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Academic literature on the topic 'Wind'

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Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Wind"

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Mandelbaum, R. "Reap the wild wind [offshore wind farm]." IEEE Spectrum 39, no. 10 (October 2002): 34–39. http://dx.doi.org/10.1109/mspec.2002.1038567.

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Matsyura, Alex, Kazimierz Jankowski, and Marina Matsyura. "BIRDS’ FLIGHT ENERGY PREDICTIONS AND APPLICATION TO RADAR-TRACKING STUDY." Biological Bulletin of Bogdan Chmelnitskiy Melitopol State Pedagogical University 3, no. 03 (October 28, 2013): 135. http://dx.doi.org/10.15421/20133_45.

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<p>In offered research, we propose to observe diurnal soaring birds to check, whether there the positions of birds in formations are such, that the wing tip interval and depth meet the predictions of aerodynamic theory for achievement of maximal conservation of energy or predictions of the hypothesis of communication. We also can estimate, whether adverse conditions of a wind influence the ability of birds to support formation. We can assume that windy conditions during flight might make precision flight more difficult by inducing both unpredictable bird and vortex positions. To this, we need to found change in wing-tip spacing variation with increasing wind speed, suggesting or rejecting that in high winds bird skeins maintained similar variation to that on calm days. The interrelation between variation of mean depth and wind speed should prove this hypothesis. Little is known about the importance of depth, but in high winds the vortex is likely to break up more rapidly and its location become unpredictable the further back a bird flies; therefore, a shift towards skeins with more regular depths at high wind speeds may compensate for the unpredictability of the vortex locations. Any significant relationship between the standard deviation of wing-tip spacing and wind speed suggests that wind has a major effect on optimal positioning.</p> <p>Results of proposed study will be used also as the auxiliary tool in radar research of bird migration, namely in research of flight features of soaring birds. It is extremely important to determine all pertinent characteristics of flock for model species, namely flocking birds.</p> <p><em>Kew words: birds, flock, radar, flight</em></p><p> </p>
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Bradley, Stuart, and Alexander Strehz. "Corrections to sodar Doppler winds due to wind drift." Meteorologische Zeitschrift 24, no. 6 (November 5, 2015): 605–14. http://dx.doi.org/10.1127/metz/2014/0627.

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Edwards, Susan. "The wild west wind." Women & Performance: a journal of feminist theory 10, no. 1-2 (January 1999): 277–90. http://dx.doi.org/10.1080/07407709908571306.

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Andreas, Edgar L., and Larry Mahrt. "On the Prospects for Observing Spray-Mediated Air–Sea Transfer in Wind–Water Tunnels." Journal of the Atmospheric Sciences 73, no. 1 (December 21, 2015): 185–98. http://dx.doi.org/10.1175/jas-d-15-0083.1.

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Abstract Nature is wild, unconstrained, and often dangerous. In particular, studying air–sea interaction in winds typical of tropical cyclones can place researchers, their instruments, and even their research platforms in jeopardy. As an alternative, laboratory wind–water tunnels can probe 10-m equivalent winds of hurricane strength under conditions that are well constrained and place no personnel or equipment at risk. Wind–water tunnels, however, cannot simulate all aspects of air–sea interaction in high winds. The authors use here the comprehensive data from the Air–Sea Interaction Salt Water Tank (ASIST) wind–water tunnel at the University of Miami that Jeong, Haus, and Donelan published in this journal to demonstrate how spray-mediated processes are different over the open ocean and in wind tunnels. A key result is that, at all high-wind speeds, the ASIST tunnel was able to quantify the so-called interfacial air–sea enthalpy flux—the flux controlled by molecular processes right at the air–water interface. This flux cannot be measured in high winds over the open ocean because the ubiquitous spray-mediated enthalpy transfer confounds the measurements. The resulting parameterization for this interfacial flux has implications for modeling air–sea heat fluxes from moderate winds to winds of hurricane strength.
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Khaghaninia, S., S. Mohammadi, A. Srafrazi, K. Nejad, and R. Zahiri. "Geometric Morphometric Study on Geographic Dimorphism of Coding Moth Cydia Pomonella (Lepidoptera, Tortricidae) from North West of Iran." Vestnik Zoologii 45, no. 5 (January 1, 2011): e-20-e-28. http://dx.doi.org/10.2478/v10058-011-0028-z.

