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Journal articles on the topic 'Wind modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 May 2024

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1

Leenman, Timo, and Frank Phillipson. "Optimal Placing of Wind Turbines: Modelling the Uncertainty." Journal of Clean Energy Technologies 3, no. 2 (2015): 91–105. http://dx.doi.org/10.7763/jocet.2015.v3.175.

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2

Done, James M., Ming Ge, Greg J. Holland, Ioana Dima-West, Samuel Phibbs, Geoffrey R. Saville, and Yuqing Wang. "Modelling global tropical cyclone wind footprints." Natural Hazards and Earth System Sciences 20, no. 2 (February 25, 2020): 567–80. http://dx.doi.org/10.5194/nhess-20-567-2020.

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Abstract. A novel approach to modelling the surface wind field of landfalling tropical cyclones (TCs) is presented. The modelling system simulates the evolution of the low-level wind fields of landfalling TCs, accounting for terrain effects. A two-step process models the gradient-level wind field using a parametric wind field model fitted to TC track data and then brings the winds down to the surface using a numerical boundary layer model. The physical wind response to variable surface drag and terrain height produces substantial local modifications to the smooth wind field provided by the parametric wind profile model. For a set of US historical landfalling TCs the accuracy of the simulated footprints compares favourably with contemporary modelling approaches. The model is applicable from single-event simulation to the generation of global catalogues. One application demonstrated here is the creation of a dataset of 714 global historical TC overland wind footprints. A preliminary analysis of this dataset shows regional variability in the inland wind speed decay rates and evidence of a strong influence of regional orography. This dataset can be used to advance our understanding of overland wind risk in regions of complex terrain and support wind risk assessments in regions of sparse historical data.

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3

Booth, Richard A., and Cathie J. Clarke. "Modelling the delivery of dust from discs to ionized winds." Monthly Notices of the Royal Astronomical Society 502, no. 2 (January 14, 2021): 1569–78. http://dx.doi.org/10.1093/mnras/stab090.

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ABSTRACT A necessary first step for dust removal in protoplanetary disc winds is the delivery of dust from the disc to the wind. In the case of ionized winds, the disc and wind are sharply delineated by a narrow ionization front where the gas density and temperature vary by more than an order of magnitude. Using a novel method that is able to model the transport of dust across the ionization front in the presence of disc turbulence, we revisit the problem of dust delivery. Our results show that the delivery of dust to the wind is determined by the vertical gas flow through the disc induced by the mass-loss, rather than turbulent diffusion (unless the turbulence is strong, i.e. α ≳ 0.01). Using these results, we provide a simple relation between the maximum size of particle that can be delivered to the wind and the local mass-loss rate per unit area from the wind. This relation is independent of the physical origin of the wind and predicts typical sizes in the 0.01–$1\, \rm{\mu m}$ range for extreme-ultraviolet- or X-ray-driven winds. These values are a factor of ∼10 smaller than those obtained when considering only whether the wind is able to carry away the grains.

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4

Barthelmie, R. J., and J. P. Palutikof. "Coastal wind speed modelling for wind energy applications." Journal of Wind Engineering and Industrial Aerodynamics 62, no. 2-3 (September 1996): 213–36. http://dx.doi.org/10.1016/s0167-6105(96)00079-7.

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5

Verheij, F. J., J. W. Cleijne, and J. A. Leene. "Gust modelling for wind loading." Journal of Wind Engineering and Industrial Aerodynamics 42, no. 1-3 (October 1992): 947–58. http://dx.doi.org/10.1016/0167-6105(92)90101-f.

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6

George, Shilpa, and Shajilal A.S. "Wind Turbine – Types and Modelling." International Journal of Engineering Trends and Technology 38, no. 8 (August 25, 2016): 417–19. http://dx.doi.org/10.14445/22315381/ijett-v38p276.

