Low Wind Speed Research Articles

Research on the Power Output of Different Floating Wind Farms Considering the Wake Effect

For floating wind turbines, one of the most interesting and challenging issues is that the movement of the rotor is strongly related to its floating platform, which results in corresponding variations in the wake characteristics of the turbine. Because the aerodynamic efficiency of the downstream turbines is affected by the wake characteristics, the power output will consequently vary depending on the different types of floating wind turbines and floating wind farms used. In this study, the rotor movement, wake characteristics, and corresponding wind farm power output are analyzed using a numerical method for three typical floating wind turbines: the semisubmersible type, spar buoy type, and tension leg platform type with a 5 MW configuration. A fixed-bottom monopile wind turbine is adopted as a benchmark. The simulation results show that of the three floating wind turbines, the rotor position and wake center are most dispersed in the case of the spar buoy type, and its wake also has the lowest impact on downstream wind turbines. Additionally, the power output of the corresponding spar buoy type wind farm is also the highest at different wind speeds, followed by the semisubmersible type, tension leg platform type, and then the fixed-bottom type. In particular, at low wind speeds, the wake effects differ significantly among the various types of wind turbines.

Enhancing energy harvesting through hybrid bluff body at a predefined angle of attack coupling vortex-induced vibration and galloping

This paper introduces a novel approach to enhance energy harvesting efficiency by coupling vortex-induced vibration and galloping through the use of a D-shaped hybrid bluff body with a predefined angle of attack. By adjusting the angle of attack, the aerodynamic interaction with the bluff body is improved, significantly increasing the vibration amplitude. Wind tunnel experiments are conducted to explore the effects of the predefined angle of attack and the rectangular height of the bluff body cross-section on the energy harvester's efficiency. The analysis identifies several optimal configurations that utilize the coupled vortex-induced vibration and galloping to enhance energy harvesting at low wind speeds while maintaining significant vibration amplitudes at high wind speeds. The proposed structure could achieve an RMS output voltage of 38.76 V, representing a 153.5% improvement compared to the traditional galloping piezoelectric energy harvesters. Additionally, the cut-in wind speed is reduced by 37.5%, allowing vibrations to begin at a low wind speed of 1.5 m/s, exhibiting a superior wind energy harvesting efficiency. Two-dimensional computational fluid dynamics simulations are employed to explain the underlying mechanisms of fluid-structure interaction, demonstrating the characteristics of the flow field, vortex shedding, and the transition in vibration modes.

International airport emissions and their impact on local air quality: chemical speciation of ambient aerosols at Madrid–Barajas Airport during the AVIATOR campaign

Abstract. Madrid–Barajas Airport (MAD) is the fourth-busiest airport in Europe. The aerosol chemical composition and the concentrations of other key pollutants were measured at the airport perimeter during October 2021 to assess the impact of airport emissions on local air quality. A high-fidelity ambient instrumentation system was deployed at Madrid–Barajas Airport to measure the following: concentrations of organic aerosols (with their composition), black carbon (eBC), carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NOx), sulfur dioxide (SO2), particulate matter (PM2.5, PM10), total hydrocarbon (THC), and total particle number. The average concentrations of eBC, NOx, SO2, PM2.5, PM10, CO, and THC at the airport for the entire campaign were 1.07 µg m−3, 22.7 µg m−3, 4.10 µg m−3, 9.35 µg m−3, 16.43 µg m−3, 0.23 mg m−3, and 2.30 mg m−3, respectively. The source apportionment analysis of the non-refractory organic aerosol (OA) using positive matrix factorisation (PMF) allowed us to discriminate between different sources of pollution, namely less oxidised oxygenated organic aerosol (LO-OOA), alkane organic aerosol (AlkOA), and more oxidised oxygenated organic aerosol (MO-OOA). The results showed that LO-OOA and MO-OOA account for more than 80 % of the total organic particle mass measured near the runway. Trace gases correlate better with the AlkOA factor than LO-OOA and MO-OOA, indicating that AlkOA is mainly related to primary combustion emissions. Bivariate polar plots were used for pollutant source identification. Significantly higher concentrations of the obtained factors were observed at low wind speeds (<3 m s−1) from the southwest, where two of the runways and all terminals are located. Higher SO2/NOx and CO/eBC ratios were observed when the winds originated from the northeast, where the two northern runways are located. These elevated ratios are attributed to the aircraft activity being the major pollutant source in the northeast area.

