MétaCan
Menu
Back to cohort
Record W4416854466 · doi:10.1139/tcsme-2025-0013

Improving vertical-axis turbine efficiency with chordwise actively deforming blades

2025· article· en· W4416854466 on OpenAlexaffvenue
Seyed Vahid Banijamali, Mathieu Olivier

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAirfoilDeformation (meteorology)TurbineAmplitudeRotation (mathematics)Function (biology)

Abstract

fetched live from OpenAlex

This study investigates the effects of employing actively deforming airfoils on the performance of vertical-axis turbines. Two-dimensional unsteady Reynolds-averaged Navier–Stokes simulations were conducted on a single-bladed H-type Darrieus vertical-axis turbine model equipped with a chordwise deforming NACA0015 airfoil. During the rotation cycle, two-thirds of the airfoil’s chord, corresponding to the trailing edge, undergoes prescribed deformations, while the remaining one-third remains undeformed. An overset mesh technique, combined with radial basis function morphing, is used to manage the deformation and movement of the mesh. The study explores the impact of deformation amplitudes and timings on the turbine’s efficiency at various tip-speed ratios. Preliminary simulations evaluating the effect of steady deformation amplitudes on the instantaneous power coefficient are used to identify promising active deformation patterns. Subsequently, the turbine’s performance is assessed using simulations involving two-phase active deformation patterns. The results show a 3.55% efficiency improvement at a near-optimal tip-speed ratio with actively deforming airfoils. More significant relative improvements are observed at low tip-speed ratios, while higher tip-speed ratios yield only marginal benefits.

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: none
Teacher disagreement score0.932
Threshold uncertainty score0.501

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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicWind Energy Research and DevelopmentFrench-language works237,207