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Record W3084409283 · doi:10.32393/csme.2020.1215

Experimental Testing, Modeling, and Simulation of 3D Printed Composite Material for Morphing Wing Application

2020· article· en· W3084409283 on OpenAlex
Rebecca Rajs, Marc Palardy-Sim, Guillaume Renaud, Michael B. Jakubinek, Farjad Shadmehri

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 3 · 2020
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsNational Research Council CanadaConcordia University
FundersNational Research Council Canada
KeywordsMorphing3d printedWingComposite numberComputer scienceEngineering drawingMechanical engineeringStructural engineeringComputer graphics (images)EngineeringManufacturing engineering

Abstract

fetched live from OpenAlex

Morphing aircraft structures offer opportunities for the development of new aerospace technologies. A benchtopscale model of a morphed leading edge composed of a carbon nanotube-polyurethane stretchable skin and 3D printed substructure was designed and developed To improve the overall accuracy of the leading edge shape, the design of the sub-structure is to be optimized. This paper describes the material characterization of the 3D printed sub-structure. The properties of the sub-structure material were determined through flexural testing of 3D printed coupons. The material properties were then calibrated through finite element modeling of the test. Finally, these properties were applied to the model of a test specimen of variable thickness in order to validate their applicability for finite element analysis of increasingly complex shapes, such as those found in the morphed leading edge benchtop model. I.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.709
Threshold uncertainty score0.681

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)0.0000.000

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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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