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Geometric Morphometric Study on Geographic Dimorphism of Coding MothCydia Pomonella(Lepidoptera, Tortricidae) from North West of IranDuring years 2003-2004, nine geographical populations of codling moth Cydia pomonella (Linnaeus) from 4 north western provinces of Iran were collected. By preparing 575 images from fore wings and 564 from hind wings, a total of 15 and 11 landmarks were determined for fore and hind wings, respectively. With transforming of landmark's geometrical data into partial warp scores, 26 and 18 scores were obtained for fore and hind wings, respectively. Canonical correlation analysis (CCA) revealed significant correlation between environmental parameters and wing shape variables. Among environmental parameters, wind speed showed the highest correlation with wing shape variables whereas, the correlation between latitude, relative humidity as well as amount of precipitation and wing shape variables was low. Considering the effect of various environmental parameters on wing shape, wind speed was determined as important parameter affecting geographic dimorphism. Among the populations collected from different regions, two geographic population pairs; Meshkinshahr-Mahneshan and Zandjan-Khoramdareh were selected as representative of low and high windy regions, respectively. Relative warp analysis (RWA) of fore and hind wings shape variables in the areas with high and low wind showed shorter and wider fore wings as well as slender and narrower hind wings in populations from high windy regions compared with populations from low wind regions. Centroid size of fore and hind wings in high windy area populations were smaller compared with those from low windy ones as revealed by t-test. The results showed aerodynamic shape and small size of wings are as adapted traits for powerful flight and its control in high windy regions.
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Rehman, Shafiqur, Kashif Irshad, Nasiru I. Ibrahim, Ali AlShaikhi, and Mohamed A. Mohandes. "Offshore Wind Power Resource Assessment in the Gulf of North Suez." Sustainability 15, no. 21 (October 25, 2023): 15257. http://dx.doi.org/10.3390/su152115257.

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Growing population, industrialization, and power requirements are adversely affecting the environment through increased greenhouse gases resulting from fossil fuel burning. Global greenhouse gas mitigation targets have led nations to promote clean and self-renewable sources of energy to address this environmental issue. Offshore wind power resources are relatively more attractive due to high winds, less turbulence, minimal visualization effects, and no interaction of infrastructure. The present study aims at conducting an offshore wind power resource assessment (OWPRA) at some locations in the Gulf of North Suez. For this purpose, the long-term hourly mean wind speed (WS) and wind direction above mean sea level (AMSL), as well as temperature and pressure data near the surface, are used. The data is obtained from ERA5 (fifth generation global climate reanalysis) at six (L1–L6) chosen offshore locations. The data covers a period of 43 years, between 1979 and 2021. The WS and direction are provided at 100 m AMSL, while temperature and pressure are available near water-surface level. At the L1 to L6 locations, the log-term mean WS and wind power density (WPD) values are found to be 7.55 m/s and 370 W/m2, 6.37 m/s and 225 W/m2, 6.91 m/s and 281 W/m2, 5.48 m/s and 142 W/m2, 4.30 m/s and 77 W/m2, and 5.03 and 115 W/m2 and at 100 m AMSL, respectively. The higher magnitudes of monthly and annual windy site identifier indices (MWSI and AWSI) of 18.68 and 57.41 and 12.70 and 42.94 at the L1 and L3 sites, and generally lower values of wind variability indices, are indicative of a favorable winds source, which is also supported by higher magnitudes of mean WS, WPD, annual energy yields, plant capacity factors, and wind duration at these sites. The cost of energy for the worst and the best cases are estimated as 10.120 USD/kWh and 1.274 USD/kWh at the L5 and L1 sites, corresponding to wind turbines WT1 and WT4. Based on this analysis, sites L1, L3, and L2 are recommended for wind farm development in order of preference. The wind variability and windy site identifier indices introduced will help decision-makers in targeting potential windy sites with more confidence.
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Wiegel, R. L. "WIND WAVES AND SWELL." Coastal Engineering Proceedings 1, no. 7 (January 29, 2011): 1. http://dx.doi.org/10.9753/icce.v7.1.

