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Record W2103777478 · doi:10.1177/0021998302036015587

The Dynamic Stress Field in the Matrix Surrounding a Spheroidal Particle

2002· article· en· W2103777478 on OpenAlexaff
R. Paskaramoorthy, F Kienhöfer, S. A. Meguid

Bibliographic record

VenueJournal of Composite Materials · 2002
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceParticle (ecology)Composite materialMatrix (chemical analysis)Stress (linguistics)Stress fieldBrittlenessvon Mises yield criterionComposite numberEigenfunctionFinite element methodStructural engineeringEigenvalues and eigenvectorsPhysics

Abstract

fetched live from OpenAlex

In this study, the steady-state stresses resulting from a dynamic loading in a composite reinforced by a single spheroidal particle are determined and the stress concentration factors within the matrix obtained. A hybrid technique that combines the finite element method with an eigenfunction expansion technique is used to determine the stresses. The stress concentrations within the matrix of the composite are found to be dependent on the frequency of excitation, the mismatch of elastic properties and density between the particle and the matrix, the aspect-ratio of the particle and Poisson’s ratio of the particle and matrix. In particular, the study reveals that the matrix would experience dynamic stresses up to 100% greater than the static values when the particle density is greater than that of the matrix. The results also indicate that composites with brittle matrices will experience crack initiation at the pole of the particle where the principal stresses are the largest. For ductile matrices, on the other hand, the region of maximum von Mises equivalent stress within the matrix varied along the particle–matrix interface, but also occurred at interior points away from the interface under certain conditions.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.010
GPT teacher head0.235
Teacher spread0.226 · 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 designBench or experimental
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
Published2002
Admission routes1
Has abstractyes

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