Search Results (416)

Vertical axis wind turbines (VAWTs) have regained attention for distributed, urban, and floating offshore applications, yet the literature remains fragmented across competing rotor concepts and modelling traditions. This review consolidates the principal archetypes—Savonius, H-Darrieus, troposkein Darrieus, helical Darrieus, and Savonius–Darrieus hybrids—through five governing parameters: drag-versus-lift-driven operating principle, tip speed ratio λ = ωR/V_∞ (0.6–1.2 for Savonius; 2.5–5.0 for Darrieus), solidity σ = Nc/R (0.1–0.4), chord-based Reynolds number R_{e_c} (10⁵ − 10⁶), and peak power coefficient C_{p_max} (0.15–0.25 for Savonius; 0.35–0.45 for optimized H-Darrieus). Off-design performance is dominated by unsteady mechanisms that quasi-steady streamtube models cannot resolve—leading edge vortex shedding, dynamic stall hysteresis, blade–wake interaction, and flow-curvature-induced virtual camber—each examined for its contribution to the instantaneous torque C_T(θ) and the cycle-averaged C_p. Turbulence closures are benchmarked against phase-locked PIV and torque measurements: k – ω SST URANS captures peak-region C_p to within ±5–10% but over-predicts torque below λ_opt; the γ – R_{e_θ} transition SST model reduces this error to ±3–5%; DES, DDES, and LES reach ±2 – 3% at one to two orders of magnitude higher cost. Best practice computational fluid dynamics (CFD) guidelines are consolidated: domain extents of 15 D upstream, 10 D downstream, and 20 D lateral; rotating sub-domain D_rot » 1.5 D; y⁺ ≤ 1; Δθ ≤ 0.1°; and 20–30 revolutions before sampling. Performance enhancement strategies (variable pitch, guide vanes, helical twist, and hybridization) are reviewed quantitatively, with reported C_p gains of 5–30%. Four research priorities are identified: (i) transition-sensitive turbulence closures validated below R_{e_c} = 5 × 10⁵; (ii) coupled aero-hydro-servo-elastic models for floating offshore VAWTs; (iii) machine-learning-augmented turbulence modelling—including physics-informed neural networks (PINNs) and neural-network-corrected RANS closures—to improve unsteady flow prediction at sub-LES cost; and (iv) integrated aeroacoustic–aeroelastic frameworks for urban and building-integrated deployment. Full article

The growing environmental crisis and rapid urbanization have made the shift to renewable energy systems even more important for smart city development. In today’s cities, such renewable energy sources as solar photovoltaics, wind energy, hybrid systems, and battery energy storage are no longer just separate assets. They are now important parts of smart grids, intelligent buildings, and urban infrastructure that work together. However, putting these systems in cities on a large scale makes it harder to monitor, control, integrate, scale, and work with them in real time. In this setting, the Internet of Things (IoT) and edge computing are technologies that make it possible to turn traditional renewable energy systems into smart, responsive, and self-sufficient urban energy systems. IoT-based monitoring and control systems let city operators, utilities, and policymakers gather real-time data, improve grid stability, optimize energy flows, and better integrate distributed renewable energy sources into smart city ecosystems. Edge computing makes these features even better by allowing for low-latency processing, more localized decision-making, and less reliance on centralized cloud infrastructures. This paper offers a thorough and methodical examination of contemporary IoT- and edge-enabled technologies used to monitor, control, and integrate renewable energy systems; specifically highlighting their significance in smart city and smart grid applications. The review combines the most recent research on hardware platforms, communication protocols, data processing architectures, and edge–cloud coordination mechanisms used in solar, wind, and hybrid energy systems. Additionally, this review synthesizes architectural design principles extracted from analyzed studies to guide the development of scalable, resilient, and cost-efficient renewable energy monitoring systems. This study offers a structured foundation for the design of scalable, resilient, and cost-effective renewable energy management systems that align with the sustainability, efficiency, and intelligence goals of future smart cities by analyzing cutting-edge solutions and pinpointing significant technological trends, challenges, and research deficiencies. This review also highlights its contribution vis-à-vis previous surveys by stressing the inter-domain comparison across solar, wind, and hybrid systems. It focuses, in particular, on edge–cloud coordination and architecture-level trade-offs pertinent to smart grid and smart city deployments. Full article

