Variations In Wind Direction Research Articles

LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment

This work presents the validation of an extended version of the control-oriented, dynamic wind farm flow solver SPLINTER. The two-dimensional model is applied to use cases of wake steering by yaw misalignment and inflow wind direction variations and the results are compared to large-eddy simulations (LES). While SPLINTER is able to reproduce the antagonal behaviour of decreasing upstream and increasing downstream turbine power under wake deflection, a systematic deviation of the downstream power is detected and quantified, which is connected to underrepresented three-dimensional wake effects. In case of changing inflow wind direction, SPLINTER is capable of computing movement and shape of the bending wakes. The model smooths small-scale turbulent structures and disturbances and does not reproduce wake meandering, but manages to describe the evolution of the mean flow, which is tested by averaging over an ensemble of LES and comparing the resulting flow fields and turbine power time series. Under dynamic inflow conditions, SPLINTER is able to predict at which time intervals and at which rates downstream turbines will be influenced by wakes, which can improve the accuracy of short-term power and load forecasting and enables its application to online model predictive wind farm control.

Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel

This paper introduces a framework to assess a hybrid data-driven wake steering strategy combining the advantages of an analytical model for the initial data gathering together with a data-driven model for modelling error correction. The main building blocks of the control strategy (required input domain, data gathering and model generation) are evaluated within the wind tunnel, utilising a newly developed setup that can simulate dynamic wind direction variations and which was specifically designed to examine wake steering control strategies. The control strategy employs a machine-learning algorithm to generate a turbine and wake model based on the measurement data, allowing this approach to be extrapolated for an arbitrarily large wind farm. The machine-learning model is used to estimate the optimal yaw angle in a closed-loop format. To implement the developed control strategy, steady measurements are analysed first to determine the required input variables. This is followed by an investigation of the potential power gain and the impact of utilising a grid-search procedure. The procedure minimises adverse conditions as it collects the required data points, still achieving a power gain.

Spatio-temporal Analysis of the factors affecting NOx concentration during the evaluation cycle of high pollution episodes in Tehran metropolitan

Tehran, the capital of Iran, experiences high levels of pollution, particularly during the cold seasons. Recent studies have shown an increase in the concentration of nitrogen compounds in Tehran. However, the role of advection, deposition, vertical diffusion processes and chemical conversion of NOx in this region requires further investigation. In this study, The Weather Research and Forecasting model with Chemistry (WRF-Chem) was employed to numerically simulate each of these processes during a severe pollution episode in winter from November 28, 2021, to December 8, 2021. During this high pollution episode, the weather conditions were calm, and based on the Air Quality Index (AQI), the air quality was unhealthy for sensitive groups. The presence of a ridge at the 500 hPa level, the placement of a low-level high-pressure cell over the region, and consequently, atmospheric subsidence and low wind speed create favorable conditions for severe pollution events in Tehran. Additionally, temperature inversion during the night and a decrease in the boundary layer height contribute to increased surface NOx concentration during this period. When comparing different processes influencing NOx concentrations, it has been observed that advection has the most significant impact, while dry deposition and chemical conversion have the least effect. On stable days advection increases NOx concentration in central and northeast of Tehran urban area during daytime. The maximum vertical diffusion is observed in the southern and western regions, corresponding to the emission pattern of NO and NO2. The examination of the vertical profile of advection and vertical diffusion shows that vertical diffusion dominates near the surface layer, while advection dominates at higher altitudes in terms of NOx concentration changes. Under unstable conditions (Dec-02), with arrival of the cold front and the influence of convective clouds, the impact of convective transport becomes more pronounced in reducing NOx concentrations in the southern and central regions of Tehran urban area. The highest NOx deposition is observed in the early morning in the southern and southwestern suburban areas of Tehran, correlating with wind direction variations, NOx concentration, and vegetation coverage. The results indicate that nearly 98% of the total NOy deposition (dry and wet deposition of NO, NO2, HNO3, and NO3) in Tehran's urban and suburban area is attributed to dry deposition of NO2.