Impact of Atmospheric Parameters on Human Respiratory Tract Infections (RTIs)

Respiratory tract infections (RTIs) are a significant public health issue globally, with varying degrees of severity and implications. Environmental factors, particularly atmospheric parameters, can exacerbate these health conditions. Understanding the relationship between atmospheric conditions and RTIs is crucial for effective public health interventions. This systematic literature review analyzes research on the impact of atmospheric parameters on RTIs through a systematic search across multiple electronic databases using different keyword combinations. The retrieved articles were categorized based on atmospheric parameters investigated, such as temperature, humidity, air pollution, and rainfall. The review findings indicate that various atmospheric factors, including wind speed, air pollution (PM10, PM2.5, CO, and O3), air temperature, atmospheric pressure, and humidity, influence the occurrence and severity of respiratory infections. Low wind speed and high air pollution were identified as risk factors for severe acute respiratory infection (SARI). In children, meteorological factors like air temperature, atmospheric pressure, rainfall, sunlight hours, wind speed, and relative humidity were associated with lower respiratory tract infections. Pollutants like PM2.5, carbon monoxide, sulfur dioxide, and tropospheric ozone directly impact pediatric respiratory infections. Climate variables affect the seasonal activity of pathogens like respiratory syncytial virus (RSV). These findings underscore the importance of considering atmospheric parameters in understanding and preventing RTIs. Addressing these challenges requires a comprehensive approach integrating environmental and public health interventions. This approach can better mitigate the effects of atmosphere-related risks on respiratory health.

Study of a Novel Updraft Tower Power Plant Combined with Wind and Solar Energy

This study presents a novel solar updraft tower power plant (SUTPP) system, which has been designed to achieve the simultaneous utilization of solar and wind energy resources in desert regions, in response to the pressing demand for sustainable and efficient renewable energy solutions. The aim of this research was to develop an integrated system that is capable of harnessing and converting these abundant energy sources into electrical power, thereby enhancing the renewable energy portfolio in arid environments. The methodology of this study involved the design and construction of a prototype SUTPP, comprising a 53 m high tower, a 6170 m2 collector, five horizontal-axis wind turbines, and a thermal energy storage layer made up of pebbles and sand. The experimental setup was meticulously detailed, and experiments were conducted to collect data on the system’s performance under various environmental conditions. Subsequently, three-dimensional numerical simulations were performed to explore the effects of ambient wind speed and solar radiation on the output power of the SUTPP. The results indicate that the output power of the system increases with the increase in ambient wind speed and solar radiation. The impact of solar irradiation on output power was observed to diminish as ambient wind speeds increased. Notably, as the inlet wind speed rose from 4 m/s to 12 m/s, the output power showed a substantial increase of 727%. The numerical simulations revealed that ambient wind speed has a more pronounced effect on power output compared to solar radiation. Furthermore, it was found that the influence of solar radiation is significant at low wind speeds, with its impact decreasing as wind speed increases. This research provides essential guidance for the design and engineering of highly efficient solar thermal energy utilization projects, representing a significant advancement in the field of renewable energy technology deployment in desert environments.

Hybrid CMOD-Diffusion Algorithm Applied to Sentinel-1 for More Robust and Precise Wind Retrieval

