately estimate sailing speeds under different true wind angles, explicitly accounting for the drift angle effect. A time-efficient cost function is constructed and integrated into the Rapidly exploring Random Tree Star (RRT*) algorithm. To improve computational efficiency, three strategies, including goal-biased sampling, artificial potential field guidance, and adjacent domain expansion, are introduced. The proposed hybrid algorithm imposes no restrictions on path step size or direction, ensuring asymptotic optimality while complying with sailboat dynamic constraints. Comparative simulations and validation tests using multiple VPP datasets demonstrate that the method is feasible, efficient, and widely applicable for unmanned sailboat navigation. Full article

The doubly fed induction generator (DFIG) has become one of the most widely adopted technologies in modern wind energy conversion systems due to its high efficiency, flexible operation, and capability to operate under variable wind-speed conditions. Nevertheless, maintaining high power quality, reducing harmonic distortion, and ensuring reliable operation under parameter uncertainties and grid disturbances remain major challenges. Conventional proportional–integral (PI)-based control approaches often suffer from limited robustness and sensitivity to system nonlinearities, which can adversely affect the dynamic performance and operational stability of wind energy systems. To address these limitations, this paper proposes an advanced hybrid control strategy based on the integration of fuzzy logic (FL) and a third-order sliding mode controller (TOSMC) for the control of DFIG-based wind turbines. The proposed FL–TOSMC combines the robustness and fast convergence properties of sliding mode control with the adaptive capability of fuzzy logic, thereby reducing dependence on accurate mathematical models and enhancing tolerance to parameter variations and external disturbances. The developed controller is applied to maximum power point tracking (MPPT) and direct field-oriented control of the DFIG to maximize energy extraction and ensure stable power regulation. The simulation results obtained in the MATLAB/Simulink (2021) environment demonstrate that the proposed strategy improves the dynamic response of the system by reducing overshoot, accelerating error convergence, minimizing steady-state oscillations, and decreasing total harmonic distortion compared with the conventional TOSMC approach. Furthermore, the proposed controller provides enhanced tracking performance, improved robustness against parameter uncertainties, and better power-quality characteristics under various operating conditions. These results indicate that the FL–TOSMC approach is an effective control solution for improving the operational performance of DFIG-based wind energy conversion systems. Full article

Accurate monitoring of internal winding temperature is essential for assessing the thermal state and operational reliability of oil-immersed transformers. However, direct deployment of distributed temperature sensors inside transformer windings is difficult because of insulation constraints, structural complexity, and potential reliability risks. To address this problem, this paper proposes a non-invasive internal winding temperature estimation method based on surface temperature sensing and a hybrid deep learning model. In the proposed framework, external surface temperature measurements are used as sensor inputs to infer the internal transient thermal state of the transformer. First, an extreme gradient boosting (XGBoost) model is employed to evaluate the contribution of different surface temperature measurement points and select the sensing locations that are most strongly correlated with internal winding temperature variations. Then, the selected surface temperature time-series data are used to train a Long Short-Term Memory (LSTM) network, which captures the temporal evolution of the transformer temperature field under different operating conditions. The proposed method is verified through both numerical simulation and experimental testing on a scaled single-phase oil-immersed converter transformer model (D-800/35) developed in this study. The results show that the proposed XGBoost-LSTM model can estimate internal winding temperature with an error of less than 1.5 K. Compared with direct internal sensing, the proposed method provides a non-invasive and sensor-efficient solution for internal temperature monitoring. The results demonstrate its potential for real-time thermal state estimation, condition monitoring, and fault diagnosis of oil-immersed converter transformers. Full article

