Variations In Wind Direction Research Articles

Assesment of wind potential energy of four major cities in Cote d’ivoire using satellite data from 2015 to 2022

In Côte d’Ivoire, the use of renewable energy is becoming a major challenge in the context of climate change and global warming. Therefore, the aim of this study is to characterize the wind profile for potential energy in Abidjan, Man, Bouaké and Korhogo of Côte d’Ivoire using satellite data. Wind speed and direction data from Copernicus Centre from 2015 to 2022 are used for these major cities. The frequency method is used to build wind rose and power density formula to estimate the potential energy for each city, at ground level (10 m) and in particular level at 100 m and 500 m. The results showed a slightly wind speed and direction variation from 2015 to 2022. This suggests that wind has a seasonal (wet and dry) evolution over the year. However, wind speeds increase for each city with the altitude. The higher average wind speed is observed in Abidjan with a power density of 58 W/m2. The lower wind speed and power density are observed in Man, Bouaké and Korhogo. So, the coastline of Côte d’Ivoire has the higher wind energy resource than the inland regions of the country.

Analysis of wind resources in Senegal using 100-meter wind data from ERA5 reanalysis

This study conducts a climatological analysis of wind resources in Senegal, utilizing wind data at 100 m from the ERA5 reanalysis. The results reveal spatio-temporal variations in wind speeds and directions, while also identifying favorable areas for the establishment of wind farms. Wind speeds range from 3 to 9 m/s, with increasing gradients in the north-south and west-east directions. Maximum speeds are observed in winter and spring, especially from December to May. A distinct daily pattern is observed, with the highest speeds at night and lower speeds during the day. On average, nighttime wind speeds are 21 % higher than daytime speeds. Most of the time, the wind blows from optimal directions, ranging from northwest to northeast. The study successfully identifies three particularly suitable zones for wind farm installation: the northwest coast between Saint-Louis and Dakar, the smaller coast between Dakar and Mbour, and the extreme north of the country bordering Mauritania between Saint-Louis and Matam. Furthermore, a long-term analysis highlights evolving wind trends. Trends and anomalies indicate a 7 % increase in winds at 100 m. In conclusion, this study provides a precise assessment of wind resources in Senegal, emphasizing that the most favorable zones are along the coast and in regions near the desert. This information is crucial for the sustainable development of wind energy in the region.

Catch the wind: Optimizing wind turbine power generation by addressing wind veer effects.

Wind direction variability with height, known as "wind veer," results in power losses for wind turbines (WTs) that rely on single-point wind measurements at the turbine nacelles. To address this challenge, we introduce a yaw control strategy designed to optimize turbine alignment by adjusting the yaw angle based on specific wind veer conditions, thereby boosting power generation efficiency. This strategy integrates modest yaw offset angles into the existing turbine control systems via a yaw-bias-look-up table, which correlates the adjustments with wind speed, and wind veer data. We evaluated the effectiveness of this control strategy through extensive month-long field campaigns for an individual utility-scale WT and at a commercial wind farm. This included controlling one turbine using our strategy against nine others in the vicinity using standard controls with LiDAR-derived wind veer data and a separate 2.5 MW instrumented research turbine continuously managed using our method with wind profiles provided by meteorological towers. Results from these campaigns demonstrated notable energy gains, with potential net gains exceeding 10% during extreme veering conditions. Our economic analysis, factoring in various elements, suggests an annual net gain of up to approximately $700 K for a 100-MW wind farm, requiring minimal additional investment, with potential for even larger gains in offshore settings with the power of individual turbines exceeding 10 MW nowadays. Overall, our findings underscore the considerable opportunities to improve individual turbine performance under realistic atmospheric conditions through advanced, cost-effective control strategies.