Integrating Pix2Pix and computational fluid dynamics for enhanced indoor airflow prediction: A case study with wing-walls

Computational Fluid Dynamics (CFD) is often used for analyzing natural ventilation but is limited in early design stages due to its high computational demands and the need for detailed inputs. To overcome such limitations of CFD simulation, this paper proposes a method using a conditional Generative Adversarial Network (cGAN) based image-to-image translation (Pix2Pix) to predict the indoor airflow condition. Compared to other traditional machine learning models that predict averaged values like indoor air velocity, Pix2Pix can predict contour plot of indoor airflow for the given building floor plan image, which provides more intuitive feedback to designers. The proposed method was tested to understand indoor air movement caused by wing-walls attached to the windows. The model was trained using 1153 pairs of floor plan images, incorporating variations in window widths, wing-wall depth, wing-wall angle, wind speed, and wind direction. It achieved a prediction accuracy of 94 % and produced results in less than a second. Moreover, the model showed relatively better performance with the changes in windows and wing-walls properties rather than wind properties. This high performance indicates that Pix2Pix can serve as an efficient proxy model of conventional CFD simulations, helping designers optimize ventilation in building designs at an early stage without the need for complex inputs.

Estimating emission flux of H2S from fumarolic fields using vertical sensor array system

The emission flux of volatiles from each fumarolic field in volcanic and geothermal areas can be used to evaluate the current state of magmatic activity and predict its future trends. The emission flux of SO2 has been quantified in many fumarolic fields using remote sensing techniques, such as differential optical absorption spectroscopy (DOAS). However, most of these remote sensing techniques are inapplicable to fumarolic fields emitting volatiles depleted in SO2 to which most of the geothermal fields are classified. In this study, we developed a vertical sensor array system to quantify the emission flux of H2S from each fumarolic field by integrating the cross-sectional distributions of H2S concentrations in the volcanic plume using the vertical sensor array system. In Iwo-yama of the Kirishima volcanic complex, the cross-sectional distribution of H2S concentrations was determined using the walking traverse method by moving the vertical sensor array system in the plume perpendicular to the direction of plume transport. The emission flux of SO2 (2.2 ± 0.4 ton SO2/day) was estimated from that of H2S using the walking traverse method (2.6 ± 0.5 ton H2S/day) and the molar ratio of the plume (SO2/H2S=0.45) corresponds well with that estimated optically by JMA. We concluded that the emission flux quantified using the vertical sensor array system was reliable. In the Oyunuma pond in the Kuttara volcano, the emission flux of H2S was quantified as 2.0 ton H2S/day through the fixed point method, wherein the vertical sensor array system was fixed in one point, whereas the cross sectional distribution of H2S in the plume was estimated using the natural variation in wind direction. The topography is often irregular and wind direction is variable in most fumarolic fields; thus, in general, the fixed point method should be more suitable to determine the emission flux of H2S from fumarolic fields, wherein H2S occupies a major portion of the total sulfur emission.

Control-oriented modelling of wind direction variability

Abstract. Wind direction variability significantly affects the performance and lifetime of wind turbines and wind farms. Accurately modelling wind direction variability and understanding the effects of yaw misalignment are critical towards designing better wind turbine yaw and wind farm flow controllers. This review focuses on control-oriented modelling of wind direction variability, which is an approach that aims to capture the dynamics of wind direction variability for improving controller performance over a complete set of farm flow scenarios, performing iterative controller development and/or achieving real-time closed-loop model-based feedback control. The review covers various modelling techniques, including large eddy simulations (LESs), data-driven empirical models, and machine learning models, as well as different approaches to data collection and pre-processing. The review also discusses the different challenges in modelling wind direction variability, such as data quality and availability, model uncertainty, and the trade-off between accuracy and computational cost. The review concludes with a discussion of the critical challenges which need to be overcome in control-oriented modelling of wind direction variability, including the use of both high- and low-fidelity models.