In wind tunnel testing, an active vibration suppression system based on piezoelectric actuators is an effective means to ensure stable operation. However, in a cryogenic wind tunnel testing environment, the performance of piezoelectric actuators degrades significantly when they are exposed to cold temperatures and subjected to uneven cooling. This is particularly problematic during real-time changes in the attack angle of a test model. To ensure the reliable operation of wind tunnel tests, an active–passive hybrid thermal control method is proposed in this paper. First, the insulation and heating structure was designed based on the thermal analysis results. Then, combining simulation and measured data, the temperature field was reconstructed in real time using a recurrent neural network algorithm. Next, considering the non-uniform heat dissipation of the system, a thermal allocation module was designed based on digital–physical integration to actively control the overall and localized heat. Finally, a heat preservation performance test platform was established to conduct cooling experiments in a small-scale cryogenic wind tunnel. The results indicated that the proposed thermal control method reduced the average cooling rate of the system by 97% and improved the overall temperature uniformity by approximately 94.23%. Full article

Offshore renewable energy is widely recognised as a critical pathway for decarbonising electricity systems, but the integration of floating offshore wind turbines with wave energy converters remains technically challenging. This paper presents a structured literature review of hybrid wave–wind offshore energy platforms, drawing on 114 reviewed sources published between 2000 and 2026. The review classifies hybrid concepts using a three-axis framework based on floating platform type, wave energy converter (WEC) integration approach, and energy-dominance category. It then compares representative configurations, including point absorbers, oscillating water columns, flap-type devices, and heaving torus concepts, with emphasis on hydrodynamic response, energy contribution, structural complexity, mooring implications, validation status, and optimization suitability. The findings show that no single hybrid configuration can be ranked as universally superior because reported performance depends strongly on platform geometry, WEC scale, site wave climate, modelling assumptions, and validation maturity. Point absorber systems offer modularity and lower integration complexity, oscillating water column (OWC)-based systems provide protected power take-off (PTO) integration and moderate hydrodynamic interaction, flap-type systems can provide stronger motion-control potential but impose higher structural and mooring demands, and spar–torus concepts remain geometrically compatible with spar platforms but are generally wind-dominated. The review further shows that optimization method selection should depend on problem class: gradient-based methods are most suitable for local PTO tuning, evolutionary methods for non-convex multi-objective layout problems, surrogate-based methods for high-cost coupled simulations, and data-driven methods for adaptive control. The paper concludes that future progress requires standardized benchmark models, transparent evidence-level reporting, multi-physics co-optimization, techno-economic assessment, and systematic experimental or field validation before definitive concept ranking or commercial-readiness claims can be made. For decision-makers, industry stakeholders, and policymakers, the framework supports early-stage concept screening, identification of technology-specific risk factors, prioritisation of validation and investment pathways, and alignment of hybrid-platform development with site conditions, infrastructure constraints, and policy objectives. Full article

Accurate measurements of wind fields in the troposphere and stratosphere are essential for advancing atmospheric dynamics research, improving weather prediction, and supporting aerospace operations. However, a single Doppler lidar technique usually has limited capability to provide vertically extended wind profiles across both aerosol-rich lower altitudes and molecular-dominated higher altitudes. In this paper, we present a hybrid Doppler lidar system that combines a 355 nm scanning incoherent Rayleigh Doppler lidar with a 1550 nm coherent aerosol Doppler lidar for multi-scale wind field detection. The coherent Doppler lidar is used for boundary-layer wind retrievals, while the Rayleigh Doppler lidar, based on the double-edge technique, extends wind profiling from the upper boundary layer to approximately 40 km. Field deployments demonstrate continuous wind profiling from 50 m to 40 km, extending from the boundary layer to the stratosphere. Comparisons with radiosonde measurements show good agreement during the field campaigns, supporting the feasibility of this hybrid configuration for vertically extended wind profiling. The resulting high-resolution wind measurements across multiple atmospheric regions provide valuable data sources for studies of multi-scale circulation research, gravity wave dynamics, and climate-related atmospheric processes. Full article

