999 resultados para wind identification


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В статье представлено развитие принципа построения автоматической пилотажно-навигационной системы (АПНС) для беспилотного летательного аппарата (БЛА). Принцип заключается в синтезе комплексных систем управления БПЛА не только на основе использования алгоритмов БИНС, но и алгоритмов, объединяющих в себе решение задач формирования и отработки сформированной траектории резервированной системой управления и навигации. Приведены результаты аналитического исследования и данные летных экспериментов разработанных алгоритмов АПНС БЛА, обеспечивающих дополнительное резервирование алгоритмов навигации и наделяющих БЛА новым функциональной способностью по выходу в заданную точку пространства с заданной скоростью в заданный момент времени с учетом атмосферных ветровых возмущений. Предложена и испытана методика идентификации параметров воздушной атмосферы: направления и скорости W ветра. Данные летных испытаний полученного решения задачи терминальной навигации демонстрируют устойчивую работу синтезированных алгоритмов управления в различных метеоусловиях. The article presents a progress in principle of development of automatic navigation management system (ANMS) for small unmanned aerial vehicle (UAV). The principle defines a development of integrated control systems for UAV based on tight coupling of strap down inertial navigation system algorithms and algorithms of redundant flight management system to form and control flight trajectory. The results of the research and flight testing of the developed ANMS UAV algorithms are presented. The system demonstrates advanced functional redundancy of UAV guidance. The system enables new UAV capability to perform autonomous multidimensional navigation along waypoints with controlled speed and time of arrival taking into account wind. The paper describes the technique for real-time identification of atmosphere parameters such as wind direction and wind speed. The flight test results demonstrate robustness of the algorithms in diverse meteorological conditions.

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Gemstone Team Renewables

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This paper proposes a method for wind turbine mode identification using the multivariable output error statespace (MOESP) identification algorithm. The paper incorporates a fast moving window QR decomposition and propagator method from array signal processing, yielding a moving window subspace identification algorithm. The algorithm assumes that the system order is known as a priori and remains constant during identification. For the purpose of extracting modal information for turbines modelled as a linear parameter varying (LPV) system, the algorithm is applicable since a nonlinear system can be approximated as a piecewise time invariant system in consecutive data windows. The algorithm is exemplified using numerical simulations which show that the moving window algorithm can track the modal information. The paper also demonstrates that the low computational burden of the algorithm, compared to conventional batch subspace identification, has significant implications for online implementation.

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Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.

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Any kind of imbalance in the operation of a wind turbine has adverse effect on the downstream torsional components as well as tower structure. It is crucial to detect imbalance at its very inception. The identification of the type of imbalance is also required so that appropriate measures of fault accommodation can be performed in the control system. In particular, it is important to distinguish between mass and aerodynamic imbalance. While the former is gradually caused by a structural anomaly (e.g. ice deposition, moisture accumulation inside blade), the latter is generally associated to a fault in the pitch control system. This paper proposes a technique for the detection and identification of imbalance fault in large scale wind turbines. Unlike most other existing method it requires only the rotor speed signal which is readily available in existing turbines. Signature frequencies have been proposed in this work to identify imbalance type based on their physical phenomenology. The performance of this technique has been evaluated by simulations using an existing benchmark model. The effectiveness of the proposed method has been confirmed by the simulation results.

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A small low air-speed wind turbine blade case study is used to demonstrate the effectiveness of a materials and design selection methodology described by Monroy Aceves et al. (2008) [24] for composite structures. The blade structure comprises a shell of uniform thickness and a unidirectional reinforcement. The shell outer geometry is fixed by aerodynamic considerations. A wide range of lay-ups are considered for the shell and reinforcement. Structural analysis is undertaken using the finite element method. Results are incorporated into a database for analysis using material selection software. A graphical selection stage is used to identify the lightest blade meeting appropriate design constraints. The proposed solution satisfies the design requirements and improves on the prototype benchmark by reducing the mass by almost 50%. The flexibility of the selection software in allowing identification of trends in the results and modifications to the selection criteria is demonstrated. Introducing a safety factor of two on the material failure stresses increases the mass by only 11%. The case study demonstrates that the proposed design methodology is useful in preliminary design where a very wide range of cases should be considered using relatively simple analysis. © 2011 Elsevier Ltd.

