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Experimental load measurement on a yawed wind turbine and comparison to FAST

2020· article· en· W3088300146 on OpenAlexaff
Farid Samara, David A. Johnson

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTurbineStall (fluid mechanics)TorqueAerodynamicsTip-speed ratioWakeAirfoilAzimuthWind powerRotor (electric)Marine engineeringMechanicsEnvironmental sciencePhysicsAerospace engineeringEngineeringMechanical engineeringElectrical engineeringOptics

Abstract

fetched live from OpenAlex

Abstract Current interest in unsteady aerodynamics of yawed turbines for wake redirection and cyclic load alleviation requires a clear understanding of turbine load fluctuation with azimuth position. To that end, an experimental 3.5 m diameter wind turbine rig was designed to measure rotor torque, flapwise/edgewise blade root bending moment, and normal force coefficient at r/R=0.66 and 0.82. The turbine was tested inside a large scale wind generation facility with a blockage ratio of 7%. Simultaneously and in time-resolved fashion, measurements were collected to produce phase-averaged performance parameters that are presented versus azimuth while the turbine is operating at different tip speed ratios and yaw angles. The NREL FAST code predictions were then compared to experimental data under the same conditions. It was found that in non-yawed conditions, FAST predictions were accurate but discrepancies start to emerge when the turbine is yawed and operating in dynamic stall conditions. In non-yawed cases, the discrepancy between experimental and FAST data is less than 7% when the flow is attached and increases to 22% when the flow is stalled. One of the main sources of discrepancy found is in the process of correcting the airfoil coefficients to account for 3D flow effects using AirfoilPrep.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.060
GPT teacher head0.261
Teacher spread0.201 · 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 designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

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