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7

Hurley, P. J., P. C. Manins, and J. A. Noonan. "Modelling wind fields in MAQS." Environmental Software 11, no. 1-3 (January 1996): 35–44. http://dx.doi.org/10.1016/s0266-9838(96)00028-7.

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8

Liljegren, S., S. Höfner, B. Freytag, and S. Bladh. "Atmospheres and wind properties of non-spherical AGB stars." Astronomy & Astrophysics 619 (November 2018): A47. http://dx.doi.org/10.1051/0004-6361/201833203.

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Context. The wind-driving mechanism of asymptotic giant branch (AGB) stars is commonly attributed to a two-step process: first, gas in the stellar atmosphere is levitated by shockwaves caused by stellar pulsation, then accelerated outwards by radiative pressure on newly formed dust, inducing a wind. Dynamical modelling of such winds usually assumes a spherically symmetric star. Aims. We explore the potential consequences of complex stellar surface structures, as predicted by three-dimensional (3D) star-in-a-box modelling of M-type AGB stars, on the resulting wind properties with the aim to improve the current wind models. Methods. Two different modelling approaches are used; the CO5BOLD 3D star-in-a-box code to simulate the convective, pulsating interior and lower atmosphere of the star, and the DARWIN one-dimensional (1D) code to describe the dynamical atmosphere where the wind is accelerated. The gas dynamics of the inner atmosphere region at distances of R ∼ 1−2 R⋆, which both modelling approaches simulate, are compared. Dynamical properties and luminosity variations derived from CO5BOLD interior models are used as input for the inner boundary in DARWIN wind models in order to emulate the effects of giant convection cells and pulsation, and explore their influence on the dynamical properties. Results. The CO5BOLD models are inherently anisotropic, with non-uniform shock fronts and varying luminosity amplitudes, in contrast to the spherically symmetrical DARWIN wind models. DARWIN wind models with CO5BOLD-derived inner boundary conditions produced wind velocities and mass-loss rates comparable to the standard DARWIN models, however the winds show large density variations on time-scales of 10–20 yr. Conclusions. The method outlined in this paper derives pulsation properties from the 3D star-in-a-box CO5BOLD models, to be used in the DARWIN models. If the current grid of CO5BOLD models is extended, it will be possible to construct extensive DARWIN grids with inner boundary conditions derived from 3D interior modelling of convection and pulsation, and avoid the free parameters of the current approach.

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9

Roscher, B., A. Werkmeister, G. Jacobs, and R. Schelenz. "Modelling of Wind Turbine Loads nearby a Wind Farm." Journal of Physics: Conference Series 854 (May 2017): 012038. http://dx.doi.org/10.1088/1742-6596/854/1/012038.

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10

Dallas, Scott, Adam Stock, and Edward Hart. "Control-oriented modelling of wind direction variability." Wind Energy Science 9, no. 4 (April 10, 2024): 841–67. http://dx.doi.org/10.5194/wes-9-841-2024.

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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.

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11

Heimann, Dietrich. "Modelling sound propagation from a wind turbine under various atmospheric conditions." Meteorologische Zeitschrift 27, no. 4 (November 16, 2018): 265–75. http://dx.doi.org/10.1127/metz/2018/0910.

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12

Ye, Zhang, and Jia Meng. "Low-level wind shear of wind field modelling and simulation." International Journal of Wireless and Mobile Computing 26, no. 1 (2024): 19–24. http://dx.doi.org/10.1504/ijwmc.2024.136581.

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13

Martini, Fahed, Leidy Tatiana Contreras Montoya, and Adrian Ilinca. "Review of Wind Turbine Icing Modelling Approaches." Energies 14, no. 16 (August 23, 2021): 5207. http://dx.doi.org/10.3390/en14165207.