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This paper deals with identification of dynamics in suction control of airfoils for low Reynolds number regimes (8 x 10^4 - 5 x 10^5). In particular, the dynamics of interest is the map that relates suction pressure and surface pressure. Identification of such dynamics is of use to a variety of active control applications including suction control in small/medium wind turbines which operate in these Reynolds number regimes. Prior research has largely focused on higher Reynolds number regimes, creating a need for such a study. Towards identifying the said dynamic relations, experiments were conducted on NACA0012 airfoil in a wind tunnel. The dynamic relation between suction and surface pressure was identified as an overdamped second order system.

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In this paper climate discrete-time dynamic models for the inside air temperature of two different greenhouses are identified, using data acquired during two different periods of the year. These models employ data from air temperature and relative humidity (both outside and inside the greenhouse), solar radiation, wind speed, and control inputs (ventialtion, etc.).

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Ecological studies that examine species-environment relationships are often limited to several meteorological parameters, i.e. mean air temperature, relative humidity, precipitation, vapour pressure deficit and solar radiation. The impact of local wind, its speed and direction are less commonly investigated in aerobiological surveys mainly due to difficulties related to the employment of specific analytical tools and interpretation of their outputs. Identification of inoculum sources of economically important plant pathogens, as well as highly allergenic bioaerosols like Cladosporium species, has not been yet explored with remote sensing data and atmospheric models such as Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT). We, therefore, performed an analysis of 24 h intra-diurnal cycle of Cladosporium spp. spores from an urban site in connection with both the local wind direction and overall air mass direction computed by HYSPLIT. The observational method was a volumetric air sampler of the Hirst design with 1 h time resolution and corresponding optical detection of fungal spores with light microscopy. The atmospheric modelling was done using the on-line data set from GDAS with 1° resolution and circular statistical methods. Our results showed stronger, statistically significant correlation (p ≤ 0.05) between high Cladosporium spp. spore concentration and air mass direction compared to the local wind direction. This suggested that a large fraction of the investigated fungal spores had a regional origin and must be located more than a few kilometers away from the sampling point.

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This paper deals with the problem of identification and semiactive control of smart structures subject to unknown external disturbances such as earthquake, wind, etc. The experimental setup used is a 6-story test structure equipped with shear-mode semiactive magnetorheological actuators being installed in WUSCEEL. The experimental results obtained have verified the effectiveness of the proposed control algorithms

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The Cassini flyby of Jupiter occurred at a time near solar maximum. Consequently, the pre-Jupiter data set reveals clear and numerous transient perturbations to the Parker Spiral solar wind structure. Limited plasma data are available at Cassini for this period due to pointing restrictions imposed on the instrument. This renders the identification of the nature of such structures ambiguous, as determinations based on the magnetic field data alone are unreliable. However, a fortuitous alignment of the planets during this encounter allowed us to trace these structures back to those observed previously by the Wind spacecraft near the Earth. Of the phenomena that we are satisfactorily able to trace back to their manifestation at 1 AU, two are identified as being due to interplanetary coronal mass ejections. One event at Cassini is shown to be a merged interaction region, which is formed from the compression of a magnetic cloud by two anomalously fast solar wind streams. The flux-rope structure associated with this magnetic cloud is not as apparent at Cassini and has most likely been compressed and deformed. Confirmation of the validity of the ballistic projections used here is provided by results obtained from a one-dimensional magnetohydrodynamic projection of solar wind parameters measured upstream near the Earth. It is found that when the Earth and Cassini are within a few tens of degrees in heliospheric longitude, the results of this one-dimensional model predict the actual conditions measured at 5 AU to an impressive degree. Finally, the validity of the use of such one-dimensional projections in obtaining quasi-solar wind parameters at the outer planets is discussed.

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The occurrence of wind storms in Central Europe is investigated with respect to large-scale atmospheric flow and local wind speeds in the investigation area. Two different methods of storm identification are applied for Central Europe as the target region: one based on characteristics of large-scale flow (circulation weather types, CWT) and the other on the occurrence of extreme wind speeds. The identified events are examined with respect to the NAO phases and CWTs under which they occur. Pressure patterns, wind speeds and cyclone tracks are investigated for storms assigned to different CWTs. Investigations are based on ERA40 reanalysis data. It is shown that about 80% of the storm days in Central Europe are connected with westerly flow and that Central European storm events primarily occur during a moderately positive NAO phase, while strongly positive NAO phases (6.4% of all days) account for more than 20% of the storms. A storm occurs over Central Europe during about 10% of the days with a strong positive NAO index. The most frequent pathway of cyclone systems associated with storms over Central Europe leads from the North Atlantic over the British Isles, North Sea and southern Scandinavia into the Baltic Sea. The mean intensity of the systems typically reaches its maximum near the British Isles. Differences between the characteristics for storms identified from the CWT identification procedure (gale days, based on MSLP fields) and those from extreme winds at Central European grid points are small, even though only 70% of the storm days agree. While most storms occur during westerly flow situations, specific characteristics of storms during the other CWTs are also considered. Copyright © 2009 Royal Meteorological Society