With the acceleration of urbanization and the intensification of climate change, wind conditions have become a critical factor in architectural design. They not only affect a building’s wind resistance but also influence ventilation, pollutant dispersion, pedestrian comfort, and energy consumption. Traditional computational fluid dynamics (CFD) simulations are costly. Although the application of machine learning for CFD prediction has become a relatively mature technology, machine learning models still face challenges in actual architectural design workflows. Building upon recent advancements in the field, it proposes two core technologies: a method for predicting outdoor wind environments in buildings based on the Site-Specific Training for Design Tasks (SST-DT) strategy, and an automated machine learning workflow. These innovations improve upon existing wind environment analysis methods and systems, establishing a fully automated working framework that is easy for architects to learn and use. Within this framework, dataset acquisition and model training are performed automatically. Finally, this framework was validated across various prediction tasks with different objectives. It significantly lowers the barrier to entry for architects adopting machine learning, advances the performance-driven design paradigm, and facilitates the deep integration of machine learning technologies into architectural wind engineering. Full article

Urban areas significantly influence atmospheric flow fields and momentum exchange processes, which are relevant for wind energy applications and meso-scale atmospheric modeling. However, meso-scale simulations typically represent urban effects using surface roughness parameterizations that neglect volumetric momentum losses within the urban canopy layer. In this study, a methodology is presented to derive a volumetric urban canopy parameterization directly from building-resolved computational fluid dynamics (CFD) simulations. A detailed micro-scale CFD simulation of a real urban region is used to evaluate the momentum balance within a control volume surrounding the urban region. Based on this analysis, two key parameters are derived: the vertical distribution of the House Area Density (HAD), representing the geometric characteristics of the urban morphology, and an effective drag coefficient describing the momentum loss induced by the built environment. These parameters are subsequently implemented as volumetric source terms in a urban canopy model formulated analogously to plant canopy parameterizations. The resulting urban canopy model is validated by comparison with the fully resolved CFD simulation. The results show good agreement in the streamwise momentum balance and pressure loss distribution, while computational cost is significantly reduced. The proposed urban canopy model provides a physically consistent framework for representing urban momentum sinks in meso-scale flow simulations. Full article

To mitigate large-scale blackout risks in urban distribution systems under typhoon-induced extreme weather and to reduce post-disaster restoration costs, this study proposes a resilience-oriented spatiotemporal co-optimization framework integrating transportation networks, power grids, and distributed energy resources. First, a city-scale typhoon spatiotemporal model is established, integrating static wind field, dynamic evolution, and trajectory-based mobility with urban-geometry-driven wind speed correction to characterize the spatiotemporal progression of extreme wind hazards. Second, the time-varying failure rates of distribution network components are quantified by explicitly accounting for network topology correlations, while the spatiotemporal dispatchability and output characteristics of distributed resources under disaster conditions are systematically modeled. Third, a pre-disaster proactive deployment model is formulated to minimize load curtailment costs and resource allocation expenditures. The model integrates active network reconfiguration with coordinated placement of distributed generation (DG) and mobile energy storage systems (MESSs), enabling resilience-enhancing pre-positioning strategies. Subsequently, a post-disaster restoration scheduling model is developed with the objective of minimizing unserved load. By embedding traffic flow constraints and optimal path computation under disrupted transportation conditions, the proposed framework realizes spatiotemporal coordination among MESSs, DG, and electric vehicles (EVs), thereby accelerating system-level recovery. Finally, the effectiveness of the proposed strategy is validated on a 51-node urban distribution system located in eastern coastal China, demonstrating significant improvements in restoration performance and resilience enhancement. Full article