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Winds blowing over the water surface generate waves. In general the higher the wind velocity, the larger the fetch over which it blows, and the longer it blows the higher and longer will be the average waves . Waves still under the action of the winds that created them are called wind waves, or a sea. They are forced waves rather than free waves. They are variable in their direction of advance (Arthur, 1949). They are irregular in the direction of propagation. The flow is rotational due to the shear stress of the wind on the water surface and it is quite turbulent as observations of dye in the water indicates. After the waves leave the generating area their characteristics become somewhat different, principally they are smoother, losing the rough appearance due to the disappearance of the multitude of smaller waves on top of the bigger ones and the whitecaps and spray. When running free of the storm the waves are known as swell. In Fig. 1 are shown some photographs taken in the laboratory of waves still rising under the action of wind and this same wave system after it has left the windy section of the wind-wave tunnel. It can be seen thati-the freely running swell has a smoother appearance than the waves in the windy section. The motion of the swell is nearly irrotational and nonturbulent, unless the swell runs into other regions where the water is in turbulent motion. Turbulence is a property of the fluid rather than of the wave motion. After the waves have travelled a distance from the generating area they have lost some energy due to air resistance, internal friction, and by large scale turbulent scattering if they run into other storm areas, and the rest of the energy has become spread over a larger area due to the dispersive and angular spreading characteristics of water gravity waves. All of these mechanisms lead to a decrease in energy density. Thus, the waves become lower in height. In addition, due to their dispersive characteristic the component wave periods tend to segregate in such a way that the longest waves lead the main body of waves and the shortest waves form the tail of the main body of waves. Finally, the swell may travel through areas where winds are present, adding new wind waves to old swell, and perhaps directly increasing or decreasing the size of the old swell.
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Xue, Yiwei, and Miao Wei. "Travel with the Wild Wind." Chinese Literature Today 9, no. 2 (July 2, 2020): 46–47. http://dx.doi.org/10.1080/21514399.2020.1851964.

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Dillon, Dennis. "Making the Wild Wind Visible." Journal of Library Administration 28, no. 1 (June 1999): 47–61. http://dx.doi.org/10.1300/j111v28n01_05.

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Dissertations / Theses on the topic "Wind"

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Ndzukuma, Sibusiso. "Statistical tools for wind energy generation." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1020627.

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In this study we conduct wind resource assessment to evaluate the annual energy production of a wind turbine. To estimate energy production of a wind turbine over a period of time, the power characteristics of the wind turbine are integrated with the probabilities of the wind speed expected at a chosen site. The first data set was obtained from a wind farm in Denmark. We propose several probability density functions to model the distribution of the wind speed. We use techniques from nonlinear regression analysis to model the power curve of a wind turbine. The best fit distribution model is assessed by performing numeric goodness–of–fit measures and graphical analyses. Johnson’s bounded (SB) distribution provides the best fit model with the smallest Kolmogorov–Smirnov (K-S) test statistic . 15. The four parameter logistic nonlinear regression (4PL) model is determined to provide the best fit to the power curve data, according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The estimated annual energy yield is compared to the actual production of the wind turbine. Our models underestimate the actual energy production by a 1 difference. In Chapter Six we conduct data processing, analyses and comparison of wind speed distributions using a data set obtained from a measuring wind mast mounted in Humansdorp, Eastern Cape. The expected annual energy production is estimated by using the certified power curve as provided by the manufacturer of the wind turbine under study. The commonly used Weibull distribution is determined to provide the best fit distribution model to our selected models. The annual energy yield is estimated at 7.33 GWh, with a capacity factor of 41.8 percent.
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Bezerra, Rufino Ferreira Paiva Eduardo. "Wind Velocity Estimation for Wind Farms." Electronic Thesis or Diss., Université Paris sciences et lettres, 2023. http://www.theses.fr/2023UPSLM046.