Synthetic Aperture Radar (SAR) imagery presents significant advantages for observing ocean surface winds owing to its high spatial resolution and low sensitivity to extreme weather conditions. Nevertheless, signal noise poses a challenge, hindering precise wind retrieval from SAR imagery. Moreover, traditional geophysical model functions (GMFs) often falter, particularly in accurately estimating high wind speeds, notably during extreme weather phenomena like tropical cyclones (TCs). To address these limitations, this study proposes a novel hybrid model, CMOD-Diffusion, which integrates the strengths of GMFs with data-driven deep learning methods, thereby achieving enhanced accuracy and robustness in wind retrieval. Based on the coarse estimation of wind speed by the traditional GMF CMOD5.N, we introduce the recently developed data-driven method Denoising Diffusion Probabilistic Model (DDPM). It transforms an image from one domain to another domain by gradually adding Gaussian noise, thus achieving denoising and image synthesis. By introducing the DDPM, the noise from the observed normalized radar cross-section (NRCS) and the residual of the GMF methods can be largely compensated. Specifically, for wind speeds within the low-to-medium range, a DDPM is employed before proceeding to another CMOD iteration to recalibrate the observed NRCS. Conversely, a posterior-placed DDPM is applied after CMOD to reconstruct high-wind-speed regions or TC-affected areas, with the prior information from regions characterized by low wind speeds and recalibrated NRCS values. The efficacy of the proposed model is evaluated by using Sentinel-1 SAR imagery in vertical–vertical (VV) polarization, collocated with data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Experimental results based on validation sets demonstrate significant improvements over CMOD5.N, particularly in low-to-medium wind speed regions, with the Structural Similarity Index (SSIM) increasing from 0.76 to 0.98 and the Root Mean Square Error (RMSE) decreasing from 1.98 to 0.63. Across the entire wind field, including regions with high wind speeds, the validation data obtained through the proposed method exhibit an RMSE of 2.39 m/s, with a correlation coefficient of 0.979.

Disparities in precipitation effects on PM2.5 mass concentrations and chemical compositions: Insights from online monitoring data in Chengdu

Precipitation plays a pivotal role in wet deposition, significantly affecting aerosol purification. The efficacy of precipitation in removing aerosols depends on its type and the characteristics of the particulates involved. However, further research is necessary to fully understand how precipitation impacts PM2.5 components. This study utilized high-temporal-resolution data on PM2.5, its components and meteorological factors to examine varying responses influenced by precipitation intensity and duration. The findings indicate that increased rainfall intensity and duration enhance PM2.5 and its constituents removal efficiency. Specifically, longer precipitation periods significantly improve PM2.5 purification, especially with drizzle and light rain. Moreover, there is a direct correlation between pre-precipitation PM2.5 levels and its scavenging rates, with drizzle potentially exacerbating PM2.5 pollution under cleaner conditions (≤ 35 µg/m3). Seasonally, the efficacy of removing PM2.5 components varies notably in response to drizzle and light rain. In spring, higher PM2.5 levels after drizzle were primarily due to increased organic carbon concentrations favored by higher relative humidity and lower pH conditions compared to other seasons, conducive to secondary organic aerosol production. Lower wind speeds and higher temperatures further contribute to water-soluble organic carbon accumulation. Daytime and nighttime precipitation exerted differing influences on PM2.5 components, particularly in spring where daytime drizzle and light rain significantly increased PM2.5 and its constituents, notably NO3-, potentially associated with phase distribution changes between gas and aerosol phases in low-temperature, high-RH conditions compared to nighttime. These results propose a dual-impact mechanism of precipitation on PM2.5 and provide scientific basis for designing effective control strategies.

Predictive Understanding of Links Between Vegetation and Soil Burn Severities Using Physics‐Informed Machine Learning

AbstractBurn severity is fundamental to post‐fire impact assessment and emergency response. Vegetation Burn Severity (VBS) can be derived from satellite observations. However, Soil Burn Severity (SBS) assessment—critical for mitigating hydrologic and geologic hazards—requires costly and laborious field recalibration of VBS maps. Here, we develop a physics‐informed Machine Learning model capable of accurately estimating SBS while revealing the intricate relationships between soil and vegetation burn severities. Our SBS classification model uses VBS, as well as climatological, meteorological, ecological, geological, and topographical wildfire covariates. This model demonstrated an overall accuracy of 89% for out‐of‐sample test data. The model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. VBS had the largest control on SBS, followed by weather (e.g., wind, fire danger, temperature), climate (e.g., annual precipitation), topography (e.g., elevation), and soil characteristics (e.g., soil organic carbon content). The relative control of processes on SBS changes across regions. Our model revealed nuanced relationships between VBS and SBS; for example, a similar VBS with lower wind speeds—that is, higher fire residence time—translates to a higher SBS. This transferrable model develops reliable and timely SBS maps using satellite and publicly accessible data, providing science‐based insights for managers and diverse stakeholders.

Effectiveness of inter-regional collaborative emission reduction for ozone mitigation under local-dominated and transport-affected synoptic patterns.

