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Record W2997555080 · doi:10.2514/6.2020-2043

Surface pressure and coherent structure evolution on an axially accelerated delta wing

2020· article· en· W2997555080 on OpenAlexaff
Han Tu, Matthew Marzanek, Melissa Green, David E. Rival

Bibliographic record

VenueAIAA Scitech 2020 Forum · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsQueen's University
Fundersnot available
KeywordsVorticityAirfoilWingAngle of attackMechanicsLyapunov exponentFlow (mathematics)Pitching momentLift (data mining)NACA airfoilPhysicsDragGeologyAerodynamicsGeometryAerospace engineeringVortexEngineeringMathematicsComputer science

Abstract

fetched live from OpenAlex

The flow field, forces, moments, and surface pressure of a NACA 0012 airfoil wing with triangular planform geometry undergoing steady and unsteady translations were measured as a model of a unmanned combat air vehicles encountering unsteady environments. To characterize the evolution of the flow field structures, a Lagrangian flow field analysis including the finite-time Lyapunov exponent (FTLE) was included. Results show that axial acceleration can induce flow reattachment at high angles of attack, and FTLE can indicate the reattachment and its location as it progresses along the wing chord. At such location, the relevant change in surface pressure distribution is also observed in the experimental data, as well as correlated fluctuations in lift, drag, and pitching moment. This augmented understanding of vorticity production, reorientation, and annihilation around and in the wake of complex three-dimensional bodies may provide critical insight for effective flow-control development on vehicles unsteady environments.

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)0.000
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.208
Teacher spread0.198 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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