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Cette thèse propose des algorithmes pour estimer la vitesse et la direction du vent pour des éoliennes et des parcs éoliens.Tout d'abord, nous proposons des méthodes basées sur les données pour estimer la vitesse effective du rotor (REWS) sans nécessiter la connaissance de certains paramètres physiques de l'éolienne, qui pourraient être inconnus de l'opérateur. Nous fournissons deux méthodes basées sur les données, l'une basée sur la régression par processus gaussien et l'autre combinant la régression par processus gaussien avec un observateur grand gain.Ensuite, en nous basant sur cette estimation locale de la REWS, au niveau d'une éolinenne, nous abordons la question de l'estimation du vent en écoulement libre au niveau du parc éolien.Nous commençons par nous concentrer sur l'estimation de la vitesse du vent, pour une direction du vent connue. Pour un parc éolien de géométrie simple, nous démontrons qu'une mesure locale de la vitesse perturbée par la présence des éoliennes peut être utilisée pour estimer la vitesse du vent en écoulement libre. Nous fondons notre méthodologie d'estimation sur une modélisation simplifiée de l'effet de sillage qui consiste en des équations aux dérivées partielles hyperboliques du premier ordre en cascade, et dont la vitesse de transport est la vitesse du vent en écoulement libre. Nous proposons d'utiliser une solution analytique de ces équations, impliquant des retards de transport, pour effectuer une estimation de la mesure locale et mettre à jour l'estimation de la vitesse du vent en écoulement libre. Nous démontrons formellement la convergence de cette estimation et illustrons numériquement l'efficacité de cette méthode.Enfin, nous passons à une configuration plus générale où à la fois la vitesse et la direction du vent en écoulement libre sont inconnues. Nous proposons d'utiliser une modélisation bidimensionelle du sillage et de nous appuyer sur une méthode basée sur l'optimisation. Le problème d'identification que nous formulons se révèle être particulièrement difficile en raison de l'apparition de retards de transport, mais nous montrons comment contourner cette difficulté en considérant une valeur moyenne de l'historique de la vitesse du vent en écoulement libre. Des résultats de simulation obtenus avec le simulateur FAST.Farm illustrent l'intérêt de la méthode proposée
This thesis designs algorithms to estimate the wind speed and direction for wind turbines and wind farms.First, we propose data-based methods to estimate the Rotor Effective Wind Speed (REWS) for a single turbine without prior knowledge of certain physical parameters of the turbine that might be unknown to an operator.We provide two data-based methods, based respectively on Gaussian Process Regression (GPR) and on an combination of GPR with high-gain observers.Second, grounding on this REWS estimation at the local level of one turbine, we address the question of estimating the free-flow wind at the level of a wind farm.We start by focusing on wind speed estimation, for a given known wind direction. For a wind farm with a simple geometry, we prove that a local speed measurement disturbed by the presence of the turbines can be used to estimate the free-flow wind speed. We ground our estimation methodology on a simplified wake model, which consists of first-order hyperbolic partial differential equations, the transport speed of which is the free-flow wind speed. We propose to use an analytical solution of these equations, involving transport delays, to perform an estimate of the local measurement and to update the free-flow wind speed estimate. We formally prove the convergence of this estimate and numerically illustrate the efficiency of this method.Finally, we move to a more general setup where both the free-flow wind speed and direction are unknown. We propose to use a two-dimensional wake model and to rely on an optimization-based method. This identification problem reveals to be particularly challenging due to the appearance of transport delays, but we illustrate how to circumvent this issue by considering an average value of the free flow wind speed history. Simulation results obtained with the simulator FAST.Farm illustrate the interest of the proposed method
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Haag, Christian. "Temporal and spatial wind field distribution in Delaware Bay." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 9.11 Mb., 62 p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:1430767.