Harvesting multidirectional wind energy based on flow-induced vibration triboelectric nanogenerator with directional tuning mechanism

Wind-induced vibration (WIV), as low-velocity wind energy utilization technology in agricultural environment, has significant advantages. Nevertheless, there is a mismatch between the variability of wind direction and the work mode of triboelectric nanogenerator (TENG), which leads to a sharp decline in the performance of triboelectric power generation. This work proposes a TENG based on multidirectional WIV (TENG-WIV), which mainly contains triboelectric power generation unit, guide-wing and triboelectric direction sensor. By introducing the directional tuning mechanism based on guide-wing, the TENG-WIV aims to break the constraints of single wind direction response and effectively respond to the wind direction within 360° range, thereby realizing multidirectional wind energy harvesting and sensor power supply. The empirical findings indicate that the output voltage and current of triboelectric power generation unit are in the ranges of 62–241 V and 0.25–1.21 μA, respectively, at the wind velocities of 1.23–5.13 m/s. At a wind velocity of 3.18 m/s, the unit achieves an out-power peak of 0.09 mW. Furthermore, the triboelectric direction sensor can respond to changes in 8 directions and has wind direction monitoring potentiality. The directional tuning mechanism endows flow-induced vibration energy harvesters with an all-around multidirectional sensitivity.

Artificial Intelligence enabled self-powered sensing and wind energy harvesting system for bridges monitoring

With the advancement of society, the significance of Artificial Intelligence (AI) and big data in facilitating the intelligent development of bridges is increasing. Bridges function as crucial urban links, and ensuring their safe and stable operation is paramount. Therefore, this paper proposes an Artificial Intelligence enabled self-powered sensing wind energy harvesting system (WEHS) designed specifically for bridges. The system mainly consists of two electromagnetic generators (EMG1 and EMG2) and a rolling ball triboelectric nanogenerator (RB-TENG). WEHS achieves the recycling of ambient wind energy through the lower electromagnetic generator (EMG2). A single-channel triboelectric nanogenerator (RB-TENG) enables the sensing of wind speed within the system, while the top electromagnetic generator (EMG1) utilizes a single magnet with coils of different diameters to realize wind direction sensing. Reynolds wind tunnel that the WEHS has an average power output of 392 mW and a power density of 414.86 W/m3, which is adequate to power the sensor. The accuracy of wind speed and direction prediction based on the Artificial Intelligence GRU deep learning algorithm model is 96 % and 99.04 %, respectively. Furthermore, by implementing deep learning models for wind speed and direction, it becomes feasible to forecast the variations in wind direction and speed in the vicinity of bridges. The WEHS exhibits significant potential for bridges application and can contribute to developing AIoT-based smart bridges.

Protective benefits of HDPE board sand fences in an environment with variable wind directions on Gobi surfaces: wind tunnel study

Protective benefits of HDPE board sand fences in an environment with variable wind directions on Gobi surfaces: wind tunnel study

Optical and Thermal Modeling of a Cylindrical– Hemispherical Receiver

Receiver geometry optimization is a crucial domain in enhancing the efficiency of parabolic dish concentrators. This optimization necessitates thoroughly scrutinizing the receiver's optical and thermal characteristics. The present study engages in a cylindrical-hemispherical receiver's optical and thermal modeling. Set at an aperture of 0.150m, the receiver's height varies between 0.150m to 0.160m. Sol-Trace software is employed for the optical modeling to analyse the total amount of heat flux absorbed by the receiver that has been concentrated using parabolic dish. The diameter of the is taken as 3m, and the receivers placement is varied between 1.55m and 2.05m from the dish concentrator to obtain the optimal distance for absorbing maximum heat flux. From the results optimal distance has been observed as 1.8m from the dish concentrator. Maximum heat flux of 6664.56W has been absorbed by the receiver surface at this position. Additionally, thermal modeling using CFD has been conducted to calculate convective heat losses from the receiver under different wind conditions, such as varying wind direction and wind velocity. The maximum convective heat loss observed under head-on wind conditions is 772.73W, while under back-on wind conditions, it is 606.84W

Seasonal and geographic viability of high altitude balloon navigation

Control of the geographic location of high-altitude balloons has been desired for decades due to the cost and simplicity of the systems. These balloon systems rely on variations in wind direction with altitude so that when a balloon changes height, it also changes the direction of its horizontal motion. An altitude control system can thus also control the horizontal position by transitioning to a wind layer with favorable winds. The system’s ability to navigate successfully thus relies on the existence of certain wind conditions. In this paper, we explore how the ability of a balloon to station-keep varies based on the geographic location and season. We used spatially and temporally variant ERA5 wind data with a tree-search-based algorithm to traverse potential trajectories, and we selected the altitude transitions that maximize time within 50 km of a target. The simulation’s outputs show large variations in success across both latitude and season. Midlatitudes are particularly challenging for station-keeping, while lower latitudes are more favorable. Summer is typically more favorable than winter. This demonstrates that for all balloon systems, the ability to station-keep is highly variant and not universally possible.

