MétaCan
Menu
Back to cohort
Record W4317636951 · doi:10.2514/6.2023-1953

Wind Tunnel Test Results for Staggered 3-D Riblets

2023· article· en· W4317636951 on OpenAlexaff
Paul D. McClure

Bibliographic record

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsWind tunnelDragRidgeDeckBoundary layerAerodynamicsMarine engineeringGeologyStructural engineeringEngineeringMechanicsPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-1953.vid This paper describes wind tunnel experiments conducted on riblets with gradual streamwise ridge height variation, known as ‘3-D’ riblets. Riblets are V-shaped or blade-type grooves with height and spacing between peaks of .002 to .005 inches, depending on conditions. The goal of 3-D riblet designs is to modify the flow topology in a manner similar to conventional riblets but with reduced wetted area, thereby achieving a larger drag reduction. A critical feature of the patent pending riblets tested is the staggering between neighboring riblets and shallow slope of the transition regions. Details of the design process and CFD simulations are covered in a concurrently written paper by Smith, Yagle and McClure. Wind tunnel testing was completed by use of a splitter plate and boundary layer rakes and relied on momentum thickness calculations to determine the drag for smooth and riblet surfaces. Riblets were produced on film and the metrology indicated precise matching to the design specifications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.224
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAIAA SCITECH 2023 ForumSame topicFluid Dynamics and Turbulent FlowsFrench-language works237,207