Controlling pollutant dispersion in high-rise buildings is crucial for public health. Vertical pollutant diffusion in stairwells occurs under thermal and wind effects. However, most existing studies rely on idealized boundary conditions. To address this, this study uses field-measured wall temperatures and a window wind velocity as boundary conditions for transient CFD simulations. We investigate the vertical diffusion characteristics of buoyant (CH₄) and dense (CO₂) pollutants under thermal pressure, window velocity, and wind–thermal coupling in a high-rise residential building in Taiyuan. Results show an asymmetric “fast-up, slow-down” diffusion under thermal pressure, a relatively symmetric profile under window velocity, and a hybrid pattern under coupling where the upper region is wind-dominated and the lower region resembles thermal-driven diffusion. Wind–thermal coupling most significantly enhances upward diffusion. Using the arrival time of CH₄ at the 28th floor (about 15 m above the source floor) as the benchmark, the diffusion rate under coupling is about 200% faster than under thermal pressure alone, and about 50% faster than under the window-velocity condition alone. Differences in density lead to variations in dispersion, with CH₄ exhibiting higher rates, concentrations (2–4 orders greater), and a broader influence range than CO₂. This work interprets the synergistic regulatory mechanism between driving forces and pollutant density, providing a theoretical basis for ventilation optimization and pollution control in high-rise buildings. Full article

Off-grid electric vehicle (EV) charging infrastructure powered by hybrid solar–wind systems address critical adoption barriers in developing countries, where grid unreliability and sparse charging networks constrain transportation electrification. Despite growing research interest, no comprehensive review has systematically synthesized the interplay between hybrid renewable architectures, intelligent energy management strategies, and techno-economic viability specifically for off-grid EV charging in resource-constrained settings. This systematic review applies the PRISMA methodology to analyze 94 peer-reviewed publications (2013–2026), examining system architectures, intelligent control strategies, power electronics, battery storage, and deployment frameworks for standalone hybrid solar–wind EV charging stations. Key findings indicate that hybrid solar–wind configurations achieve 30–50% reductions in battery storage requirements and 15–25% lower levelized cost of energy (LCOE) (USD 0.08–0.15/kWh) compared with single-source systems, driven by diurnal and seasonal resource complementarity. Among intelligent control methods, the two-stage distributionally robust optimization (TSDRO) framework emerges as the most promising for data-scarce environments, outperforming conventional deterministic and stochastic approaches by 10–20% in managing renewable intermittency without requiring precise probability distributions. Wide-bandgap power semiconductors (SiC, GaN) enable 96–98% conversion efficiency, while lithium iron phosphate batteries provide 3000–5000 cycle lifetimes suited to tropical operating conditions. Critical gaps remain with field validation still predominantly simulation based, long-term operational data exceeding 24 months on equipment degradation and climate resilience are scarce, and scalable financing models for developing country contexts require further development. Nigeria is presented as an exemplar deployment context, with transferable insights for sub-Saharan Africa, South Asia, and Southeast Asia. Full article

The European Urban Wastewater Treatment Directive revision introduced the energy neutrality concept, accelerating the transition of wastewater treatment plants (WWTPs) towards a 100% renewable energy share. Energy audits must be initially conducted to assess current energy consumption levels, identifying deviations from benchmarking values, and energy efficiency measures must be implemented. Strategies should be then diversified according to WWTP size: anaerobic digestion (AD) is a core technology for large-scale plants. The refurbishment of conventional digesters into “enhanced” AD, including sludge pretreatment, co-digestion, or two-stage AD, significantly increases energy yields, providing most of the required electricity/heat. Enhanced AD can be complemented by photovoltaic (PV) panels and thermal energy recovery from effluents. For medium-scale plants, instead, PV implementation is a key solution for electricity production, coupled with hydroenergy recovery and, eventually, wind turbines, while heat can be provided by solar thermal panels or thermal energy recovery from effluents. Hybrid systems, which integrate multiple renewable sources, are often the best solution to reach energy neutrality, improving the system’s resiliency; however, dedicated mathematical models are needed to size and operate the different components, considering local factors. Future research must connect theoretical and in-field studies to allow a wider implementation of hybrid systems. Full article