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When operating in cold climates, wind turbines are vulnerable to ice accretion. The main impact of icing on wind turbines is the power losses due to geometric deformation of the iced airfoils of the blades. Significant energy losses during the wind farm lifetime must be estimated and mitigated. Finding solutions for icing calls on several areas of knowledge. Modelling and simulation as an alternative to experimental tests are primary techniques used to account for ice accretion because of their low cost and effectiveness. Several studies have been conducted to replicate ice growth on wind turbine blades using Computational Fluid Dynamics (CFD) during the last decade. While inflight icing research is well developed and well documented, wind turbine icing is still in development and has its peculiarities. This paper surveys and discusses the models, approaches and methods used in ice accretion modelling in view of their application in wind energy while summarizing the recent research findings in Surface Roughness modelling and Droplets Trajectory modelling. An An additional section discusses research on the modelling of electro-thermal icing protection systems. This paper aims to guide researchers in wind engineering to the appropriate approaches, references and tools needed to conduct reliable icing modelling for wind turbines.

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14

Koenigsberger, Gloria, and Lawrence H. Auer. "Modelling the Wind Eclipses in WR+O Binaries: The Qualitative Picture." Symposium - International Astronomical Union 143 (1991): 175–78. http://dx.doi.org/10.1017/s0074180900045083.

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The phase dependent profile variations due to wind eclipses in WR + O binary systems are shown to be a means of establishing the WR wind structure. The results of two model calculations are presented which indicate that there are important qualitative differences between the profile variatons which will be observed in slowly -and rapidly- accelerating winds.

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15

Feng, Ju, and Wen Shen. "Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction." Energies 8, no. 4 (April 20, 2015): 3075–92. http://dx.doi.org/10.3390/en8043075.

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16

Astolfi, Davide. "Wind Turbine Operation Curves Modelling Techniques." Electronics 10, no. 3 (January 23, 2021): 269. http://dx.doi.org/10.3390/electronics10030269.

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Wind turbines are machines operating in non-stationary conditions and the power of a wind turbine depends non-trivially on environmental conditions and working parameters. For these reasons, wind turbine power monitoring is a complex task which is typically addressed through data-driven methods for constructing a normal behavior model. On these grounds, this study is devoted the analysis of meaningful operation curves, which are rotor speed-power, generator speed-power and blade pitch-power. A key point is that these curves are analyzed in the appropriate operation region of the wind turbines: the rotor and generator curves are considered for moderate wind speed, when the blade pitch is fixed and the rotational speed varies (Region 2); the blade pitch curve is considered for higher wind speed, when the rotational speed is rated (Region 2 12). The selected curves are studied through a multivariate Support Vector Regression with Gaussian Kernel on the Supervisory Control And Data Acquisition (SCADA) data of two wind farms sited in Italy, featuring in total 15 2 MW wind turbines. An innovative aspect of the selected models is that minimum, maximum and standard deviation of the independent variables of interest are fed as input to the models, in addition to the typically employed average values: using the additional covariates proposed in this work, the error metrics decrease of order of one third, with respect to what would be obtained by employing as regressors only the average values of the independent variables. In general it results that, for all the considered curves, the prediction of the power is characterized by error metrics which are competitive with the state of the art in the literature for multivariate wind turbine power curve analysis: in particular, for one test case, a mean absolute percentage error of order of 2.5% is achieved. Furthermore, the approach presented in this study provides a superior capability of interpreting wind turbine performance in terms of the behavior of the main sub-components and eliminates as much as possible the dependence on nacelle anemometer data, whose use is critical because of issues related to the sites complexity.

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17

Reid, Bell, and Copeland. "MODELLING THE SPATIAL VARIATION OF WIND." Weather and Climate 18, no. 1 (1998): 21. http://dx.doi.org/10.2307/44280025.

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18

Coles, Stuart G., and David Walshaw. "Directional Modelling of Extreme Wind Speeds." Applied Statistics 43, no. 1 (1994): 139. http://dx.doi.org/10.2307/2986118.

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19

Pintea, A., D. Popescu, and P. Borne. "Modelling and control of wind turbines." IFAC Proceedings Volumes 43, no. 8 (2010): 251–56. http://dx.doi.org/10.3182/20100712-3-fr-2020.00042.