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Thesis (M.E.E.)--University of Delaware, 2006.
Principal faculty advisors: Kenneth E. Barner, Dept. of Electrical and Computer Engineering; and Mohsen Badiey, Dept. of Marine and Earth Studies. Includes bibliographical references.
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Duhaut, Thomas H. A. "Wind-driven circulation : impact of a surface velocity dependent wind stress." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=101117.

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The use of an ocean surface velocity dependent wind stress is examined in the context of a 3-layer double-gyre quasigeostrophic wind-driven ocean circulation model. The new wind stress formulation results in a large reduction of the power input by the wind into the oceanic circulation. This wind stress is proportional to a quadratic function of Ua--u o, where Ua is the wind at 10m above the ocean surface and uo is the ocean surface current. Because the winds are typically faster than the ocean currents, the impact of the ocean surface velocity on the wind stress itself is relatively small. However, the power input is found to be greatly reduced with the new formulation. This is shown by simple scaling argument and numerical simulations in a square basin. Our results suggest that the wind power input may be as much as 35% smaller than is typically assumed.
The ocean current signature is clearly visible in the scatterometer-derived wind stress fields. We argue that because the actual ocean velocity differs from the modeled ocean velocities, care must be taken in directly applying scatterometer-derived wind stress products to the ocean circulation models. This is not to say that the scatterometer-derived wind stress is not useful. Clearly the great spatial and temporal coverage make these data sets invaluable. Our point is that it is better to separate the atmospheric and oceanic contribution to the stresses.
Finally, the new wind stress decreases the sensitivity of the solution to the (poorly known) bottom friction coefficient. The dependence of the circulation strength on different values of bottom friction is examined under the standard and the new wind stress forcing for two topographic configurations. A flat bottom and a meridional ridge case are studied. In the flat bottom case, the new wind stress leads to a significant reduction of the sensitivity to the bottom friction parameter, implying that inertial runaway occurs for smaller values of bottom friction coefficient. The ridge case also gives similar results. In the case of the ridge and the new wind stress formulation, no real inertial runaway regime has been found over the range of parameters explored.
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SILVA, ILITCH VITALI GOMES DA. "THE WIND FORECAST FOR WIND POWER GENERATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16824@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
A energia eólica é uma das alternativas mais promissoras para geração de energia elétrica, pois assegura a diversidade e segurança no fornecimento de energia e atende à necessidade premente de reduzir os níveis de emissão de gases poluentes. Na operação de sistemas elétricos com forte presença de geração eólica é fundamental prever com pelo menos um dia de antecedência os valores futuros (pelo menos horários) da veloci-dade do vento, pois assim pode-se avaliar a disponibilidade de energia para o próximo dia, uma informação útil no despacho das unidades geradoras e no controle do sistema elétrico. A proposta dessa dissertação objetiva especificamente desenvolver modelos de previsão de curto prazo da velocidade do vento, baseado em técnicas de inteligência artificial, modelo da rede neural artificial e neuro-fuzzy adaptativa (ANFIS) e um mode-lo Estatístico composto por um modelo de regressão harmônica e Box-Jenkins. Para aplicação da metodologia considerou-se o município de São João do Cariri (Estado de Paraíba), onde está localizada uma das estações de referência do projeto SONDA (Sis-tema Nacional de Dados Ambientais para o setor de energia). O desempenho dos mode-los rede neural, neuro-fuzzy (ANFIS) e modelo Estatístico são comparados nas previ-sões de 6 horas, 12 horas, 18 h e 24horas a frente. Os resultados obtidos mostram o me-lhor desempenho da modelagem ANFIS e encorajam novos estudos no tema.
Wind power is one of the most promising options for power generation. It ensures the diversity and security of energy supply and meets the pressing need to reduce the levels of emission of polluting gases. In the operation of electrical systems with a strong presence of wind generation, it is essential to provide at least one day in advance the future values (at least hourly) of wind speed, so that we can assess the availability of energy for the next day, a useful information in the order of the generating units and electrical control system. The purpose of this dissertation aims to develop models spe-cifically to develop models to forecast short-term wind speed, based on artificial intelligence techniques, artificial neural network model and adaptive neuro-fuzzy Systems (ANFIS) and a statistical model composed of a harmonic regression model and Box-Jenkins. For application of the methodology, the city of São João do Cariri (State of Paraíba), where a reference station of SONDA project (National Environmental Data for the energy sector) is located, was considered.To apply the methodology was consi-dered the city of the ray tracing model (State of Paraíba), which is located a station ref-erence design (National Environmental Data for the energy sector). The performance of artificial neural network model and adaptive neuro-fuzzy Systems (ANFIS) and a statis-tical model are compared mixed forecasts of 6 hours, 12 hours, 18hours and 24 hours ahead. The results show the best performance of the ANFIS model and encourage fur-ther studies on the subject.
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Hickle, Curtis. "Wind Tunnel renovation, flow verification and flapping wing analysis." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Jun%5FHickle.pdf.

