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Record W1999252309 · doi:10.1002/zamm.201200105

Dynamic response of a cracked magnetoelectroelastic layer sandwiched between two elastic layers

2012· article· en· W1999252309 on OpenAlexaff
Keqiang Hu, Zengtao Chen

Bibliographic record

VenueZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik · 2012
Typearticle
Languageen
FieldEngineering
TopicNumerical methods in engineering
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsLaplace transformFredholm integral equationIntegral transformMaterials scienceIntegral equationMagnetic fieldStress intensity factorElectric fieldFourier transformMechanicsPlane (geometry)Electric displacement fieldSingular integralAntiplane shearMathematical analysisGeometryMathematicsFracture mechanicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract The dynamic response of a cracked magnetoelectroelastic layer sandwiched between dissimilar elastic layers under anti‐plane shear and in‐plane electric and magnetic impacts is investigated by the integral transform method. Fourier transforms and Laplace transforms are applied to reduce the mixed boundary value problem of the impermeable crack to simultaneous dual integral equations, which are then expressed in terms of simultaneous Fredholm integral equations of the second kind. The stress field, electric field and magnetic field near the crack tip are obtained asymptotically, and the corresponding field intensity factors are further determined. Numerical results show that the stress intensity factors are influenced by the material properties, the electric and magnetic loadings, and the geometry. The crack initiation can be enhanced or retarded depending on the electric and magnetic loading, and the crack may propagate along its original crack line when the criterion of maximum hoop stress is applied.

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 score0.001
Threshold uncertainty score0.004

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.280
Teacher spread0.266 · 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

Citations12
Published2012
Admission routes1
Has abstractyes

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