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Record W262699394 · doi:10.1115/imece2014-39060

Magnetoelastic Analysis of Sandwich Cellular Cylinders

2014· article· en· W262699394 on OpenAlexaff
A.H. Akbarzadeh, Jiawei Fu, Zengtao Chen, D. Pasini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of New BrunswickMcGill University
Fundersnot available
KeywordsHomogenization (climate)EigenstrainMaterials scienceCylinderSandwich-structured compositeMagnetic fieldTopology (electrical circuits)Sandwich panelComposite materialComposite numberGeometryMathematicsPhysics

Abstract

fetched live from OpenAlex

This paper examines the time-harmonic eigenstrain behavior of a magnetoelastic sandwich cylinder with solid and cellular material layers. A sandwich panel subjected to an external magnetic field is assumed to endure an eigenstrain with a cubic polynomial radial distribution in the sandwich core. Using asymptotic homogenization, the effective material properties of the cellular material are determined as a function of relative density for various cell topologies, that are used in the cellular layers of a cylinder under given magnetic field. Bessel, Struve, and Lommel functions are used to obtain semi-analytic solutions for a sandwich cylinder with perfectly and imperfectly bonded interfaces. The results are first verified with those available in the literature of composite cylinders with solid material layers. Then the paper studies the role that cell topology, relative density, and bonding type at the layer interfaces play on the time-harmonic magnetoelastic responses. The numerical results reveal that the proper choice of relative density, cell topology, and cellular layer configuration can reduce the weight and stress regime, as well as improve the dynamic response of a sandwich cylinder subjected to a given magnetic field.

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 categoriesInsufficient payload (model declined to judge)
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 score0.935
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.004
GPT teacher head0.175
Teacher spread0.171 · 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.

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
Published2014
Admission routes1
Has abstractyes

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