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Record W3118753997 · doi:10.2514/6.2021-0036

Riblet design, manufacturing, and measurements – A new rapid iteration process

2021· article· en· W3118753997 on OpenAlexaff
Peter A. Leitl, Christoph Feichtinger, Henry C. Bilinsky, Andreas Flanschger, Mitchell S. Quinn, Inigo Ortiz de Viñaspre, Barbara Forster

Bibliographic record

VenueAIAA Scitech 2021 Forum · 2021
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsMicrosemi (Canada)
Fundersnot available
KeywordsMicrofabricationProcess (computing)Manufacturing engineeringAviationDragProcess capabilitySystems engineeringComputer scienceMechanical engineeringWork in processEngineeringAerospace engineeringFabricationOperations management

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2021-0036.vid The growing application of riblets in various industries, mainly aviation and wind turbines, increase the need to develop riblet designs of higher drag reduction capabilities. Bionic Surface Technologies (BST) and MicroTau present a new rapid iteration process to bring riblet development into the next level. By combining the Direct Contactless Microfabrication technology (DCM) of MicroTau and the simulation and measurement capabilities of BST the time to iterate riblet design, manufacturing and testing drops significantly. The present work defines the process and shows examples of the highly effective process.

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)0.012
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0120.007

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 head0.027
GPT teacher head0.243
Teacher spread0.217 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

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