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Record W7161843189 · doi:10.82308/41299

CFD analysis and design of wind turbine ice protection systems

2015· dissertation· en· W7161843189 on OpenAlexaboutno aff
David Switchenko

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsIcingTurbineWind powerAerodynamicsComputational fluid dynamicsTurbine bladeWind speedRotor (electric)

Abstract

fetched live from OpenAlex

Cold climate regions have a high potential for wind energy production, but can also be characterized by frequent atmospheric icing events, which can significantly reduce the annual power production of a wind farm. Ice that accretes on turbine blades degrades their aerodynamic performance and reduces their power output. Thus, there is a need for more accurate assessment of the effect of atmospheric icing on wind turbines and for strategies to protect turbine blades from icing.The present work uses CFD analysis to focus on two important engineering issues related to wind turbine blade icing: the wind turbine performance loss due to blade icing, and the design of blade heating systems to prevent ice accretion. All CFD simulations are performed using the FENSAP-ICE simulation system.First, CFD simulations are used to predict the impact atmospheric icing has on wind turbine power production. Fully 3D simulations are performed considering the rotor geometry of the National Renewable Energy Laboratory (NREL) Unsteady Aerodynamics Experiment (UAE) Phase VI rotor. Four representative icing conditions are simulated. The resulting ‘1-hour‘ ice shapes are shown to reduce rotor torque, and therefore resulting power output, by up to 60%. Furthermore, at high wind speeds the NREL turbine blade is regulated by intentional blade stall to prevent very high torque and overproduction. CFD simulations showed that at these wind speeds, ice accretion could increase the wind turbine rotor torque significantly, potentially damaging the turbine due to overproduction and creating possible safety concerns. Next, the FENSAP-ICE system is used to predict the power required and effective coverage region needed for an anti-icing system to prevent ice accretion on the NREL UAE Phase VI rotor. In all cases the power required to keep the rotor ice-free was less than the rated power of the turbine.Lastly, a CFD simulation of a real-world, long-term, 17-hour icing event that took place at a wind farm in the Gaspé Peninsula of Québec was performed. Results of power loss successfully matched that which occurred on site. Moreover, it was determined that an anti-icing system used during a similar icing event could protect against icing in a self-sufficient manner.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.233
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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