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20

Di Fazio, A. R., and M. Russo. "Wind farm modelling for reliability assessment." IET Renewable Power Generation 2, no. 4 (December 1, 2008): 239–48. http://dx.doi.org/10.1049/iet-rpg:20080005.

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21

Cornford, Dan, Ian T. Nabney, and Christopher K. I. Williams. "Modelling Frontal Discontinuities in Wind Fields." Journal of Nonparametric Statistics 14, no. 1-2 (January 2002): 43–58. http://dx.doi.org/10.1080/10485250211392.

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22

Frank, H. P., and L. Landberg. "Modelling the Wind Climate of Ireland." Boundary-Layer Meteorology 85, no. 3 (December 1997): 359–77. http://dx.doi.org/10.1023/a:1000552601288.

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23

Ayotte, Keith W. "Computational modelling for wind energy assessment." Journal of Wind Engineering and Industrial Aerodynamics 96, no. 10-11 (October 2008): 1571–90. http://dx.doi.org/10.1016/j.jweia.2008.02.002.

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24

Tagliaferri, F., B. P. Hayes, I. M. Viola, and S. Z. Djokić. "Wind modelling with nested Markov chains." Journal of Wind Engineering and Industrial Aerodynamics 157 (October 2016): 118–24. http://dx.doi.org/10.1016/j.jweia.2016.08.009.

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25

Melbourne, W. "Dispersion modelling in convective wind flows." Atmospheric Environment 28, no. 11 (June 1994): 1879–85. http://dx.doi.org/10.1016/1352-2310(94)90327-1.

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26

Ackerman, M. C. "Yaw modelling of small wind turbines." Journal of Wind Engineering and Industrial Aerodynamics 39, no. 1-3 (January 1992): 1–9. http://dx.doi.org/10.1016/0167-6105(92)90527-h.

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27

Wang, Zhao Yin, Erich J. Plate, Matthias Rau, and Rolf Keiser. "Scale effects in wind tunnel modelling." Journal of Wind Engineering and Industrial Aerodynamics 61, no. 2-3 (July 1996): 113–30. http://dx.doi.org/10.1016/0167-6105(96)00049-9.

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28

Esser, Ruth. "Recent Development in Solar Wind Modelling." International Astronomical Union Colloquium 154 (1996): 57–64. http://dx.doi.org/10.1017/s0252921100029961.

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AbstractAn improved knowledge of the physical conditions in the low corona and solar wind can only be obtained through careful comparisons between theoretical descriptions of the solar wind expansion and plasma parameters derived from observations. In this review we will present a summary of recent approaches in solar wind modeling. The plasma parameters characterizing the solar wind models will be compared to constraints inferred from in-situ and remote observations. We will then discuss the implications of the results obtained from this study for future model studies and observations. Emphasis will be placed on high-speed solar wind streams originating from large coronal holes.

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29

Eslamian, Saeid, and Negin Zamani. "Progresses in wind analysis and modelling." International Journal of Global Energy Issues 32, no. 3 (2009): 175. http://dx.doi.org/10.1504/ijgei.2009.030650.

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30

Le, Bryant, and John Andrews. "Modelling wind turbine degradation and maintenance." Wind Energy 19, no. 4 (May 27, 2015): 571–91. http://dx.doi.org/10.1002/we.1851.

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31

Kestens, Elke, and Jef L. Teugels. "Challenges in modelling stochasticity in wind." Environmetrics 13, no. 8 (2002): 821–30. http://dx.doi.org/10.1002/env.522.

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32

Green, Johnathan, and Subajan Sivandran. "Wind tunnel testing and computational fluid dynamics in FLNG and floating production system design." APPEA Journal 56, no. 2 (2016): 613. http://dx.doi.org/10.1071/aj15119.