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Thesis (M.S. in Mechanical Engineering)--Naval Postgraduate School, June 2006.
Thesis Advisor(s):Dr. Kevin Jones and Dr. Garth Hobson. "June 2006." Includes bibliographical references (p.79-81). Also available in print.
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Paul, Matthew G. "Wing Deflection Analysis of 3D Printed Wind Tunnel Models." DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1751.

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This work investigates the feasibility of producing small scale, low aerodynamic loading wind tunnel models, using FDM 3D printing methods, that are both structurally and aerodynamically representative in the wind tunnel. To verify the applicability of this approach, a 2.07% scale model of the NASA CRM was produced, whose wings were manufacturing using a Finite Deposition Modeling 3D printer. Experimental data was compared to numerical simulations to determine percent difference in wake distribution and wingtip deflection for multiple configurations. Numerical simulation data taken in the form of CFD and FEA was used to validate data taken in the wind tunnel experiments. The experiment utilized a wake rake to measure 3 different spanwise locations of the wing for aerodynamic data, and a videogrammetry method was used to measure the deflection of the wingtips for structural data. Both numerical simulations and experiments were evaluated at Reynolds numbers of 258,000 and 362,000 at 0 degrees angle of attack, and 258,000 at 5 degrees angle of attack. Results indicate that the wing wake minimum in the wind tunnel test had shifted approximately 8.8mm at the wingtip for the Nylon 910 wing at 258,000 Reynolds number for 0 degrees angle of attack when compared to CFD. Videogrammetry results indicate that the wing deflected 5.9mm, and has an 18.6% difference from observed deflection in FEA. This reveals the potential for small scale wind tunnel models to be more representative of true flight behavior for low loading scenarios.
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Fridén, Tobias. "Robust Autonomous Landing of Fixed-Wing UAVs in Wind." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165136.

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Fixed-wing UAVs are today used in many different areas, from agriculture to search and rescue operations. Through various research efforts, they are becoming more and more autonomous. However, the procedure of landing a fixed-wing UAV remains a challenging task, which requires manual input from an experienced pilot. This work proposes a novel method which autonomously performs such landings. The main focus is on small and light-weight UAVs, for which the wind acts as a major disturbance and has to be taken into account. Robustness to other disturbances, such as variations in environmental factors or measurement errors, has also been prioritized during the development of this method.The main contribution of this work consists of a framework in which der\-iva\-tive-free optimization is used to calculate a set of waypoints, which are feasible to use in different wind speeds and directions, for a selected UAV model. These waypoints are then combined online using motion planning techniques, to create a trajectory which safely brings the UAV to a position where the landing descent can be initiated. To ensure a safe descent in a predefined area, another nonlinear optimization problem is formulated and solved. Finally, the proposed method is implemented on a real UAV platform. A number of simulations in different wind conditions are performed, and data from a real flight experiment is presented. The results indicate that the method successfully calculates feasible landing sequences in different scenarios, and that it is applicable in a real-world landing.
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Li, Simeng. "WIND ARRAY PERFORMANCE EVALUATION MODEL FOR LARGE WIND FARMS AND WIND FARM LAYOUT OPTIMIZATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1405080318.