THE RELATIONSHIP OF THE DISTRIBUTION OF SEA SURFACE TEMPERATURE WITH RAINFALL AND WIND IN THE WATERS OF WEST SUMATRA

This research was carried out in February-March 2023 in the West Sumatra waters. It aimed to determine the variability of sea surface temperature, rainfall intensity, wind direction, and speed in the West Sumatra waters. Sea surface temperatures were taken from three stations, namely: in the coastal waters of Pariaman City, Padang-Pariaman Regency, and Bungus, Padang. The survey method was used, which was directly observed in the field. The results showed that the Sea Surface Temperature of the study areas ranged from 29.25oC-30.28oC, while the rainfall went from 211.97-499.89 mm, and the dominant speed was between 0.50-2.10 m/s. The correlation analysis between rainfall and sea surface temperatures in Station 1 yielded an "r" value of 0.871, a powerful correlation category, and the determination coefficient (R2) was 0.579 or 57.9% during the western season. In the east season, the "r" value was 0.321, in the moderate correlation category, and the determination coefficient was 0.103 or 10.3%. At Station 2, the correlation coefficient (r) was 0.788, a powerful correlation category, and the determination coefficient (R2) was 0.622 or 62.2% during the western season. In the east season, the "r" value was 0.699, a strong correlation category, and the determination coefficient was 0.488 or 48.8%. At Station 3, the "r" value was 0.688, a strong correlation category, and the determination coefficient was 0.477 or 47.7% during the western season. In the east season, the "r" value was 0.743, a strong correlation category, and the determination coefficient was 0.579 or 57.9%

Dynamic wind farm flow control using free-vortex wake models

Abstract. A novel dynamic economic model-predictive control strategy is presented that improves wind farm power production and reduces the additional demands of wake steering on yaw actuation when compared to an industry state-of-the-art reference controller. The novel controller takes a distributed approach to yaw control optimisation using a free-vortex wake model. An actuator-disc representation of the wind turbine is employed and adapted to the wind farm scale by modelling secondary effects of wake steering and connecting individual turbines through a directed graph network. The economic model-predictive control problem is solved on a receding horizon using gradient-based optimisation, demonstrating sufficient performance for realising real-time control. The novel controller is tested in a large-eddy simulation environment and compared against a state-of-the-art look-up table approach based on steady-state model optimisation and an extension with wind direction preview. Under realistic variations in wind direction and wind speed, the preview-enabled look-up table controller yielded the largest gains in power production. The novel controller based on the free-vortex wake produced smaller gains in these conditions while yielding more power under large changes in wind direction. Additionally, the novel controller demonstrated potential for a substantial reduction in yaw actuator usage.

Wind Farm Layout Optimization Based on Dynamic Opposite Learning-Enhanced Sparrow Search Algorithm

In recent years, the proportion of wind power in new energy generation has gradually increased. The natural wind in wind farms is subject to velocity attenuation by the wake effect, so improving the efficiency of wind farm power generation has become a problem that must be solved for wind power generation. Considering the uncertainty of wind farms, we regard wind farm layout optimization (WFLO) as a strongly nonlinear problem. In this paper, we improve the sparrow search algorithm (SSA) using dynamic opposite learning (DOL) strategy. Twenty-eight benchmark test results prove that compared with other algorithms, the improved algorithm DOLSSA has excellent robustness and the ability of searching for a better solution when solving a strongly nonlinear optimization problem, and the DOL strategy effectively improves the shortcomings of the original algorithm which is prone to local optimization and space limitation. In this paper, the authors establish the dynamic rotational coordinates of wind farms and set six different physical scenarios by considering the wind direction and wind speed variables, and the results prove that the performance of DOLSSA is optimal.

Experimental investigation of wind turbine stress and power characteristics under dynamic changes in wind direction

ABSTRACT The dynamic inlet conditions of variable wind direction had an obvious influence on the response of wind turbine stress and power, and accurately analyzing their changing characteristics is of great significance to enhancing wind turbines’ stability and efficient operation. Experiments were performed in a wind tunnel, where the wind turbine was mounted on a rotating platform to simulate dynamic changes in wind direction at prescribed speeds and angles. The wind turbine’s stress and power were measured. Results show that the stress was minimally reduced by 4.4% (tower) and 1.7% (blade) when the wind direction change angle was small. Maximum stress reduction of 92% (tower) and 93% (blade) with increasing wind direction change angle. As the wind direction change speed increased, the magnitude of the drop in blade stress decreased by 6% ~32%. The decrease in power coefficient and rotational speed at small wind direction change angles and large wind direction change speeds were not significant, and the fluctuations were more obvious. The coupling of aerodynamic deterioration and inertial effects during dynamic changes in wind direction was one of the main reasons for the variations in the wind turbine power and stress response.