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Demonstrating how numerical modelling, such as computational fluid dynamics (CFD), can be used to validate results from detailed physical wind tunnel models of FLNG vessels and floating systems is the objective of this extended abstract. 3D rapid prototyping is used to build detailed physical wind tunnel models. This physical model (normally of an approximate scale of 1:200) is then placed in a wind tunnel facility to measure the time histories of the wind loads for a full range of wind directions and a range of drafts. CFD is then used to validate the wind tunnel modelling results. Numerical modelling can also be used to analyse a number of different issues such as the impact of turbine exhaust dispersion, and turbulence on helicopter operations and resulting helideck availability. This extended abstract discusses the importance of wind tunnel testing and numerical modelling during the design phase. The idea that numerical modelling does not replace pure theoretical or experimental results, but acts to complement them with gaining a greater overall picture, will be highlighted. Findings will be presented to discuss the advantages and disadvantages of both approaches, and highlight results such as wind shear and turbulence impacts being best calculated through wind tunnel testing. The extended abstract demonstrates that, ideally during the design process, wind tunnel testing should be followed by numerical modelling to interpolate results.

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33

Hansen, Anca D., Poul Sørensen, Frede Blaabjerg, and John Becho. "Dynamic Modelling of Wind Farm Grid Interaction." Wind Engineering 26, no. 4 (July 2002): 191–210. http://dx.doi.org/10.1260/030952402321039403.

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This paper describes a dynamic model of a wind farm and its nearest utility grid. It is intended to use this model in studies addressing the dynamic interaction between a wind farm and a power system, both during normal operation of the wind farm and during transient grid fault events. The model comprises the substation where the wind farm is connected, the internal power collection system of the wind farm, the electrical, mechanical and aerodynamic models for the wind turbines, and a wind model. The integrated model is built to enable the assessment of power quality and control strategies. It is implemented in the commercial dedicated power system simulation tool DIgSILENT.

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34

Sohoni, Vaishali, S. C. Gupta, and R. K. Nema. "A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Systems." Journal of Energy 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/8519785.

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Power curve of a wind turbine depicts the relationship between output power and hub height wind speed and is an important characteristic of the turbine. Power curve aids in energy assessment, warranty formulations, and performance monitoring of the turbines. With the growth of wind industry, turbines are being installed in diverse climatic conditions, onshore and offshore, and in complex terrains causing significant departure of these curves from the warranted values. Accurate models of power curves can play an important role in improving the performance of wind energy based systems. This paper presents a detailed review of different approaches for modelling of the wind turbine power curve. The methodology of modelling depends upon the purpose of modelling, availability of data, and the desired accuracy. The objectives of modelling, various issues involved therein, and the standard procedure for power performance measurement with its limitations have therefore been discussed here. Modelling methods described here use data from manufacturers’ specifications and actual data from the wind farms. Classification of modelling methods, various modelling techniques available in the literature, model evaluation criteria, and application of soft computing methods for modelling are then reviewed in detail. The drawbacks of the existing methods and future scope of research are also identified.

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35

Baskaran, A., and T. Stathopoulos. "Prediction of wind effects on buildings using computational methods — review of the state of the art." Canadian Journal of Civil Engineering 21, no. 5 (October 1, 1994): 805–22. http://dx.doi.org/10.1139/l94-087.

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Advancements in computer software and hardware technology provide a new direction for analyzing engineering problems. Recently the field of wind engineering has gained significant momentum in the computer modelling process. This paper reviews the state of the art in computational wind engineering, including the finite element method, finite difference method, and control volume technique. A portion of this paper summarizes the research in this area carried out by the authors. Computations have been made for a variety of building configurations, including normal wind flow conditions for a building with different aspect ratios, and modelling wind environmental conditions around groups of buildings. The computer modelling technique may eventually enhance the design of buildings and structures against wind loading and supplement the current design practice of using building codes and standards or performing experiments in wind tunnels. Key words: buildings, computer modelling, pressure, velocity, wind engineering, wind tunnels.