Rooftop photovoltaic systems are a key component of sustainable urban energy strategies; however, their performance is strongly influenced by environmental variability across spatial scales. This study develops and validates a mathematical model to quantify the influence of Global Horizontal Irradiation (GHI), air temperature, wind speed, and dust on rooftop solar energy efficiency at country, regional, and city levels. The model is applied to environmental and energy data from 96 countries and 17 regions and further validated using four large-scale rooftop PV projects in Bahrain. The results show strong agreement between predicted and actual solar energy production, with coefficients of determination of R² = 0.77 at the country level, R² = 0.84 at the regional level, and R² = 0.998 at the city level, while mean absolute percentage errors generally remain below 10%. Regression and sensitivity analyses showed that at least one environmental factor exerts a statistically significant influence on rooftop solar energy yield, supporting the alternative research hypothesis. GHI is identified as the most influential driver at the national scale, whereas temperature and dust effects become more pronounced at finer spatial resolutions. Deployment gap analysis further reveals substantial untapped rooftop solar potential, highlighting the importance of non-environmental constraints in shaping real-world solar adoption. 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

Solar energy is emerging as a cornerstone of the global renewable energy transition, with projections indicating that photovoltaics (PV) could contribute up to 90% of electricity generation by 2050. However, environmental factors, particularly dust deposition, pose a significant challenge to the long-term performance and efficiency of PV systems. Dust accumulation varies widely across different geographic regions, influenced by climate, land use, humidity, and pollution. Arid and semi-arid areas experience the highest deposition rates, while tropical and temperate regions are affected by seasonal rainfall and urban pollutants. This review comprehensively examines the impact of dust on PV performance, highlighting factors such as surface roughness of PV module, panel tilt angle, seasonal variations, wind dynamics, and dust composition. Furthermore, the review assesses various dust mitigation strategies, including manual and water-based cleaning, robotic systems, hydrophobic coatings, and electrostatic methods. By synthesizing global studies and presenting a holistic view of dust effects, this paper provides critical insights into the impact of performance degradation with regional variation in PV, optimizing performance, maintenance, and effective dust mitigation strategies to ensure sustained energy yield and reliability in solar energy systems worldwide. Full article

The Archimedes Spiral Wind Turbine (ASWT) is a novel horizontal axis wind turbine for urban low-wind-speed applications. To improve the wind energy capture efficiency of the ASWT, this study adopted a multivariable global optimization strategy. A differential evolution–Kriging surrogate model method was employed for blade structural optimization. The blade geometry was parametrically modeled, and three design variables were selected: spiral pitch, opening angle, and spiral rotation number (SRN). Latin hypercube sampling was used to generate sample points in the design space. The power coefficients (C_p) of all design samples were calculated by Computational Fluid Dynamics (CFD) simulations. A Kriging surrogate model was constructed to map the nonlinear relationship between the design variables and C_p. The optimal blade geometry was obtained by solving the surrogate model with differential evolution (DE) and validated by CFD. The results showed that at the design condition of a wind speed of 8 m/s and a tip speed ratio (TSR) of 1.875, the relative error between Kriging model predictions and CFD simulations was only 0.27%. The optimized blade achieved a C_p of 0.3085, representing a 4.78% improvement over the best sample blade, with both achieving their peak power coefficients at TSR = 1.875. Full article