Hold your breath! Air pollution and cognitive performance in Colombia

Air pollution is a known health hazard, and evidence of negative effects beyond the health dimension is rapidly emerging. This paper studies the effect on one non-health dimension, namely cognitive performance. It exploits exogenous variation in exposure to air pollution during secondary school exams and estimate the contemporaneous effect on students’ cognitive performance in Colombia between 2012 and 2018. The results indicate that exposure to air pollution on the day of the exam itself negatively impacts students’ performance. Using variation in wind direction as an instrument for air pollution, I find that a one standard deviation increase in air pollution reduces overall test scores by 0.05 standard deviations. For students who continue to tertiary education, there is no evidence that this distorted signal of their cognitive abilities at the secondary school exam translates into differences in college attendance, as proxied by college graduation rates.

Concepts for drone based pipeline leak detection.

The quickly developing drone technology can be used efficiently in the field of pipeline leak detection. The aim of this article is to provide drone mission concepts for detecting releases from pipelines. It provides an overview of the current applications of natural gas pipeline surveys, it considers environmental conditions by plume modelling, it discusses suitable commercially available sensors, and develops concepts for routine monitoring of pipelines and short term missions for localising and identifying a known leakage. Suitable platforms depend on the particular mission and requirements concerning sensors and legislation. As an illustration, a feasibility study during a release experiment is introduced. The main challenge of this study was the variability of wind direction on a time scale of minutes, which produces considerable differences to the plume simulations. Nevertheless, the leakage rates derived from the observations are in the same order of magnitude as the emission rates. Finally the results from the modeling, the release experiment and possible drone scenarios are combined and requirements for future application derived.

Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects

Abstract. Near-wake effects of wind turbine models using the free-vortex wake have been studied extensively, but there is a lack of validation for such predictions in the mid to far wake. This paper presents a novel validation study using three free-vortex wake models of increasing complexity: an actuator disc, an actuator disc with rotation, and a lifting-line model. We emphasise the application for dynamic wind farm flow control optimisation with a focus on wake redirection using yaw misalignment. For this purpose, wake models should provide sufficiently accurate power predictions at a low computational expense to enable real-time control optimisation. Three sets of wind tunnel data are used for validation: flow measurements under steady yaw misalignment, time-resolved flow measurements for a step change in yaw, and turbine output measurements with yaw control and simulated wind direction variation. Results indicate that the actuator-disc model provides the best balance between computational cost and accuracy in power predictions for the mid to far wake, which is not significantly improved upon by the addition of rotation. In the near wake, the added complexity of the lifting-line model may provide value as it models blade loading and individual tip vortices. Altogether, this study provides important validation for further studies into optimisation of wake steering under time-varying conditions and suggests that the actuator-disc model is a suitable candidate for use in a model-predictive wind farm flow control framework.

A novel spatio-temporal wind speed forecasting method based on the microscale meteorological model and a hybrid deep learning model

Wind power, as a renewable alternative to conventional fossil fuel energy sources, exhibits substantial variability owing to fluctuations in wind velocity. In light of this, the present study puts forward a novel approach for spatio-temporal wind speed forecasting. It enables the prediction of multi-step ahead wind speeds at any location within a given region utilizing data from a single anemometer. The hybrid deep learning model and microscale meteorological model are employed to carry out the temporal and spatial wind speed forecasting, respectively. Specifically, we implement an enhanced complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique for signal decomposition to improve time series prediction accuracy, followed by prediction of subsequences via the hybrid deep learning model. In addition, instantaneous wind speed is divided into mean and fluctuating components, and they are then predicted by using the wind speed ratios, wind direction variations and correlation relationships between the observation location and the prediction site, obtained by the microscale meteorological model. Finally, the accuracy and reliability of the proposed model are validated against observational data. This innovative approach generates accurate wind speed forecasting in both temporal and spatial domains.