Waterfront brownfield urban public parks (WBUPPs) are complex regeneration projects that require comprehensive assessment of environmental remediation, climate resilience, urban connectivity, and social well-being. This study proposes a structured GIS-based spatial analysis protocol that operationalizes key attributes of brownfields, waterfronts, public parks, and sustainability, with the aim of examining how digital tools can support WBUPP planning processes. Using free and open source resources and datasets (QGIS and OpenStreetMap), the approach produces eight core thematic maps that spatially organize 39 of 50 criteria identified from the literature and classified under economic, environmental, and social sustainability dimensions. This mapping protocol streamlines navigation for planners through complex datasets and offers researchers a foundation for thematic spatial analyses aligned with these literature-based criteria. The protocol is illustrated with Brooklyn Bridge Park in New York City—an 85-acre waterfront redevelopment that demonstrates heritage conservation, ecological restoration, and financial viability. The results highlight identifiable spatial patterns such as dual zones (urban buffer and recreation), winding pathways, and clustered amenities. At the same time, the analysis underscores the importance of data validation, as inconsistencies in volunteered geographic information require cross-referencing with multiple sources and field verification. The analysis shows that WBUPPs require tailored approaches that integrate land–water mobility, heritage adaptation, nature-based solutions, and equitable service distribution. This criteria-driven protocol offers adaptable guidance for future waterfront brownfield regeneration, while emphasizing that digitalization enhances the process, but it cannot replace hybrid analytical methods that combine quantitative spatial analysis with qualitative evaluations. Full article

This study used Computational Fluid Dynamics (CFD) with the Reynolds-Averaged Navier–Stokes (RANS) k-ω Shear Stress Transport (SST) model to evaluate how crop presence and evapotranspiration affect airflow and thermal stratification in a naturally ventilated tropical tomato greenhouse. Three configurations were simulated: SP-SC-R (No Plants—No crop thermal load—Radiation), CP-SC-R (Crop Present—No crop thermal load—Radiation), and CP-CC-R (Crop Present—Crop thermal load (233.68 W·m⁻²)—Radiation). Mesh independence analysis yielded numerical uncertainties of 1.58% (velocity) and 1 × 10⁻⁶ (temperature). Vegetation reduced canopy air velocity by 55% (from 4 m·s⁻¹ to values below 2 m·s⁻¹). Evapotranspiration enhanced buoyancy-driven mixing, decreasing temperature gradients by up to 1.5 °C, but thermal stratification persisted above 4.5 m in all cases (vertical gradients 0.31–0.42 °C·m⁻¹; maximum roof temperature 37.95 °C). Extreme wind speeds (greater than 20 m·s⁻¹) occurred in the leeward span but above the main foliage. Natural ventilation alone is insufficient for tomato cultivation under tropical conditions. Practical recommendations include increasing roof vent area, installing windbreak baffles, and adopting hybrid ventilation. Future work should use unsteady, RANS/large-eddy simulation (LES), porous media models based on leaf area density (LAI), and field validation. This study demonstrates that coupling crop geometry and evapotranspiration is essential for realistic greenhouse CFD modelling in warm climates. Full article

Accurate regional wind speed forecasting is critical yet challenging due to inherent spatiotemporal correlations and data non-stationarity. This paper proposes a hybrid framework combining Principal Component Analysis (PCA), Variational Mode Decomposition (VMD), and Long Short-Term Memory (LSTM) networks. First, PCA extracts dominant spatial features from a regional wind field (9 × 9 grid), retaining 99.5% of the information to reduce redundancy. Next, an adaptive VMD strategy, optimized by the Sparrow Search Algorithm (SSA), decomposes these components to mitigate temporal non-stationarity. High-correlation sub-signals are then fed into the LSTM predictor. Experimental results demonstrate that the framework achieves an average coefficient of determination ($R^2$) of approximately 0.41 in the first forecasting step. Crucially, it significantly mitigates error accumulation in multi-step forecasting, maintaining a stable $R^2$ of 0.39 in the third step. Conversely, complex spatiotemporal models like ConvLSTM achieve high initial accuracy but suffer severe degradation ($R^2$ dropping from 0.70 to 0.24) alongside significantly higher computational overhead. The proposed strategy effectively prevents overfitting to high-frequency noise, ensuring a computationally efficient and robust solution for multi-step regional wind forecasting. Full article