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36

Evensberget, D., B. D. Carter, S. C. Marsden, L. Brookshaw, and C. P. Folsom. "The winds of young Solar-type stars in the Hyades." Monthly Notices of the Royal Astronomical Society 506, no. 2 (June 16, 2021): 2309–35. http://dx.doi.org/10.1093/mnras/stab1696.

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ABSTRACT Stellar winds govern the spin-down of Solar-type stars as they age, and play an important role in determining planetary habitability, as powerful winds can lead to atmospheric erosion. We calculate 3D stellar wind models for five young Solar-type stars in the Hyades cluster, using TOUPIES survey stellar magnetograms and state-of-the-art Alfvén wave-driven wind modelling. The stars have the same 0.6 Gyr age and similar fundamental parameters, and we account for the uncertainty in and underestimation of absolute field strength inherent in Zeeman–Doppler imaging by adopting both unscaled and scaled (by a factor of five) field strengths. For the unscaled fields, the resulting stellar wind mass-loss is 2–4 times greater and the angular momentum loss 2–10 times greater than for the Sun today, with the scaled results correspondingly greater. We compare our results with a range published of wind models and for the Alfvén wave-driven modelling see evidence of mass-loss saturation at ${\sim 10} \dot{M}_{\odot }$.

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37

Reid. "MODELLING OF CHANNELLED WINDS IN HIGH WIND AREAS OF NEW ZEALAND." Weather and Climate 17, no. 1 (1997): 3. http://dx.doi.org/10.2307/44279905.

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38

Malfait, J., W. Homan, S. Maes, J. Bolte, L. Siess, F. De Ceuster, and L. Decin. "SPH modelling of wind-companion interactions in eccentric AGB binary systems." Astronomy & Astrophysics 652 (August 2021): A51. http://dx.doi.org/10.1051/0004-6361/202141161.

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Context. The late evolutionary stages of low- and intermediate-mass stars are characterised by mass loss through a dust-driven stellar wind. Recent observations reveal complex structures within these winds, which are believed to be formed primarily via an interaction with a companion. How these complexities arise, and which structures are formed in which type of systems, is still poorly understood. Particularly, there is a lack of studies investigating the structure formation in eccentric systems. Aims. We aim to improve our understanding of the wind morphology of eccentric asymptotic giant branch (AGB) binary systems by investigating the mechanism responsible for the different small-scale structures and global morphologies that arise in a polytropic wind with different velocities. Methods. Using the smoothed particle hydrodynamics (SPH) code PHANTOM, we generated nine different high-resolution, 3D simulations of an AGB star with a solar-mass companion with various wind velocity and eccentricity combinations. The models assume a polytropic gas, with no additional cooling. Results. Compared to the zero-eccentricity situation, we find that for low eccentricities, for the case of a high wind velocity, and hence limited interaction between the wind and the companion, the standard two-edged spiral structure that dominates the shape of the wind in the orbital plane is only minimally affected. When the wind speed is lower, strong compression of the wind material by the companion occurs, causing a high-pressure region around the companion which shapes the wind into an irregular spiral. In extreme cases, with low wind velocity and high eccentricity, these instabilities grow to such proportion that they cause high-speed ejections of matter along the orbital plane, shaping the wind into a highly irregular morphology. In more eccentric orbits, the amplitude of the phase-dependent wind-companion interaction increases significantly, introducing additional complexities that make the outbursts even more energetic, leading in some cases to high-speed polar flows of matter. Further, the orbital motion of the stars tends to flatten the global density distribution of the models with no instabilities. We distinguish global flattening from an equatorial density enhancement, the latter being formed by a strong gravitational interaction of the companion with the wind particles. We classify the resulting morphologies according to these new definitions, and find that (i) all low-velocity models have an equatorial density enhancement and (ii), in general, the flattening increases for decreasing wind velocity, until the low wind velocity results in high-energy outflows that clear away the flattening. Conclusions. We conclude that for models with a high wind velocity, the short interaction with the companion results in a regular spiral morphology, which is flattened. In the case of a lower wind velocity, the stronger interaction results in the formation of a high-energy region and bow-shock structure that can shape the wind into an irregular morphology if instabilities arise. High-eccentricity models show a complex, phase-dependent interaction leading to wind structures that are irregular in three dimensions. However, the significant interaction with the companion compresses matter into an equatorial density enhancement, irrespective of eccentricity.

