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Record W2089644731 · doi:10.1115/imece2012-89141

A Model for the Nonlinear Buckling of Human Aorta

2012· article· en· W2089644731 on OpenAlexafffund
Marco Amabili, Kostas Karazis, Rosaire Mongrain, Nastaran Shahmansouri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMechanicsBucklingOrthotropic materialMaterials scienceFlow (mathematics)Nonlinear systemAortaBifurcationBlood flowDeformation (meteorology)Structural engineeringPhysicsFinite element methodEngineeringComposite material

Abstract

fetched live from OpenAlex

Human aortas are subjected to large mechanical stresses due to blood flow pressurization and through contact with the surrounding tissue. It is essential that the aorta does not lose stability by buckling for its proper functioning to ensure proper blood flow. A refined reduced-order bifurcation analysis model is employed to examine the stability of an aortic segment subjected to internal blood flow. The structural model is based on a nonlinear cylindrical orthotropic laminated composite shell theory that assumes three aortic wall layers representing the tunica intima, media and adventitia. The fluid model contains the unsteady effects obtained from linear potential flow theory and the steady viscous effects obtained from the time-averaged Navier-Stokes equations. Residual stresses due to pressurization are evaluated and included in the model. The aortic segment loses stability by divergence with deformation of the cross-section at a critical flow velocity for a given static pressure, exhibiting a strong subcritical behaviour with partial or total collapse of the inner wall. Subsequent analyses including the effect of geometric wall imperfections indicate that imperfections in the axial direction have a more profound effect on the stability of the aorta decreasing the critical flow velocity for buckling.

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 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 score0.630
Threshold uncertainty score0.129

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 head0.048
GPT teacher head0.267
Teacher spread0.218 · 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 teacher head, 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
Published2012
Admission routes2
Has abstractyes

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