This study aims to improve the accuracy of cruise-phase power consumption prediction for multirotor unmanned aerial vehicles operating under varying wind conditions. Existing parametric energy models typically retain the wind velocity vector in the ground or inertial reference frame, and this representation does not distinguish between axial drag contributions along the fuselage and lateral attitude-correction contributions perpendicular to it. The proposed framework addresses this limitation through a physics-informed coordinate transformation that projects the measured wind vector into the body frame of the aircraft using quaternion-derived heading angles, yielding separate axial and lateral wind components. These components enter the power model as two additional predictors that augment the induced-power baseline, with the axial term following a cubic airspeed–power relationship consistent with parasitic drag formulations and the lateral term following a quadratic relationship consistent with attitude-correction mechanics. The framework is validated on a publicly available flight dataset, which comprises 188 flights of a DJI Matrice 100 quadcopter across payloads of 0 to 0.75 kg, ground speeds of 4 to 12 m/s, and altitudes of 25 to 100 m. Compared with the induced-power baseline, the proposed model reduces the root mean square error by 15.9% and the mean squared error by 29.7% during the cruise phase. The improvement is larger when wind speeds exceed 6 m/s, a regime in which the baseline residuals increase while the proposed model retains a comparatively stable error profile. Residual analysis indicates that baseline errors follow an approximately quadratic trend relative to the axial and lateral wind components, consistent with established parasitic-power and attitude-correction formulations. The closed-form structure of the proposed model is compatible with onboard execution on flight controllers, which suggests a feasible pathway toward its use as the power-prediction module within downstream range-estimation and energy-reserve sizing routines. Full article

Urban low-altitude logistics is increasingly constrained by obstacle-rich city morphology and wind-induced flight disturbances, which makes conventional path-planning methods insufficient for simultaneously ensuring efficiency, feasibility, and robustness. To address this issue, this study proposes an improved whale optimization algorithm (IWOA) for wind-field-coupled three-dimensional UAV path planning in urban environments. A voxel-based urban model is established, and the planning objective integrates flight time, energy consumption, wind-field penalty, and path smoothness. On the basis of the original whale optimization algorithm, the proposed method introduces a wind-field-guided local adjustment operator, adaptive convergence control, elite preservation, large-scale mutation, and feasibility repair. The proposed method is evaluated through a structured simulation framework comprising four scenarios: a baseline case, urban density variation, complex wind-field variation, and multi-destination delivery. The results show that IWOA consistently yields the lowest composite cost among the compared algorithms and exhibits better path smoothness, stronger wind adaptation, and earlier convergence stability. In the baseline case, the total cost of IWOA is reduced by 17.3%, 13.1%, and 6.7% relative to A*, GA, and WOA, respectively. Under the high-density urban environment and the complex wind field, IWOA also maintains the best performance, indicating stronger robustness under increased environmental difficulty. Sensitivity analyses further show that wind speed and wind direction have pronounced effects on the total cost, while the energy coefficient mainly affects the energy-related component. These results demonstrate that the proposed framework provides an effective and practically relevant solution for urban low-altitude UAV logistics path planning. Full article

In evaluating energy consumption in building complexes, the influence of urban microclimate variations—primarily driven by the urban heat island (UHI) effect—is often overlooked, leading to modeling inaccuracies. This study develops a numerical simulation framework integrating Weather Research and Forecasting (WRF) and EnergyPlus to assess the energy consumption of university dormitories while accounting for regional microclimate conditions. This is because university dormitories serve as a key indicator for measuring the type of high-density residential buildings in China. The model incorporates dynamic microclimate variables, including ambient temperature, relative humidity, wind speed, solar radiation, and cloud cover, to simulate dormitory energy consumption profiles. Simulation results are validated against measured data, yielding an annual energy consumption error of −1.03%. Quantitative analysis indicates that ignoring the microclimate effect and directly using data from nearby meteorological stations or TMY data has a limited impact on the annual total energy consumption but has a significant impact on seasonal results. To improve the simulation accuracy of building complexes, more attention should be paid to temperature and relative humidity. Moreover, for areas with low occupant density and a high shape coefficient, energy consumption simulation should also consider the local microclimate factors. Full article