Simulation of ambient temperature and humidity distribution in eight-span greenhouse under different wind conditions and corn height

The greenhouse environment suitable for crop growth usually depends on reasonable ventilation, and outdoor air flow state and indoor crop height are important factors affecting the ventilation effect. This study conducted simulation and experimental research on corn cultivation in an eight-span plastic greenhouse, analyzing the effects of outdoor wind speed, wind direction, and indoor crop height variations on the uniformity of temperature and relative humidity distribution as well as the air flow dynamics within the greenhouse. The study shows that as outdoor wind speed increases from 0.5 m/s to 2.5 m/s, for every 1 m/s increment, temperatures decrease by 2.2 °C and 0.9 °C, respectively. The increase in outdoor wind speed resulted in uniformity of greenhouse temperature and humidity along the vertical height, but exacerbated the degree of inhomogeneity in the east-west direction. The change in outdoor wind direction leads to differences in airflow direction and ventilation volume. On the south side of the greenhouse, while temperature is lower, the relative humidity is higher, with maximum differences in temperature and humidity between the north and south sides being 0.6 °C and 1.5 %, respectively, when the northeast wind blows. The east wind has the best cooling effect. The uneven degree of temperature and humidity distribution in the east and west direction of the greenhouse is positively correlated with crop height. This study can provide valuable insights for adjusting and optimizing natural ventilation strategies in large multi-span greenhouses.

Practical method for evaluating wind influence on autonomous ship operations (2nd report)

Recently, a considerable number of research and development projects have focused on automatic vessels. A highly realistic simulator is needed to validate control algorithms for autonomous vessels. For instance, when considering the automatic berthing/unberthing of a vessel, the effect of wind in such low-speed operations cannot be ignored because of the low rudder performance during slow harbor maneuvers. Therefore, a simulator used to validate an automatic berthing/unberthing control algorithm should be able to reproduce the time histories of wind speed and wind direction realistically. Therefore, in our first report on this topic, to obtain the wind speed distribution, we proposed a simple algorithm to generate the time series and distribution of wind speed only from the mean wind speed. However, for wind direction, the spectral distribution could not be determined based on our literature surveys, and hence, a simple method for estimating the coefficients of the stochastic differential equation (SDE) could not be proposed. In this study, we propose a new methodology for generating the time history of wind direction based on the results of Kuwajima et al.’s work. They proposed a regression equation of the standard deviation of wind direction variation for the mean wind speed. In this study, we assumed that the wind direction distribution can be represented by a linear filter as in our previous paper, and its coefficients are derived from Kuwajima’s proposed equation. Then, as in the previous report, the time series of wind speed and wind direction can be calculated easily by analytically solving the one-dimensional SDE. The joint probability density functions of wind speed and wind direction obtained by computing them independently agree well with the measurement results.

The Calm and Variable Inner Life of the Atlantic Intertropical Convergence Zone: The Relationship Between the Doldrums and Surface Convergence

AbstractThe doldrums are regions of low wind speeds and variable wind directions in the deep tropics that have been known for centuries. Although the doldrums are often associated with the Intertropical Convergence Zone (ITCZ), the exact relationship remains unclear. This study re‐examines the relationship between low‐level convergence and the Atlantic doldrums. By analyzing the frequency distribution of low wind speed events in reanalysis and buoy data, we show that the doldrums are largely confined between the edges of the ITCZ marked by enhanced surface convergence. While the region between the edges is a region of high time‐mean precipitation, low wind speed events occur in the absence of precipitation. Based on these results, we hypothesize that low wind speed events occur in regions of low level divergence rather than convergence.

Satellite observations of Arctic blowing dust events >82°N

AbstractThis study reports satellite evidence for the most northerly blown dust activity yet observed on Earth. A systematic inspection of high‐resolution satellite imagery identified active dust events and their sources >82°N in Peary Land, Greenland. In the absence of any local weather measurements, for all observed dust activity a focus period in April 2020 with multiple dust plumes, reanalysis climate data found the majority of dust events to be associated with wind speeds exceeding a typical threshold value for blowing sand and dust uplift. Wind direction variability points to dust‐raising by cold airflow down‐valley winds, likely from nearby ice masses.

