MétaCan
Menu
Back to cohort

Model-free control of the dynamic lift of a wind turbine blade section: experimental results

2022· article· en· W4281741960 on OpenAlexaff
Loïc Michel, Ingrid Neunaber, Rishabh Mishra, Caroline Braud, Franck Plestan, Jean‐Pierre Barbot, Xavier Boucher, Cédric Join, Michel Fliesś

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsLift (data mining)AerodynamicsWind tunnelTurbine bladeTurbineEngineeringTrailing edgeControl theory (sociology)Computer scienceMarine engineeringMechanical engineeringStructural engineeringAerospace engineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Abstract This work addresses the problem of developing control algorithms for the control of the aerodynamic lift of wind turbine blades using air injection, taking into account disturbances caused by turbulent perturbations. For this, a test bench is used where the lift of a 2D blade section in a wind tunnel can be controlled by a set of micro-jets close to the trailing edge. Through a continuous, local identification of the lift variations a model-free control that does not need any prior knowledge of the system is proposed. It allows the control of the flow of the micro-jets and stabilizes the lift around a tracking reference. The ability of the proposed control algorithm to track the lift reference when subjected to external perturbations, i.e., gusts, is discussed. In particular, this work demonstrates that the lift can be set to particular values using the proposed control strategy, and can be re-stabilized to pre-gust lift conditions. Experimental results illustrate globally the feasibility of such a control.

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 score0.091
Threshold uncertainty score0.415

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicFluid Dynamics and Turbulent FlowsFrench-language works237,207