Response surface methodology for characterization of wind effect on combined convective loss from a bicylindrical cavity receiver

To assess the combined convective loss of a bicylindrical cavity receiver, an investigation was conducted using response surface methodology under varying wind directions, speeds, and cavity tilt angles. The study revealed that wind direction has a more substantial influence on the combined Nusselt number than cavity tilt angle. For all wind speeds and cavity tilt angles, the head-on wind direction results in a higher combined convective loss compared to back-on and side-on wind directions. Wind speed significantly affects the combined Nusselt number at higher cavity tilt angles for the back-on and side-on winds, with the effect diminishing at lower tilt angles. Increasing the tilt angle of the cavity leads to a greater change in the combined Nusselt number as wind speed increases. A quadratic model was developed to predict the combined Nusselt number, which achieves reasonable agreement between the predicted and experimental values, with a deviation of up to 12.5%.

Heating and cooling of insulated wires of overhead power lines with variations in wind direction

Subject of research: heating and cooling of insulated wires of overhead lines of electric power systems.
 The purpose of the study: experimental determination of heating and cooling curves of insulated wires of overhead lines with changes in wind speed and its direction relative to the axis of the wire.
 Object of research: insulated wires of overhead lines.
 The main results of the study: an overview of an experimental stand developed for the study of non-stationary thermal modes of overhead power lines wires that occur when weather conditions and flowing currents change. The results of a study of heating and cooling of an insulated wire of overhead lines with a cross section of 16 mm2 with a change in the direction of the laminar airflow relative to the axis of the wire, provided that the current flowing through the sample is constant, are presented. The measurements were carried out using devices that record the current strength, temperature and voltage. Heating and cooling curves would be constructed based on the results. The angles of the wind direction relative to the wire were altered, at which the temperature difference is the smallest.

A study of the characteristics of dynamic incoming flow directions of different airflows and their influence on wind comfort

Studies on the dynamic characteristics and comfort evaluation of natural and mechanical wind have been extensively conducted over the past two decades. However, in these contributions, varying wind directions were not considered. In this study, we systematically analysed different airflows from the perspective of the wind azimuth by using statistical analysis, power spectral analysis, wavelet analysis and chaos analysis. Several concepts are newly defined, such as the wind azimuth fluctuation rate (WAFR), the wind azimuth power spectral exponent (βa value) and the dimensionless parameter of the phase space curve (δa value). The results show that the standard deviations (SDs) of the wind azimuth, WAFR, and βa values of typical natural wind are typically larger than those of mechanical wind, while the δa value is lower. The differences in the direction characteristics between natural and mechanical wind highly agree with those in the velocity characteristics. To determine how the direction influences wind comfort, seventeen human subjects were invited to participate in a mixed experiment and exposed to five types of airflow. The results show that true natural wind was preferred, which may be due to the frequently changing direction. The findings also reveal that both the velocity and direction characteristics significantly influence wind comfort evaluations. By understanding the dynamic wind direction characteristics of airflows, this paper may provide new insights into further studies on characteristics, comfort assessments and the application of dynamic wind field in indoor built environments.

Integrated analysis of the transport process and source attribution of an extreme ozone pollution event in Hefei at different vertical heights: A case of study

