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Record W2802229113 · doi:10.1139/tcsme-2015-0041

AERODYNAMIC EFFECT OF 3D PATTERN ON AIRFOIL

2015· article· en· W2802229113 on OpenAlexvenueno aff
Xiaoyu Wang, Soo Young Lee, Pilkee Kim, Jongwon Seok

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsnot available
Fundersnot available
KeywordsAirfoilLift coefficientDrag coefficientLift (data mining)FluentDragLift-to-drag ratioAerodynamicsMechanicsZero-lift drag coefficientAerodynamic centerDrag divergence Mach numberLift-induced dragAngle of attackMaterials scienceComputer simulationPhysicsComputer sciencePitching momentReynolds numberTurbulence

Abstract

fetched live from OpenAlex

It is known from recent observations that the textured surface plays a role in reducing the drag force and increasing the lift force of a moving body. Comparing the numerical simulation between smooth surface and textured surface in this study, we also observe that the textured surface reduces the drag coefficient and increases the lift coefficient of the surface. As for the two simulation models performed in this study, we use the modified NACA0018 model for the basic airfoil configurations. After the simulation using Fluent, the results about the two models are mutually compared, and we found that the textured airfoils can decrease the drag coefficient and increase the lift coefficient dramatically. We also found that there exists an optimal angle, at which both the drag coefficient decrement and the lift coefficient become maximum. The final goal of this study is to design the airfoil with the reduced drag coefficient and the improved 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)0.000
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 score0.609
Threshold uncertainty score0.997

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 head0.216
Teacher spread0.206 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicAerodynamics and Fluid Dynamics ResearchFrench-language works237,207