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Record W4401010419 · doi:10.1016/j.prostr.2024.06.013

Finite Element Simulation of Crack Propagation in Ice Floes

2024· article· en· W4401010419 on OpenAlexafffund
Igor Gribanov, Ahmed Elruby, Rocky Taylor

Bibliographic record

VenueProcedia Structural Integrity · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsTraction (geology)Finite element methodFracture mechanicsDegree RankineFracture (geology)Ultimate tensile strengthStructural engineeringMaterials scienceMechanicsEngineeringComposite materialPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

A method for modeling fracture in stiff plates of uniform thickness is presented. The Mindlin-Reissner plate theory within the framework of the finite element method is utilized. The fracture criterion is based on the Rankine theory, in which a crack is initiated when normal traction at a node exceeds a given tensile strength. The traction is calculated as a path integral around a crack tip or a tentative split. The propagation direction is such that the normal traction at the crack tip is maximized. A time-based criterion for crack initiation and propagation is added to the model, which yields better correspondence with the experimentally observed fracture patterns. The proposed methodology was implemented in an in-house code. Initial validation shows excellent agreement between the proposed methodology's predictions and the realistic fracture patterns of ice floes.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.266
Teacher spread0.248 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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