Autonomous trajectory planning for electric Vertical Takeoff and Landing (eVTOL) Unmanned Aerial Vehicles (UAVs) faces the dual challenges of low-altitude environmental interference and limited onboard energy, which affects the reliability and safety of unmanned missions. To address these challenges, this paper develops the EA-TD3 autonomous trajectory planning framework for eVTOL UAV systems. First, a stochastic urban wind field model is established to simulate low-altitude interference. Then, by integrating eVTOL UAV battery discharge data from Carnegie Mellon University (CMU), a mapping relationship between maneuvers and energy consumption is identified to construct a nonlinear energy consumption model. Finally, an energy boundary penalty function is introduced into the TD3 algorithm to ensure that trajectory planning remains within battery safety margins. Experiments based on the parameters of the EH216-S platform show that EA-TD3 achieves a near 100.00% success rate under ideal conditions and outperforms benchmark algorithms while reducing average energy consumption by 11.6%. Under an energy constraint of 120 J, its success rate remains at 87.80%, which exceeds the performance of the DDPG, SAC, and standard TD3 algorithms. This study optimizes the autonomous trajectory planning of eVTOL UAV platforms in urban air mobility (UAM) to improve the energy perception and power management of the autonomous system. Full article

The limited self-starting capability of H-type Darrieus Vertical-Axis Wind Turbines (VAWT) remains one of the main obstacles to their deployment in low-power and urban applications, where wind conditions are typically weak and intermittent. Although several passive geometric modification strategies have been proposed to enhance initial torque generation, most available studies rely predominantly on numerical simulations, with limited systematic experimental validation under low tip-speed ratio (TSR) conditions. In this work, the influence of passive blade modifications on self-starting performance is assessed through a combined numerical–experimental approach. An integrated numerical–experimental framework was used to systematically compare passive blade configurations under equivalent low-wind conditions. Two modified configurations, a biomimetic profile incorporating passive trailing-edge devices and an asymmetric J-type geometry, were optimized using transient CFD simulations of the first rotation cycle and a Design of Experiments (DOE) framework. Additively manufactured full-rotor test blades were then manufactured via additive manufacturing and tested in a controlled wind tunnel at 3.0 m/s and 2.25 m/s. Start-up time, azimuthal robustness, tip-speed-ratio evolution, and static start-up torque (interpreted through its corresponding torque coefficient) were measured and compared against a baseline NACA0018 profile. The biomimetic configuration consistently produced higher start-up torque and shorter acceleration times, achieving self-starting in 66.7% of the evaluated azimuthal positions at 2.25 m/s, compared to 22.2% for the baseline profile. Within the investigated operating range, this configuration emerged as the most robust passive strategy. The agreement between CFD predictions and experimental measurements supports the use of first-cycle maximum torque as a representative indicator of self-starting performance. These findings highlight the comparative value of first-cycle maximum torque as a practical metric for passive self-starting design assessment in low-TSR Darrieus turbines. These findings provide direct experimental evidence to guide the rational design of Darrieus turbines intended for marginal wind conditions. Full article

To address challenges such as the sudden onset of urban fires, data synchronization delays in early warning systems, response lags, and insufficient routine monitoring, this paper proposes a Spatio-Temporal Collaborative Optimization for Joint Control and Scheduling (STCO-JCS) algorithm tailored for unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs). First, spatial autocorrelation analysis based on fire data classifies areas into ultra-high, high, medium, and low risk zones to assist in determining UAV access priorities. Second, we construct optimal inspection trajectories for the UAV by taking into account the inspection sequence and the city’s topography. By modeling the path deviations caused by wind interference and designing precision control algorithms, we improve the accuracy of the UAV’s flight path, ultimately achieving the goal of reducing UAV inspection time. Finally, by coordinating the spatiotemporal operations of drones and wireless sensor networks, we can achieve early detection and rapid response in high-risk fire zones, thereby reducing drone energy consumption while enhancing the efficiency of the UAV-WSN fire monitoring system. Simulation results demonstrate that under a 20-square-kilometer simulation area, STCO-JCS controls inspection paths within 14–17 km. In the multi-UAV scenario, the proposed method achieves approximately 3.17–9.66% improvement in energy efficiency, while in the single-UAV scenario, improvements of 10.83%, 50.54%, and 9.26% are observed in metrics. This provides effective decision support for the dynamic deployment of firefighting and rescue resources. Full article