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Record W4241056964 · doi:10.1515/ijnsns-2011-119

On the Energy of Dynamic Fractures

2012· article· en· W4241056964 on OpenAlexaff
Kaiwen Xia, Cangli Liu, Patrick Kanopoulos

Bibliographic record

VenueInternational Journal of Nonlinear Sciences and Numerical Simulation · 2012
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFracture mechanicsHyperelastic materialConstant (computer programming)Fracture (geology)Materials scienceMechanicsWork (physics)Dynamic loadingComposite materialStructural engineeringComputer sciencePhysicsThermodynamicsEngineeringFinite element method

Abstract

fetched live from OpenAlex

Abstract The validity of the constant propagation fracture energy postulation for dynamic fracture is discussed. As shown from existing direct and indirect experimental results, this assumption may not represent the physical reality. For spontaneous fractures, the fracture energy was shown to increase linearly with the crack length, and for dynamic fractures driven by known amplitude impulsive loading (generated by planar impact), the fracture energy was not a constant either. Despite of its phenomenogical origin, the Broberg’s theory developed for self-similar crack growth works well for both spontaneous fractures and dynamic fractures produced by well defined dynamic loading. In this theory, the fracture energy is not a constant. Furthermore, with given far-field loading or equivalent far-field loading, the crack speed is uniquely determined by a strength-like material parameter. This parameter is related to the cohesive strength as proposed by H. J. Gao for hyperelastic materials in the crack-tip process zone. It is proposed in this work that the strength-like parameter (or equivalently the constant fracture speed) is a better material parameter to describe the dynamic fracture propagation process for most dynamic fractures.

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)0.002
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.297
Teacher spread0.281 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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