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Shelley, Dena L. "A wind energy landscape : the Searsburg Wind Park." Virtual Press, 2008. http://liblink.bsu.edu/uhtbin/catkey/1390311.

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Wind Energy facilities are becoming a more common occurrence among the U.S. landscape. The shift to renewable from non-renewable energy sources is an important agenda item for energy policy in the 21st century. Unlike other forms of energy, the unique visual aspects of wind energy provide opportunities to engage with and actually view the process of energy production. The sculptural element of turbines and their placement in highly visible areas, such as mountain ridges, provides opportunities of environmental interpretation and public interaction. Although existing security and safety precautions in the U.S. do not allow public use of these facilities, the integration of turbines into public places is becoming more common in other parts of the world. This creative project focuses on developing dynamic and unique cultural places that also serve as education spaces to celebrate wind and wind energy. Environmental art installations among the wind turbines serve as human-scaled interpretational guides to create meaningful, learning experiences between the user, the wind and the landscape.This project highlights the existing eleven-turbine (6MW) facility in the town of Searsburg in southern Vermont. This project includes inventory, analysis and site design of an existing wind facility. The methodology includes using GIS data and existing sight line data, as well as significant and environmental cultural points. Finally, general guidelines are included as a design foundation for other wind energy facilities.
Department of Landscape Architecture
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Books on the topic "Wind"

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Lowell, Elizabeth. Sweet wind, wild wind. Sutton: Severn House, 2010.

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Maxwell, Ann. Sweet wind, wild wind. Waterville, Me: Wheeler Pub., 2004.

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Corp, Windsor Publishing, and Copyright Paperback Collection (Library of Congress), eds. Wild wind! New York, NY: Windsor Pub. Corp., 1993.

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Thompson, Victoria. Wild Texas wind. New York: Zebra Books, 1992.

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Thompson, Victoria. Wild Texas wind. New York: Zebra Books, 1992.

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ill, Kiesler Kate, ed. Wind-wild dog. New York: Henry Holt and Company, 2006.

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Buck, Gayle. Wild tiger wind. Uhrichsville, Ohio: Heartsong Presents, 1999.

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Verrette, Joyce. Sweet wild wind. London: Macdonald, 1986.

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Crowe, Evelyn A. A wild wind. Richmond, Surrey: Worldwide, 1990.

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Verrette, Joyce. Sweet wild wind. London: Futura, 1986.

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Book chapters on the topic "Wind"

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Lenschow, Donald H. "Wind and Wind Fluctuations." In Advances in Berthing and Mooring of Ships and Offshore Structures, 173–86. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-1407-0_7.

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Campbell, Gaylon S., and John M. Norman. "Wind." In An Introduction to Environmental Biophysics, 63–75. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1626-1_5.

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Zlomusica, Elvir. "Wind." In Handbook of Sustainable Engineering, 1109–42. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8939-8_119.

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Heckel, Pamela E. "Wind." In SpringerBriefs in Environmental Science, 61–66. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9701-6_7.

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Krupar III, Richard J. "Wind." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 1–4. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-51727-8_133-1.

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Krupar III, Richard J. "Wind." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 1179–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-52090-2_133.

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Himoto, Keisuke. "Wind." In Large Outdoor Fire Dynamics, 11–27. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003096689-2.

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Wilcox, Alison, and Adam Bushnell. "Wind." In Descriptosaurus Story Writing, 47–49. Subjects: LCSH: Creative writing (Elementary education) | Description (Rhetoric)–Study and teaching (Elementary) | Vocabulary–Study and teaching (Elementary): Routledge, 2020. http://dx.doi.org/10.4324/9781003095675-14.

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Weller, Bernhard, Marc-Steffen Fahrion, Sebastian Horn, Thomas Naumann, and Johannes Nikolowski. "Wind." In Baukonstruktion im Klimawandel, 265–82. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13011-4_8.

