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Record W6911748517 · doi:10.5281/zenodo.14532422

Modelling of ice crushing with Material Point Method

2024· other· en· W6911748517 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typeother
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsNational Research Council CanadaCentre For Cold Ocean Resources EngineeringMemorial University of Newfoundland
Fundersnot available
KeywordsMaterial point methodDeformation (meteorology)SubseaKeelPoint (geometry)Constitutive equationMaterial propertiesProcess (computing)

Abstract

fetched live from OpenAlex

AbstractIce crushing has been a challenging process to model because of the large number of particles and fragments, which present computational difficulty, as well as the non-trivial constitutive behavior of the material. The Material Point Method (MPM) is a promising modeling technique in situations with high deformations where the material transitions from solid to granular. A continuum damage MPM has been developed to model dynamic ice crushing under indentation. We reproduce the significant experimentally observed phenomena, such as the localized damage near the indenter, ejection of fine particles, spalls, and cracks in the solid part of the sample. Moreover, the modelling approach reproduces important features of ice-structure interaction, most notably the formation and evolution of high-pressure zones. Our approach can potentially contribute to assessing risks in ice-structure interaction, iceberg keel impacts on subsea pipelines, ice-pipe-soil interactions, and other applications.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.237
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.002

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 head0.027
GPT teacher head0.243
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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 routes1
Has abstractyes

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