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39

Sokolov, Andrei, and Boris Chubarenko. "Wind Influence on the Formation of Nearshore Currents in the Southern Baltic: Numerical Modelling Results." Archives of Hydro-Engineering and Environmental Mechanics 59, no. 1-2 (October 1, 2012): 37–48. http://dx.doi.org/10.2478/v10203-012-0003-3.

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Abstract A two-dimensional numerical model was used for a simulation of vertical average longshore currents generated by both wind friction and wind-wave action in the nearshore zone. The modelling domain includes the southern part of the Baltic Proper (all boundaries were closed). Wind, uniform in space and varying in time, was the only forcing in the model. The correlation coefficient higher than 0.8 was obtained by model calibration versus the field measurements of currents conducted at the Lubiatowo field station (southern Baltic) during about 1.5 months in 2006. Comparative simulations of total currents including both wind-induced drift and wave components, and of total currents including only a wind-induced drift component, showed that the input of the drift component into currents in the nearshore zone is greater than commonly believed.Wind-induced drift strongly dominates outside the zone of wave transformation, and its input into the total resulting currents remains noticeable even in a zone between the shoreline and the depth of the first wave breaking. Thus, wind-induced drift constitutes up to 50% of the resulting longshore currents for longshore winds and no less than 20% of the longshore component of currents for winds at 45 degrees to the longshore direction.

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40

Gabr, Walaa I., Eman T. Abdel Razik, and H. T. Dorrah. "Wind-Based Multi-Regional Modelling and Control of Wind Turbines Generation." Benha Journal of Applied Sciences 7, no. 4 (April 1, 2022): 197–205. http://dx.doi.org/10.21608/bjas.2022.257796.

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41

Andersen, S. J., N. N. Sørensen, and M. Kelly. "LES Modelling of Highly Transient Wind Speed Ramps in Wind Farms." Journal of Physics: Conference Series 1934, no. 1 (May 1, 2021): 012015. http://dx.doi.org/10.1088/1742-6596/1934/1/012015.

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42

Kalvig, Siri, Eirik Manger, and Bjørn Hjertager. "Comparing different CFD wind turbine modelling approaches with wind tunnel measurements." Journal of Physics: Conference Series 555 (December 16, 2014): 012056. http://dx.doi.org/10.1088/1742-6596/555/1/012056.

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43

Loukatou, Angeliki, Sydney Howell, Paul Johnson, and Peter Duck. "Stochastic wind speed modelling for estimation of expected wind power output." Applied Energy 228 (October 2018): 1328–40. http://dx.doi.org/10.1016/j.apenergy.2018.06.117.

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Prasad, R. D., R. C. Bansal, and M. Sauturaga. "Wind modelling based on wind input data conditions using Weibull distribution." International Journal of Global Energy Issues 32, no. 3 (2009): 227. http://dx.doi.org/10.1504/ijgei.2009.030653.

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Frandsen, Sten, Rebecca Barthelmie, Sara Pryor, Ole Rathmann, Søren Larsen, Jørgen Højstrup, and Morten Thøgersen. "Analytical modelling of wind speed deficit in large offshore wind farms." Wind Energy 9, no. 1-2 (January 2006): 39–53. http://dx.doi.org/10.1002/we.189.