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Gerring, Dorothy. "Wind." In Renewable Energy Systems for Building Designers, 177–92. New York: Routledge, 2022. http://dx.doi.org/10.1201/9781003297819-17.

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Conference papers on the topic "Wind"

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Colidiuc, Alexandra, Stelian Galetuse, Bogdan Suatean, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Wind Generator with Oscillating Wing." In ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics 2010. AIP, 2010. http://dx.doi.org/10.1063/1.3498268.

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Johnson, Steven C. "Space Shuttle Wind Profiler." In Coherent Laser Radar. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/clr.1991.tud3.

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Abstract:
Knowledge of winds is required to reduce aerodynamic loads on the Space Shuttle during launch. Knowledge of winds is also required to safely land the unpowered orbiter. Balloons are currently used in both instances to produce the necessary wind profiles. The balloons require an hour to rise through the altitude range, sometimes drifting far from the area where the wind measurement is desired. As a result, the correlation between the actual winds encountered by the vehicle and those measured is reduced. NASA is investigating the potential of alternate wind sensors to produce more local wind measurement in less time to increase this correlation. Lidar is one technique under investigation.
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Streatfeild, C. "Offshore wind: scale of opportunity." In Offshore Wind Technology. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0066.

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Briggs, C. "Proactive turbine maintenance integrated support for wind turbine operations." In Offshore Wind Technology. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0067.

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Hendriks, B. "Megaturbines: an engineering background to the historic growth in turbine size and future developments." In Offshore Wind Technology. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0068.

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Plet, C. "Power frequency optimisation." In Offshore Wind Technology. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0069.

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MacLeod, N. "DC Transmission." In Offshore Wind Technology. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0070.

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Jones, C. "JACOBS: cable technology in offshore transmission." In Offshore Wind Technology. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0071.

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"Specialising in the impossible: reducing repair times in offshore wind O&M." In Offshore Wind Technology. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0072.

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Laupattarakasem, Peth, W. Linwood Jones, and Christopher C. Hennon. "SeaWinds Hurricane Wind Retrievals and Comparisons with H*Wind Surface Winds Analyses." In 2008 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4778849.

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Reports on the topic "Wind"

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Friehe, Carl A., and Jesus Ruiz-Plancarte. Wind and Wind Stress Measurements in HiRes. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada533833.

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Cris Hein, Michael Schirmacher, Ed Arnett, and Manuela Huso. Win(d)-Win(d) Solutions for wind developers and bats. Office of Scientific and Technical Information (OSTI), October 2011. http://dx.doi.org/10.2172/1038838.

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Shahidehpour, Mohammad. WINS. Market Simulation Tool for Facilitating Wind Energy Integration. Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1188373.

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Inghram, M. G. Wind data from Point MacKenzie Wind Station, 1983. Alaska Division of Geological & Geophysical Surveys, 1988. http://dx.doi.org/10.14509/1375.

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Inghram, M. G. Wind data from Point MacKenzie Wind Station, 1984. Alaska Division of Geological & Geophysical Surveys, 1988. http://dx.doi.org/10.14509/1376.

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Inghram, M. G. Wind data from Point MacKenzie Wind Station, 1985. Alaska Division of Geological & Geophysical Surveys, 1988. http://dx.doi.org/10.14509/1377.

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Inghram, M. G. Wind data from Point MacKenzie Wind Station, 1986. Alaska Division of Geological & Geophysical Surveys, 1988. http://dx.doi.org/10.14509/1378.

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David C. Morris and Dr. Will D. Swearingen. Wind Fins: Novel Lower-Cost Wind Power System. Office of Scientific and Technical Information (OSTI), October 2007. http://dx.doi.org/10.2172/917314.

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Smith, Ken, and John Wolar. Wind Resource Assessment and Requested Wind Turbine Recommendations. Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1345828.

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Author, Not Given. Wind Powering America Podcasts, Wind Powering America (WPA). Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1041355.

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