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Introducing a parameterization of the interactions between wind-driven snow depth changes and melt pond evolution allows us to improve large scale models. In this paper we have implemented an explicit melt pond scheme and, for the first time, a wind dependant snow redistribution model and new snow thermophysics into a coupled ocean–sea ice model. The comparison of long-term mean statistics of melt pond fractions against observations demonstrates realistic melt pond cover on average over Arctic sea ice, but a clear underestimation of the pond coverage on the multi-year ice (MYI) of the western Arctic Ocean. The latter shortcoming originates from the concealing effect of persistent snow on forming ponds, impeding their growth. Analyzing a second simulation with intensified snow drift enables the identification of two distinct modes of sensitivity in the melt pond formation process. First, the larger proportion of wind-transported snow that is lost in leads directly curtails the late spring snow volume on sea ice and facilitates the early development of melt ponds on MYI. In contrast, a combination of higher air temperatures and thinner snow prior to the onset of melting sometimes make the snow cover switch to a regime where it melts entirely and rapidly. In the latter situation, seemingly more frequent on first-year ice (FYI), a smaller snow volume directly relates to a reduced melt pond cover. Notwithstanding, changes in snow and water accumulation on seasonal sea ice is naturally limited, which lessens the impacts of wind-blown snow redistribution on FYI, as compared to those on MYI. At the basin scale, the overall increased melt pond cover results in decreased ice volume via the ice-albedo feedback in summer, which is experienced almost exclusively by MYI.

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Il seguente elaborato si concentra sull'identifi�cazione strutturale di sistemi soggetti a sollecitazioni aeroelastiche e nello speci�fico l'attenzione viene rivolta ad impalcati da ponte. Si analizzano i concetti principali caratterizzanti il campo dell'aeroelasticità indagando i fattori dominanti che entrano in gioco sul piano teorico. In seguito, si considera il metodo di identifi�cazione strutturale chiamato Covariance Block Hankel Matrix (CBHM) utilizzato come strumento di derivazione dei coeffi�cienti aeroelastici propri della struttura. Infi�ne, si indaga il comportamento di questo metodo di identi�ficazione al variare di una serie di parametri chiave e all'interno di diversi scenari, visionando risultati ottenuti tramite una serie di test eff�ettuati per provare l'a�dattabilità del metodo stesso al variare delle condizioni che caratterizzano il sistema.

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Diversity of endolithic Dry Valley rock microorganisms was studied by evaluating the presence of morphotypes in enrichments. Storage of rock samples for 16 h over dry ice affected the diversity of endolithic organisms, especially that of algae and fungi. Diversity in various samples depended on rock location and exposure, on the rock type, and to some extent on the pH of the pulverized rock samples. In most cases sandstone contained more morphotypes than dolerite or granite. Presence of many different phototrophs resulted in greater diversity of the heterotrophs in the enrichments. Samples from Linnaeus Terrace and Battleship Promontory had higher morphotype (MT) numbers than those from more exposed sites such as New Mountain, University Valley, Dais, or Mt. Fleming. Beacon sandstone (13 samples) from Linnaeus Terrace varied greatly with respect to MT numbers, although the pH values ranged only from 4.2-5.3. The highest MT number of 24 per sample was obtained from the upper surface of a flat boulder tilted to the North. Only two MT's were found in a hard sandstone sample from the wind-exposed and more shaded east side of the Terrace. 15 sandstone samples from Battleship Promontory contained more diverse populations: there occurred a total of 131 different MT's in these samples as compared to only 68 in Linnaeus Terrace samples. Cysts of colorless flagellates were found in some Battleship Promontory samples; rnost samples were populated with a wealth of different cyanobacteria. Studies on the distribution of actinomycete morphotypes in Linnaeus Terrace sandstone revealed great differences between individual boulders. Identification tests and lipid analyses made with representative strains of the isolated 1500 pure cultures led to genus names such as Caulobacter, Blastobacter, Hyphomicrobium, Micrococcus, Arthrobacter, Brevibacterium, Corynebacterium, Bifidobacterium, Mycobacterium, Nocardia (Amycolata), Micromonospora, Streptomyces, Blastococcus, and Deinococcus. Our data demonstrate the great diversity of Antarctic endolithic microbial populations.