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Record W4396526658 · doi:10.47176/jafm.17.7.2404

An Engineering Approach to Improve Performance Predictions for Wind Turbine Applications: Comparison with Full Navier-Stokes Model and Experimental Measurements

2024· article· en· W4396526658 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Fluid Mechanics · 2024
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTurbineMarine engineeringEnvironmental scienceAerospace engineeringComputer scienceMeteorologyEngineeringPhysics

Abstract

fetched live from OpenAlex

Accurate predictions of aerodynamic performance and near wake expansion around Horizontal Axis Wind Turbine (HAWT) rotors is pivotal for studying wind turbine wake interactions and optimizing wind farm layouts. This study introduces a novel engineering model centered on stall delay correction to enhance the precision of the Actuator Disk Method (ADM) predictions in both aerodynamic performance and near wake expansion around HAWT rotors. The model is developed based on a comprehensive study of the 3D lift coefficient evolution over the rotor blade, incorporating a shift parameter that considers both stall angle detection and radial decrement. The proposed approach demonstrates remarkable agreements, showcasing discrepancies as low as 7% for both loads and axial wake predictions. These quantifiable results underscore the effectiveness of the model in capturing intricate aerodynamic phenomena. Looking forward, the success of this approach opens avenues for broader applications, guiding future research in wind energy towards improved simulation accuracy and optimized wind farm designs.

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.

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: none
Teacher disagreement score0.733
Threshold uncertainty score0.647

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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