Unmanned Aerial Vehicles (UAVs) present a promising solution for urban logistics, where an effective energy management strategy guided by optimal path planning is crucial for reducing operational costs and extending system lifespan. This study begins by analyzing the wind field distribution in a specific urban area of Chengdu using Computational Fluid Dynamics, and establishes a data-driven power prediction model to evaluate UAV energy consumption. A hybrid wind-field-aware A* with Ant Colony Optimization algorithm is subsequently proposed to compute the optimal flight path that balances energy consumption and distance, generating corresponding power demand profiles for the ensuing energy management strategy. Finally, a Deep Q-Learning (DQN)-based energy management strategy is implemented to regulate power distribution between the fuel cell and the battery, aiming to minimize hydrogen consumption and stabilize the power output of the primary source. Experimental results demonstrate that the proposed path planning method can effectively reduce energy consumption across different scenarios while causing only a marginal increase in travel distance. In addition, the DQN-based strategy significantly suppresses fuel cell power fluctuations at the cost of only a slight increase in hydrogen consumption, thereby demonstrating the effectiveness of the path-planning-informed energy management strategy. Full article

Small rotorcraft unmanned aerial vehicles (UAVs) are highly susceptible to wind disturbances when performing tasks such as fixed-point hovering, low-altitude inspection, and aggressive maneuvers. Under complex, variable meteorological conditions, attitude stability and position-holding accuracy are particularly critical. Although quadrotor UAVs exhibit structural and dynamic symmetry, real wind disturbances are often asymmetric, disrupting the original balance and leading to intensified attitude oscillations, position drift, and degraded data quality. To effectively address the challenges of wind-induced oscillation and positional deviation, this paper proposes a fuzzy logic-based linear active disturbance rejection control (Fuzzy-LADRC) strategy. This approach employs a hybrid algorithm combining particle swarm optimization and gray wolf optimization to optimize controller parameters and incorporates fuzzy logic to enhance the adaptive capability of the linear active disturbance rejection controller (LADRC). Simulation experiments conducted in MATLAB/Simulink under complex wind-field conditions demonstrate that the proposed method significantly outperforms traditional PID controllers: in the regulation of roll and pitch angles, control performance improves by approximately 5%, while in yaw angle control, the improvement reaches up to 30%. Furthermore, this method can significantly suppress position deviation and fluctuation in the X and Y directions, and reduce the overshoot in the Z-axis during the UAV’s takeoff phase by 75%. Full article

Safe and energy-aware navigation is still difficult for unmanned surface vehicles (USVs), especially in cluttered waters where obstacles, smooth motion, and wind or current effects must be considered at the same time. If these issues are handled separately, the path may become longer and the vehicle may turn more often, which raises propulsion effort and hurts stability. To reduce these problems, a hybrid path planning method called ${\theta }^{\ast }$-APF-Q is proposed, and it combines global planning, learning-based decisions, and local adjustment in a three-layer structure. First, an any-angle ${\theta }^{\ast }$ global planner is employed to generate a near-optimal backbone trajectory by line-of-sight pruning, thereby reducing redundant waypoints and limiting detours. Second, an enhanced tabular Q-learning model is executed in an expanded eight-direction action space, and policy learning is guided by a multi-objective reward that jointly encourages distance reduction, alignment with ocean current and wind-induced forces for energy saving, smooth heading variation to suppress excessive steering, and maintenance of a safety margin near obstacles. Third, an adaptive artificial potential field (APF) module is used for real-time local correction, providing repulsion in high-risk regions and assisting trajectory smoothing to reduce unnecessary turning operations. A decision bias strategy further couples instantaneous APF forces with long-term state–action values, while the influence weight is adaptively adjusted according to environmental complexity. The algorithm is validated on the randomly generated marine grid maps and on the real-world satellite map scenario, with comparisons against a conventional four-direction Q-learning baseline. Across randomized tests, average path length, turning frequency, and the composite energy indicator are reduced by 22.3%, 55.6%, and 26.4%, respectively, and the success rate increases by 16%. The results indicate that integrating global guidance, adaptive learning, and local reactive decision making supports practical, energy-aware USV navigation. Full article