Simulation of Water Isotopes in Combustion‐Derived Vapor Emissions in Winter

AbstractWith urbanization, anthropogenic water vapor emissions have become a significant component of the urban atmosphere. Fossil fuel combustion‐derived vapor (CDV) is a primary source of these emissions. Owing to the notably low CDV d‐excess, stable hydrogen and oxygen isotopes are promising for distinguishing CDV from natural sources. Considering the limitations of in situ observations, this study aims to explore the feasibility of using IsoRSM, an isotopically enabled regional atmospheric model, to simulate CDV emissions in urban areas in winter. Two experiments were conducted: one in Salt Lake City (SLC) in January 2017 and another in Beijing in January 2007. The simulation results showed that the CDV addition significantly reduced the water vapor d‐excess, particularly when the boundary layer was stable. The simulation with CDV emissions aligned better with the time series of in situ observations in SLC. The modification led to a more pronounced positive correlation between vapor d‐excess and specific humidity, which was similar to the observation of SLC. The CDV inclusion significantly increased the vapor d‐excess variability with varying wind directions in both sites. However, in Beijing, the underestimation of d‐excess variation from natural sources caused a bigger discrepancy between the observed and simulated d‐excess and CDV fraction. Thus, though there were still biases, the inclusion of CDV could improve the accuracy of isotopic simulation in the urban regions where CDV was one of the controlling factors of vapor d‐excess.

On the importance of wind predictions in wake steering optimization

Abstract. Wake steering is a technique that optimizes the energy production of a wind farm by employing yaw control to misalign upstream turbines with the incoming wind direction. This work highlights the important dependence between wind direction variations and wake steering optimization. The problem is formalized over time as the succession of multiple steady-state yaw control problems interconnected by the rotational constraints of the turbines and the evolution of the wind. Then, this work proposes a reformulation of the yaw optimization problem of each time step by augmenting the objective function by a new heuristic based on a wind prediction. The heuristic acts as a penalization for the optimization, encouraging solutions that will guarantee future energy production. Finally, a synthetic sensitivity analysis of the wind direction variations and wake steering optimization is conducted. Because of the rotational constraints of the turbines, as the magnitude of the wind direction fluctuations increases, the importance of considering wind prediction in a steady-state optimization is empirically demonstrated. The heuristic proposed in this work greatly improves the performance of controllers and significantly reduces the complexity of the original sequential decision problem by decreasing the number of decision variables.

Application of uncertainty quantification techniques in the framework of process safety studies: Advanced dispersion simulations

AbstractIn the framework of process safety studies, consequence assessment of accidental scenarios is a crucial step affecting the eventual risk profile associated with the facilities under analysis. Conventional models used for consequence assessment are based on integral models, and may not be adequate to cope with the dynamic evolution of accidental scenarios and their three‐dimensional features. On the other hand, consequence assessment models based on computational fluid dynamics (CFD) approaches are promising to cope with complex scenarios and environments, but setting the simulation introduces relevant uncertainties associated with both the input data, assumptions, and with the modelling of physical effects involved. In the present study, uncertainty quantification (UQ) techniques are applied to support advanced safety studies based on CFD simulations of hazardous gas dispersion. Firstly, the accidental scenarios are characterized by defining release scenarios and conditions and quantifying source terms using integral models. At the same time, input meteorological data are gathered. This enables the development of high‐fidelity CFD simulations of gas dispersion based on different input sets and eventually the implementation of UQ techniques. The generalized polynomial chaos (gPC) expansion is employed to obtain hazardous gas concentration based on the variation of wind direction and speed. The present method is applied for the analysis of a real plant featuring a complex layout. The results show the advantages of the present approach by quantifying the influence of meteorological conditions and providing indications for supporting the development of protection systems and emergency measures.