In recent years, the concentrations of ozone and the pollution days with ozone as the primary pollutant have been increasing year by year. The sources of regional ozone mainly depend on local photochemical formation and transboundary transport. The latter is influenced by different weather circulations. How to effectively reduce the inter-regional emission to control ozone pollution under different atmospheric circulation is rarely reported. In this study, we classify the atmospheric circulation of ozone pollution days from 2014 to 2019 over Central China based on the Lamb-Jenkinson method and the global analysis data of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) operation. The effectiveness of emission control to alleviate ozone pollution under different atmospheric circulation is simulated by the WRF-Chem model. Among the 26 types of circulation patterns, 9 types of pollution days account for 79.5% of the total pollution days and further classified into 5 types. The local types (A and C type) are characterized by low surface wind speed and stable weather conditions over Central China due to a high-pressure system or a southwest vortex low-pressure system, blocking the diffusion of pollutants. Sensitivity simulations of A-type show that this heavy pollution process is mainly contributed by local emission sources. Removing the anthropogenic emission of pollutants over Central China would reduce the ozone concentration by 39.1%. The other three circulation patterns show pollution of transport characteristics affected by easterly, northerly, or southerly winds (N-EC, EC, S-EC-type). Under the EC-type, removing anthropogenic pollutants of East China would reduce the ozone concentration by 22.7% in Central China.

Bioinspired swept-curved blade design for performance enhancement of Darrieus wind turbine

The present research proposes novel swept-curved bioinspired blades for Darrieus wind turbine. The design was influenced by the curved flippers of the bottle-nose dolphin. The proposed model was designed using the Solidworks software, empirically tested for power improvement, later fine-tuned adopting high fidelity numerical modeling. An in-depth analysis comparing the traditional H-bladed turbine with the new-bladed design was performed using OpenFOAM. Further, an elaborate experimental observations on the proposed design suggest that the model with 0.6c forward sweep outperformed the rest. The efficiency of the curved blade was notably enhanced by 14.41%, in contrast to the conventional model, and the numerical analysis endorses this observation. The modified turbine blades have the maximum wind streamlines that converge toward the center of the blade's trailing edge due to the lateral shift, and the resulting convergence reduces blade tip losses. The modified blade models demonstrated improved angular acceleration at a given wind speed, indicating enhanced efficiency and superior self-starting capabilities. Notably, these improvements were evident even at a lower wind speed of 4.2 m/s compared to the traditional H-model.

Spatio-Temporal Variability of Algal Bloom in the Caspian Sea

Seasonal, interannual variability and spatial distribution of algal bloom in the North, Middle and South Caspian Sea has been investigated on the basis of Aqua MODIS chlorophyll-a (Chl-a) concentration data. Multi-year data demonstrate that over the two past decades Chl-a concentration has increased only in the North Caspian where the main rivers Volga and Ural enter the sea. In the Middle and South Caspian, it has shown weak negative linear trends. Special attention is paid to anomalous phytoplankton bloom, first of all cyanobacteria, in the South Caspian. Over 24 years of satellite observation, 1999-2022, abnormally intense phytoplankton bloom has been detected 8 times. Till present, it remains an open question what causes such outbreaks of intense algal bloom in the South Caspian. Generally, the literature and our studies suggest a variety of possible drivers of algal bloom, such as high sea surface and air temperature, low wind speed, high Chl-a concentration in periods preceding the development of phytoplankton bloom, atmospheric precipitation, and even dust storms. However, for the South Caspian we have found no clear correlation with any of these factors. Satellite monitoring of intense blooms, especially cyanobacteria, is of vital importance because cyanobacteria can produce potent toxins that can cause negative consequences for wildlife, ecosystems and even affect human health.

