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Enregistrement W7033969748

Study of Atmospheric Ice Accretion on Wind Turbine Blades

2021· dissertation· en· W7033969748 sur OpenAlexaboutno aff

Notice bibliographique

RevueMunin Open Research Archive (The Arctic University of Norway) · 2021
Typedissertation
Langueen
DomaineArts and Humanities
ThématiqueLiterature and Cultural Memory
Établissements canadiensnon disponible
Organismes subventionnairesInterregCranfield UniversityUniversitetet i Tromsø
Mots-clésIcingWind powerIcing conditionsTurbineWind speedMaximum sustained windAccretion (finance)Turbine blade
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

<p>This Ph.D. work concerns itself with the atmospheric ice accretion on wind turbine blades. The wind energy has been at the forefront of the renewable energy generation for the last several decades, with the amount and capacity of installed wind turbines steadily increasing. The cold climate (CC) regions around the world like Finland, Germany, Slovak Republic, Norway, Czech Republic, UK, Sweden, Bulgaria, Hungary, Russia, Canada and USA have great potential of wind resources. Estimated wind energy capacity in CC is about 60 GW. [1] However, due to this steady increase in the installed power capacity, more and more turbines have to be placed in regions with harsh geographical conditions, such as arctic regions, in which the temperatures below the normal operating conditions can result in the atmospheric icing to accumulate on the wind turbines particularly along blades. The icing on wind turbines blades leads to negative effects, such as, decreased lift and increased drag, increased mechanical wear and fatigue, possibility of ice throw, which negatively impacts the personnel and life in the area, aeroacoustics noise, generated from iced wind turbines, etc. The icing on wind turbines occurs when super-cooled water droplets collide with the wind turbine structure in the passing clouds (in-cloud icing) and/or freezing rain or drizzle freezes on the exposed wind turbine structure (precipitation icing). Within the scope of this Ph.D. work, the focus is made on the in-cloud icing on the wind turbines.\n<p>While there are existing standards and guidelines for the design and operation of wind turbines in normal, temperate climates, for example, the International Electrotechnical Commission standards for offshore turbines, including IEC 61400-1, IEC 61400-3, and the standards for the processes of type certification, which are commonly used to certify turbines in Europe (IEC 2001, 2005, 2010a, 2010b). However, no such definite framework exists for the design, operation and maintenance of wind turbines in cold, ice-prone regions. Thus, the better understanding of the atmospheric ice accretions on wind turbines and their negative effects, such as losses in power production due to the icing is a critical objective for the successful operation of the wind power in CC, ice-prone regions.\n<p>For the purposes of better understanding of the icing physics, involved in the icing on the wind turbines, the analytical, numerical and the experimental tools are used in this project. The analytical modelling is done by using the ISO 12494 standard: “Atmospheric Icing on Structures” with some modifications done to it, in order to permit analytical modelling of ice accretion on wind turbines, using basic circular cylinders from ISO 12494 as a reference collector. The numerical modelling scheme employs the usage of modern Computational Fluid Dynamics (CFD) tools such an ANSYS FENSAP-ICE and ANSYSFluent which are used to study the ice accretion process on airfoils and blades. These CFD tools allow for the study of icing physics in greater detail than the analytical model allows, for example by simulating the resultant ice shapes and their impact on the aerodynamic performance of the iced airfoils, when compared to the clean ones. The experimental methodology of this work encompasses usage of the icing tunnel experimental data, for the validation purposes of the numerical modelling, and the field measurements data from the Supervisory Control and Data Acquisition (SCADA) system, taken from a wind park operating in the CC region. The main reason for this is to perform a wind resource assessment study in the CC, ice prone region, in addition to the use of supplementary statistical and numerical modelling tools, such as T19IceLossMethod and WindSim.\n<p>The results of atmospheric ice accretion on the wind turbine blades show that the aerodynamic performance changes mainly due to difference in droplet freezing fraction as due to low freezing fraction for the glaze ice conditions, higher amount of the water runback and the aerodynamic heat flux along leading edge is observed which results in the complex horn type ice shapes. The phenomenon of the flow interaction in the third dimension results in the velocity magnitudes being reduced in the 3D simulations, when compared to the 2D simulations. This, in turn, affects the ice accretion process, as the higher velocity magnitudes in the 2D cases result in the higher droplet inertia, collision efficiencies and the maximum impingement angles, which results in more ice mass accreted along the leading edge with the thicker and larger ice shapes present in the 2D simulations.\n<p>The results of wind resource assessment of ice prone region show that power production for wind parks can be lower in CC regions when compared to identical wind parks/turbines situated in warmer temperate climates. However, the icing-related issues and the associated power losses need to be solved. It shows that duration and timing of the icing event is different for different wind turbines in a wind park, which clearly indicates that the icing events depend upon the meteorological conditions, airflow behaviour and also the location of the wind turbine. Even in the same wind park, it is not given that ice will accrete on all wind turbines under the same instrumental and on-site conditions. The wind park layout and changes in flow behaviour affects the occurrence of ice accretion, despite the favourable conditions for icing events being present.\n<p>Two main topics have been considered in this Ph.D. work: the atmospheric ice accretion on wind turbine blade and the performance losses associated with it; and the wind resource assessment in the ice prone region. Both of these topics are of major importance for the wind industry in CC, ice prone regions, due to the challenges present in the form of potential icing conditions and events and the resultant energy production losses. The results obtained in this Ph.D. thesis can be summarized, in short, as follows: power losses due to icing on wind turbines occur not because of a single reason, but through a combination of effects that need to be taken into account carefully during the wind park design process. These effects include the blade profile surface roughness and heat fluxes, which change significantly during the ice accretion process, and, in turn, affect the airflow and droplet behaviour. The change in the accreted ice shape affects both the airflow behaviour and the aerodynamics performance. With the increase in the atmospheric temperature, the type of accreted ice also changes from dry rime to wet glaze ice, which leads to a change in the ice density and also the accreted ice shapes on the wind turbine blades. Generally, wet ice growth is more damaging for wind turbine operations in icing conditions as compared to dry rime ice growth, due to higher degradation of aerodynamic characteristics under the glaze icing conditions.\n<p>The results obtained in this work also provide the need and motivation for improving the understanding about icing effects on the wind turbine blades and the improvement of the existing (or creating new) anti-/de-icing technologies.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,070
Score d'incertitude au seuil0,996

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0020,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,067
Tête enseignante GPT0,301
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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