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Record W4317632748 · doi:10.2514/6.2023-1585

Numerical Investigation of a Dynamically Morphing UAS-S45 Wing Airfoil at moderate Reynolds number

2023· article· en· W4317632748 on OpenAlexaff
Musavir Bashir, Nicola Zonzini, Ruxandra Mihaela Botez, Alessandro Ceruti, Tony Wong

Bibliographic record

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsAirfoilTrailing edgeLift coefficientStall (fluid mechanics)MorphingPitching momentDeflection (physics)Leading edgeMechanicsLift-to-drag ratioAngle of attackWingReynolds numberAerodynamicsDragAerospace engineeringPhysicsStructural engineeringComputer scienceTurbulenceEngineeringClassical mechanics

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-1585.vid The unsteady flow characteristics and responses of the UAS-S45 airfoil with a morphing trailing edge shape at high angles of attack undergoing deflections are investigated at a Reynolds number of 2.4 × 106. The flexible trailing edge was simulated using a computational fluid dynamics approach using a dynamic mesh and user-defined functions. The goal was to achieve a dynamically deflected trailing edge in an unsymmetrical airfoil and assess the influence of unsteady morphing trailing edge deflection on transient forces and flow field unsteadiness. The steady aerodynamic characteristics of the morphing deflection and the conventional deflection was initially studied. Then, the unsteady aerodynamic characteristics of the morphing wing was investigated as the trailing edge deflects at different rates. The dynamic flow responses to downward deflections are studied using the turbulence model. The time histories of the lift and drag coefficient responses exhibit a proportional relationship between the morphing frequency and the slope of response at which these parameters evolve. Coefficients of lift, drag, and moment of the deflected trailing edge airfoils were compared to those of the reference airfoils for various angles of attack. The numerical results show that the transient lift coefficient in the deflection process was higher than that of the static case at different angles of attack. The transient lift coefficient were higher as the deflection frequency increased. It was also revealed that the trailing edge deflection did not favor the flow reattachment. In addition, the dynamic mesh strategy, cell quality, and the proposed method of deforming the morphing trailing-edge was presented. increased. It was also revealed that the trailing edge deflection did not favor the flow reattachment. In addition, the dynamic mesh strategy, cell quality, and the proposed method of deforming the morphing trailing-edge was 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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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