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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 OpenAlex
Peter A. Leitl, Christoph Feichtinger, Henry C. Bilinsky, Andreas Flanschger, Mitchell S. Quinn, Inigo Ortiz de Viñaspre, Barbara Forster

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.649
Threshold uncertainty score0.706

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)0.0010.000

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 head0.027
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