Repeated drone photogrammetry surveys demonstrate that reconstructed canopy heights are sensitive to wind speed but relatively insensitive to illumination conditions

ABSTRACT Unoccupied aerial vehicle (UAV) based structure-from-motion (SfM) photogrammetry surveys are becoming a standard tool for ecologists to measure plant structure and biomass in non-forest ecosystems. The reproducibility of SfM survey results under different operational conditions, namely wind speed, sun elevation, and cloud condition, is poorly understood. It is also unclear to what extent commonly applied point-to-grid interpolation of derived point clouds affects inference of vegetation structure. These knowledge gaps limit the use of these methods for measuring and monitoring detection. We captured 61 UAV SfM surveys at the same study area under a range of wind/sun/cloud conditions over a 24-day period during 2021 and used generalized linear mixed effects models to test how the structural reconstructions varied with environmental conditions. Wind speed significantly influenced the canopy height reconstructions, with greater wind speeds reducing mean canopy height. Different plant species exhibited varying sensitivities to wind that are likely related to leaf attributes (size, structure, and density), growth form of the canopy, and physical properties such as limb flexibility. The movement of plants can reduce canopy height estimates derived from photogrammetric surveys, even under relatively low wind speeds. Reconstructed canopy heights were comparatively insensitive to solar elevation variations. Cloud conditions and illumination by direct sunlight had a weak, non-significant effect on reconstructed canopy height, with sunny conditions (generating shadows) resulting in a measurable but marginal reduction in mean canopy heights. When comparing canopy heights of interpolated and discontinuous derived canopy height models the results highlighted that these were insensitive to interpolation in this specific setting. We recommend measuring mean wind speeds throughout surveys where comparisons are to be made between different drone-based SfM surveys of vegetation over either time or space. Care should be taken to ensure that the effects of wind are controlled for so that inferences are valid.

Effects of the influence of triangular dimple and aspect ratio on NACA 4412 airfoil on the overall performance of H-Darrieus wind rotor: An experimental investigation

This study examines how adjusting aspect ratio (AR) and adding triangular dimples to the airfoil can enhance H-type Darrieus wind rotor (H-rotor) performance under low wind speeds (4, 6, and 8 m/s) using wind tunnel testing. Results show optimal performance at an AR of 1.0 for both standard and Triangular dimple airfoil H-rotors, with variations at different wind speeds for other aspect ratios. Triangular dimple-equipped H-rotors demonstrate improved self-starting capability and a wider operational range of tip speed ratio (TSR) compared to standard rotors, ensuring reliability across diverse wind conditions. Moreover, the triangular dimple rotor achieves a nearly 13.6% increase in maximum coefficient of power ([Formula: see text]) compared to the dimple-free H-rotor at 4 m/s wind speed. This study underscores the efficacy of adjusting AR and integrating triangular dimple blades to enhance H-rotor performance, particularly in low wind speeds.

A simplified approach to modeling temperature dynamics in photovoltaic systems – Validation, case studies, and parametric analysis

This paper presents a simplified theoretical model for analyzing the temperature dynamics of photovoltaic (PV) modules. The model is built on an energy balance approach, considering solar irradiation as the primary energy input. It accounts for the conversion of solar power into electricity and the storage of thermal energy within the PV module, which subsequently increases the PV temperature leading to decay in its electrical efficiency. Heat loss from the top and bottom surfaces of the module, with variations for cooling techniques, is also included. The model is validated against three different experimental studies, demonstrating good agreement with the experimental temperature variations, with a maximum discrepancy ranging from 4 % to 16 %. This simplified model is then applied to two case studies: one representing typical daytime conditions and another simulating cold weather events. By comparing the PV temperature dynamics between the model and the experimental data, it is shown that the model accurately captures the dynamic response of the PV temperatures to changes in solar irradiation and ambient temperatures. Moreover, a parametric analysis is conducted to investigate the effects of key parameters on PV temperature and efficiency. Higher cooling rates from the bottom surface significantly boost electric power output, aligning closely with solar irradiance variations. Enhanced upper surface cooling, through increased convection, reduces PV temperature and thereby improves electric efficiency and power production. The PV temperature increases quasi-linearly with ambient temperature, especially at lower wind speeds, and decreases with higher wind speeds, eventually stabilizing at high values. Additionally, both ambient temperature and solar irradiance contribute to rising PV temperatures, with ambient temperature having a slightly greater effect. These findings underscore the critical role of cooling techniques in optimizing PV performance amidst varying environmental conditions.

Assessing the potential spray drift of a six-rotor unmanned aerial vehicle sprayer using a test bench and airborne drift collectors under low wind velocities: impact of atomization characteristics and application parameters.

