MétaCan
Menu
Back to cohort
Record W4210863426 · doi:10.11159/jffhmt.2022.003

Parametric Study of Fluid Injection Winglet on Aerodynamic Performance of the Wing

2022· article· en· W4210863426 on OpenAlexvenueno aff
Hariprasad Thimmegowda, Yadu Krishnan S, G S Gisa, Vootukuri Gowtham Reddy

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2022
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsnot available
Fundersnot available
KeywordsWingtip deviceWingAerodynamicsParametric statisticsStructural engineeringEngineeringAerospace engineeringMathematics

Abstract

fetched live from OpenAlex

The possibility of fluid injection winglets for reducing drag without increasing the wingspan of aircraft was investigated in this research. The study used a rectangular baseline wing made of NACA 0012 cross-sectional airfoil with zero-twist and a slot at the wingtip for fluid injections. The commercial CFD tool ANSYS Fluent solver was used to do the computational analysis. For the analysis, numerous parameters such as vertical and downward injection methods, injection velocity range, and range of angles of attack are taken into account. The injection velocities and aerodynamic characteristics such as coefficient of lift, drag, and L/D ratio are shown to have a strong relationship in this simulation. There was an improvement in the distribution of pressure around the wingtip. The reduction of wingtip vortices induced by vertical fluid injection causes a significant increase in the L/D ratio. In comparison to the upward injection method with increased angles of attack, the results reveal that downward fluid injection is better at enhancing aerodynamic efficiency.

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)0.001
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 score0.229
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.213
Teacher spread0.203 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Fluid Flow Heat and Mass TransferSame topicAerodynamics and Fluid Dynamics ResearchFrench-language works237,207