Fluid-structure interaction in inflatable membrane structures: A featured study of mechanical behavior

This paper presents a comprehensive analysis of fluid-structure interaction (FSI) effects on the behavior of inflatable membrane structures under wind loads, aiming to enhance the understanding of the structural performance. We adopted a mixed-methods approach, incorporating a numerical simulation based on the RNG k-ε turbulence model. The work focused on a semi-cylindrical air-supported membrane structure examining the effects of varying wind directions, speeds, and internal pressures. Specifically, we investigated the displacement and stress on the inflatable membrane structure under wind velocities of 12, 15, and 18 m/s, taking into account the internal air pressure. Consequently, we determined the influence coefficients for displacement and stress, representing the ratio of FSI effects to those in a static case. Our findings indicate that the displacement coefficient is inversely related to internal pressure, whereas the stress coefficient directly correlates with wind speed. The maximum displacement and stress under FSI conditions can be calculated by applying an influence coefficient to the static case results. The findings from this study offer a convenient and reliable method for predicting the actual structural response. This paper provides valuable insights for the design and optimization of inflatable membrane structures to withstand wind loads, a topic that has been underexplored in previous studies. Future studies should investigate additional cases to further enhance the reliability of this method.

Onsite measurements of pedestrian-level wind and preliminary assessment of effects of turbulence characteristics on human body convective heat transfer

Wind is an important factor affecting outdoor thermal comfort in urban environment, but its turbulence characteristics at the pedestrian level and effects on convective heat transfer (hc) from a human body remain poorly understood. Previous studies were only conducted in wind tunnels with low turbulence intensity, small turbulence integral length scale, and fixed prevailing wind direction. To address this knowledge gap, this field study was conceived to monitor the pedestrian level wind in actual outdoor settings and to measure the hc of a thermal manikin at the same time. The results show that both longitudinal and lateral turbulence intensity significantly enhance whole body hc. It remains to be examined whether the small-scale wind direction variation can be included in the lateral turbulence intensity or vice versa. The integral length scale found at the two sites were larger than a typical manikin dimension, and the whole body hc peaked when these two scales were comparable. As the first major field study to quantify convection heat loss of a thermal manikin exposed to real-life urban boundary layer wind, it is demonstrated crucial to consider realistic turbulence characteristics in the field when evaluating hc over a human body for urban microclimate design.

Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production

AbstractWind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production.

Time-Series Embeddings from Language Models: A Tool for Wind Direction Nowcasting

Wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation operations and safety. In this context, the TELMo (Time-series Embeddings from Language Models) model, a sophisticated deep learning architecture, has been introduced in this work for enhanced wind-direction nowcasting. Developed by using three years of data from multiple stations in the complex terrain of an international airport, TELMo incorporates the horizontal u (east–west) and v (north–south) wind components to significantly reduce forecasting errors. On a day with high wind direction variability, TELMo achieved mean absolute error values of 5.66 for 2-min, 10.59 for 10-min, and 14.79 for 20-min forecasts, processed within a swift 9-ms/step timeframe. Standard degree-based analysis, in comparison, yielded lower performance, emphasizing the effectiveness of the u and v components. In contrast, a Vanilla neural network, representing a shallow-learning approach, underperformed in all analyses, highlighting the superiority of deep learning methodologies in wind direction nowcasting. TELMo is an efficient model, capable of accurately forecasting wind direction for air traffic operations, with an error less than 20° in 97.49% of the predictions, aligning with recommended international thresholds. This model design enables its applicability across various geographical locations, making it a versatile tool in global aviation meteorology.

Comparison of helix and wake steering control for varying turbine spacing and wind direction

A variety of wind farm control strategies exist in order to reduce unfavorable wake effects in large wind farms. While strategies like wake steering already reached a high maturity level, it is interesting to compare them to more recently proposed strategies. Such a comparison can form the basis for the development of a symbiotic wind farm control toolbox, from which a control strategy is chosen and activated depending on the operating conditions. The present study compares wake steering with helix control across a wide range of turbine spacings and wind directions using large-eddy simulation (LES). The size of the search space is made computationally tractable for LES by adopting a setup based on one physical upstream turbine and a distribution of virtual downstream turbines which do not exert any thrust force. It is found that helix control is beneficial for full wake overlap and turbine spacing of less than six rotor diameters whereas wake steering proves to be optimal further downstream and for partial wake overlap. Furthermore, the results show that the helix control setpoint in the proximity of full wake overlap scenarios is less susceptible to wind direction variations. This finding indicates that the combination of wake steering and helix control has potential for the design of a wind farm controller which is more robust in full wake overlap scenarios and can reduce the need for large yaw offset adjustments.