The unmanned aerial spraying systems (UASS) have gained widespread use for plant protection in recent years. However, spray drift from UASS is a major concern when controlling weeds over large areas and warrants a thorough investigation. This study examined the atomization characteristics of the herbicide florpyrauxifen-benzyl under downwash airflow using a UASS spray test platform. Potential spray drift was assessed using a test bench (TB) and airborne drift collectors (ADCs) in the field under low wind speeds (<1 m s-1). Atomization characteristics were significantly affected by the spray liquid, adjuvant, nozzle type and spray pressure. The addition of an adjuvant reduced the liquid sheet length, improved physicochemical properties and increased droplet size under the downwash airflow field. Drift evaluation in the field using the TB revealed that sediment spray drift predominantly occurred from the middle to the entire length of the device when fine-to-medium droplets were produced after the sprayer passed. ADC assessment found that higher flight altitudes and finer droplets resulted in higher drift values, whereas the addition of an adjuvant and the use of an air-induction nozzle reduced drift <3 m aboveground. The combination of using TB in the target area and ADCs in the off-target area as an alternative method to determine residual droplets in the current airflow provided valuable insights into airborne drift assessment for UASS. © 2024 Society of Chemical Industry.

Two‐way feedback between the Madden–Julian Oscillation and diurnal warm layers in a coupled ocean–atmosphere model

AbstractDiurnal warm layers develop in the upper ocean on sunny days with low surface wind speeds. They rectify intraseasonal sea‐surface temperatures (SSTs), potentially impacting intraseasonal weather patterns such as the Madden–Julian Oscillation (MJO). Here we analyse 15‐lead‐day forecast composites of coupled ocean–atmosphere and atmosphere‐only numerical weather prediction (NWP) models of the UK Met Office to reveal that the presence of diurnal warming of SST (dSST) leads to a faster MJO propagation in the coupled model compared with the atmosphere‐only model. To test the feedback between the MJO and the dSST, we designed a set of experiments with instantaneous vertical mixing over the top 5 or of the ocean component of the coupled model. Weaker dSST in the mixing experiments leads to a slower MJO over 15 lead days. The dSST produces a increase in the MJO phase speed between the coupled and the atmosphere‐only model. An additional increase is found for other coupling effects, unrelated to the dSST. A two‐way feedback manifests in the coupled model over the 15 lead days of the forecast between the MJO and the dSST. The MJO regime dictates the strength of the dSST and the dSST rectifies the intraseasonal anomalies of SST in the coupled model. Stronger dSST in the coupled model leads to stronger intraseasonal anomalies of SST. The MJO convection responds to these SSTs on a seven‐lead‐day timescale, and feeds back into the SST anomalies within the next three lead days. Overall, this study demonstrates the importance of high vertical resolution in the upper ocean for predicting the eastward propagation of the MJO in an NWP setting, which is potentially impactful for seasonal predictions and climate projections, should this feedback be unrepresented in the models.

Probabilistic assessment of short‐term voltage stability under load and wind uncertainty

AbstractContemporary electricity networks are exposed to operational uncertainties, which may jeopardise the stability of the power grid. More specifically, the increasing penetration level of variable renewable energy generation and uncertainty in load demand are key catalysts for these emerging stability issues. A mathematical relationship is established to track the system voltage trajectory with respect to variations in uncertain inputs (associated with wind speed, system load, and wind power penetration levels). Additionally, it demonstrates the consequences of varying uncertain inputs on the short‐term voltage response across different potential operating conditions. The theoretical proposition has further been verified by the simulation studies with two test power networks in DIgSILENT PowerFactory software. The simulation results revealed that uncertain injection sources significantly impacted the system voltage at the receiving end. High uncertainty in wind speed and system loads increased voltage recovery variation, causing delays in voltage response during low wind speeds and high system loads. Additionally, increased wind power penetration levels expanded voltage recovery uncertainties, resulting in decreased system voltage and potentially leading to voltage violations and instability at 30% wind power levels. Moreover, the results showed that the system's response time increased, and in some cases, it collapsed due to increased system capacity (&gt;80%) and dynamic load (&gt;75%), as well as encountering a large disturbance under uncertain circumstances.