The Yangtze River Delta (YRD) region frequently experiences ozone pollution events during the summer and autumn seasons. High-concentration events are typically related to synoptic weather patterns, which impact the transport and photochemical production of ozone at multiple scales, ranging from the local to regional scale. To better understand the regional ozone pollution problem, studies on ozone source attribution are needed, especially regarding the contributions of sources at different vertical heights based on tagging the region or time periods. Between September 3 and 8, 2020, an episode of ozone concentration anomaly high was observed in Hefei through ground-based stations and ozone Lidar. The mechanism behind this event was uncovered through synoptic weather pattern analysis and using the Weather Research and Forecasting Chemistry model (WRF-Chem). Because an approaching typhoon caused variable wind direction, the O3-rich air masses (ORMs) arising from the YRD region were transported to Hefei via the nocturnal residual layer and descended to the ground through horizontal advection and vertical mixing processes the next day. Based on geographic source tagging, the anthropogenic NOx emissions (ANEs) from local and regional sources were the main contributors to the heavy ozone pollution over Hefei on September 6. Furthermore, the intra-regional transported ozone from southern Jiangsu (SJS), southern Anhui (SAH), and Zhejiang (ZJ) in the YRD was the main driving factor of the surface and upper atmosphere ozone pollution. Based on time period tagging, The ozone generated due to ANEs from September 3 to 5 significantly contributed to this episode. It is important to pay attention to the impact of ANEs on September 5 on the surface peak ozone concentration the following day (i.e., September 6). Our findings provide significant insights into the regional ozone transport mechanism in the YRD and optimization of measures to prevent and control heavy ozone pollution on spatiotemporal scales.

Wind Fields in Category 1–3 Tropical Cyclones Are Not Fully Represented in Wind Turbine Design Standards

AbstractOffshore wind energy deployment in the U.S. is expected to increase in the years to come, with proposed wind farm sites located in regions with high risk for tropical cyclones. Yet, the wind turbine design criteria outlined by the International Electrotechnical Commission for extreme events may not account for wind field characteristics unique to tropical cyclones. To evaluate if current design standards capture the extreme conditions of these storms, we perform idealized large‐eddy simulations of three tropical cyclones (one Category 1, one Category 2, and one Category 3 storms) using the Weather Research and Forecasting model. Wind conditions near the eyewall of Cat‐1, Cat‐2, and Cat‐3 storms can exceed current design standards for offshore wind turbines. Winds at 90 m can be faster than outlined in the design criteria for Class I and Class T turbines for 50‐year recurrence periods. Wind speed shear across the turbine rotor layer is also larger than assumed in design specifications. Moreover, vertical variations in wind direction across the turbine rotor layer are large for tropical cyclones of all intensity levels, suggesting design standards should include veer, which can amplify loads in wind turbines. The existence of hurricane wind field characteristics that are not represented in design standards does not imply that damage or failure will certainly occur. Engineering safety factors incorporated in the design of the turbine's blades and structural components may prevent damage from occurring.

Morphological characteristics and dynamic changes of typical climbing dunes on the Qinghai-Tibet Plateau

Climbing dunes are a form of topographic barrier dunes that feature in the aeolian landforms on the Qinghai-Tibetan Plateau. Topography fundamentally influences the spatial distribution and morphological characteristics of climbing dunes by modifying the near-surface airflow and creating space for sediment deposition. Previous studies have considered several aspects of the influence of topography on aeolian landforms. However, morphological differences between climbing dunes in different geomorphic contexts and dynamic changes in the area have received little attention. Therefore, topographic measurements, near-surface flow field measurements, dune morphology analysis, and remote sensing image analysis were combined to explore relationships between dune morphology and topography. Topographic obstacles can accelerate the near-surface airflow, particularly with a 25–35° slope gradient, which is beneficial for sand transport and deposition. Suitable accommodation space (concave slope morphology) and abundant sediment comprise the primary factors that control the scale and morphological diversity of climbing dunes, which are also influenced by a variable wind direction. In general, the morphology of the climbing dunes becomes progressively more complicated from the bottom to the middle of the windward slope, and the sand ridge lines of the dunes are perpendicular to the prevailing wind direction. Based on the influence of climate and human activities, the total area of climbing dunes in this region has decreased over the past 30 years. Despite general evidence of enhanced human-induced desertification in semi-arid and sub-humid regions, warming has not led to the rapid expansion of climbing dunes in these arid locations. This research offers new insights into the relationship between the terrain and dune morphology, enriching the theoretical basis for the plateau aeolian geomorphology. Furthermore, it allows us to consider the dynamic changes in climbing dunes area in the context of global warming.