MétaCan
Menu
Back to cohort
Record W2105885184 · doi:10.2514/1.31488

Transition Point Detection from the Surface Pressure Distribution for Controller Design

2008· article· en· W2105885184 on OpenAlexafffund
Andrei Vladimir Popov, Ruxandra Mihaela Botez, Michel Labib

Bibliographic record

VenueJournal of Aircraft · 2008
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsÉcole de Technologie Supérieure
FundersConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsSurface pressureDistribution (mathematics)Surface (topology)Control theory (sociology)Computer scienceAerospace engineeringMathematicsMechanicsPhysicsGeometryEngineeringMathematical analysisArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

airfoil types: NACA 4415 and WTEA-TE1, as well as for 17 modified WTEA-TE1 airfoil shapes, obtained by displacing the flexible wing upper surface using a single point control mechanism. The second derivative of the pressure distribution is calculated, using two interpolation schemes: piecewise cubic Hermite interpolating polynomial and Spline, from which it is determined that transition may be identified as the location of maximum curvature in the pressure distribution. The results of this method are validated using the well-known XFoil code, which is used to theoretically calculate the transition point position. Advantages of this new method in the real-time control of the location of the transition point are presented.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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 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

Citations80
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueJournal of AircraftSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207