Application of polyhedral meshes in large-eddy simulation of building array flow fields within neutral and unstable boundary layers

Urbanization has intensified the urban heat island phenomenon, a critical concern for human habitation in recent decades. Analyzing flow fields around building arrays is vital for mitigating issues such as heatstroke and air pollution, thereby enhancing human comfort in urban environments. However, challenges in accuracy and computational efficiency persist in simulating urban thermal environments. This study evaluates how boundary layer meshes in polyhedral meshes affect wind and temperature distributions in neutral and unstable boundary layers, utilizing large-eddy simulation (LES). A total of ten cases were examined, including eight with local mesh refinement and two without. The study demonstrates that local mesh refinement is an effective strategy to reduce mesh count by approximately 60 %, thereby reducing computational time by 60 % using the same computational configuration. The performance of the polyhedral meshes was found to be comparable to that of the hexahedral meshes. For the mean velocity and temperature, the discrepancies between the hexahedral and polyhedral cases were within 10 %, while for the second-order statistics, the discrepancies were within 20 %. Within the neutral boundary layer, boundary layer meshes on the ground and building surfaces had minimal impact on the wind field. However, these meshes enhanced the accuracy of low percentile wind speed estimations by approximately 20 % within the unstable boundary layer. Moreover, this study lays the foundation for research on the thermal environment in actual urban areas and contributes to the guidelines for LES with non-isothermal inflow conditions.

Charge generation by passive plant leaf motion at low wind speeds: design and collective behavior of plant-hybrid energy harvesters

Energy harvesting techniques can exploit even subtle passive motion like that of plant leaves in wind as a consequence of contact electrification of the leaf surface. The effect is strongly enhanced by artificial materials installed as ‘artificial leaves’ on the natural leaves creating a recurring mechanical contact and separation. However, this requires a controlled mechanical interaction between the biological and the artificial component during the complex wind motion. Here, we build and test four artificial leaf designs with varying flexibility and degrees of freedom across the blade operating on Nerium oleander plants. We evaluate the apparent contact area (up to 10 cm2 per leaf), the leaves’ motion, together with the generated voltage, current and charge in low wind speeds of up to 3.3 m s−1 and less. Single artificial leaves produced over 75 V and 1 µA current peaks. Softer artificial leaves increase the contact area accessible for energy conversion, but a balance between softer and stiffer elements in the artificial blade is optimal to increase the frequency of contact-separation motion (here up to 10 Hz) for energy conversion also below 3.3 m s−1. Moreover, we tested how multiple leaves operating collectively during continuous wind energy harvesting over several days achieve a root mean square power of ∼6 µW and are capable to transfer ∼80 µC every 30–40 min to power a wireless temperature and humidity sensor autonomously and recurrently. The results experimentally reveal design strategies for energy harvesters providing autonomous micro power sources in plant ecosystems for example for sensing in precision agriculture and remote environmental monitoring.

Effects of Wind Speed and Heat Flux on De-Icing Characteristics of Wind Turbine Blade Airfoil Surface

The icing on wind turbines reduces their aerodynamic performance and can cause other safety issues. Accordingly, in this paper, the de-icing characteristics of a wind turbine blade airfoil under different conditions are investigated using numerical simulation. The findings indicate that when the de-icing time is 10 s, the peak ice thickness on the leading edge of the airfoil surface decreases from 0.28 mm to 0.068 mm and from 0.77 mm to 0.45 mm at low (5 m/s) and high (15 m/s) wind speeds, respectively. This is due to the fact that the ice melting rate is much greater than the icing rate at low wind speeds, while the icing rate increases at high wind speeds. When the de-icing time is 20 s, ice accretion on the leading edge of the airfoil is completely melted. At a low heat flux (8000 W/m2) and high heat flux (12,000 W/m2), the peak ice thickness decreases by 31.2% and 64.9%, respectively. With an increase in de-icing time and heat flux, the peak thickness of runback ice increases. This is due to an increase in runback ice as a result of more ice melting on the leading edge of the airfoil. The surface temperature in the ice-free area is significantly higher than that in the ice-melting area, due to high thermal resistance in the ice-free area. This study will provide guidance for the thermal distribution and coating layout of a wind turbine blade airfoil to make the anti-/de-icing technology more efficient and energy-saving.