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Crespo, A., J. Hernández, and S. Frandsen. "Survey of modelling methods for wind turbine wakes and wind farms." Wind Energy 2, no. 1 (January 1999): 1–24. http://dx.doi.org/10.1002/(sici)1099-1824(199901/03)2:1<1::aid-we16>3.0.co;2-7.

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Hirata, Yoshito, Danilo P. Mandic, Hideyuki Suzuki, and Kazuyuki Aihara. "Wind direction modelling using multiple observation points." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1865 (August 13, 2007): 591–607. http://dx.doi.org/10.1098/rsta.2007.2112.

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The prediction of wind direction is a prerequisite for the intelligent and efficient operation of wind turbines. This is a complex task, due to the intermittent behaviour of wind, its non-Gaussian and nonlinear nature, and the coupling between the wind speed and direction. To provide improved wind direction forecasting, we propose a nonlinear model with augmented information from an additional measurement point. This is further enhanced by making use of both the speed and direction components of the wind field vector. The analysis and a comprehensive set of simulations demonstrate that the proposed approach achieves improved prediction performance over the standard and persistent model. The potential of the proposed approach is justified by the fact that even relatively small improvements in the forecasts result in large gains in the produced output power.

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Radics, K., J. Bartholy, and R. Pongrácz. "Modelling studies of wind field on urban environment." Atmospheric Chemistry and Physics Discussions 2, no. 6 (November 11, 2002): 1979–2001. http://dx.doi.org/10.5194/acpd-2-1979-2002.

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Abstract. Increasing load of air pollution in urban environment emphasises the need for detailed evaluation of wind characteristics that significantly affect the air quality of urban areas, especially, in large agglomerations. This paper includes analysis of urban wind climatology and estimation of wind profiles based on measurements of the new urban climate station located at the Eötvös University, observations of the meteorological station network of the Budapest agglomeration area, and multi-level wind measurements near Hegyhátsál. Furthermore, wind field modelling (using the WAsP linear spectral wind flow model) is presented over selected representative complex areas that demonstrates strong dependence between wind, height, topography, and roughness.

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Shah, Akhter Hussain. "Modelling and Control of Wind PV Battery Fuelcell based Hybrid Power System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 8–13. http://dx.doi.org/10.31142/ijtsrd12950.

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Pobočíková, Ivana, Mária Michalková, Zuzana Sedliačková, and Daniela Jurášová. "Modelling the Wind Speed Using Exponentiated Weibull Distribution: Case Study of Poprad-Tatry, Slovakia." Applied Sciences 13, no. 6 (March 22, 2023): 4031. http://dx.doi.org/10.3390/app13064031.

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In the paper, we statistically analysed data on the average hourly wind speed obtained from the meteorological station Poprad (located at the Poprad-Tatry airport, the Prešov region, Northern Slovakia) for the period 2005–2021. High altitude and rough mountainous terrain influence the weather conditions considerably and are a source of occasional weather risks. Finding an appropriate wind speed distribution for modelling the wind speed data is therefore important to determine the wind profile at this particular location. In addition to the commonly used two- and three-parameter Weibull distribution, a more flexible exponentiated Weibull (EW) distribution was applied to model the wind speed. Based on the results of the goodness-of-fit criteria (the Kolmogorov–Smirnov test, the Anderson–Darling test, Akaike’s and Bayesian information criteria, the root mean square error, and the coefficient of determination), the EW distribution obtained a significantly better fit to seasonal and monthly wind speed data, especially around the peaks of the data. The EW distribution also proved to be a good model for data with high positive skewness. Therefore, we can recommend the EW distribution as a flexible distribution for modelling a dataset with extremely strong winds or outliers in the direction of the right tail. Alongside the wind speed analysis, we also provided the wind direction analysis, finding out that the most prevailing direction was west (W)—with an occurrence rate of 34.99%, and a mean wind speed of 3.91 m/s, whereas the northern (N) direction featured the lowest occurrence rate of only 4.45% and the mean wind speed of 1.99 m/s.

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