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Record W2244961457

Explicit FEA and Constitutive Modelling of Damage and Fracture in Polycrystalline Ice - Simulations of Ice Loads on Offshore Structures

2005· article· en· W2244961457 on OpenAlexvenueaboutno aff
Ahmed Derradji-Aouat

Bibliographic record

VenueNPARC · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsConstitutive equationFinite element methodCrackingGeotechnical engineeringSubmarine pipelineStructural engineeringGeologyFracture (geology)MechanicsEngineeringMaterials sciencePhysicsComposite material
DOInot available

Abstract

fetched live from OpenAlex

In Finite Element Analyses (FEA) of ice interactions with offshore structures, the constitutive material model for the behaviour of ice becomes a critical factor to accurately calculate maximum ice loads. Cracking activity is an integral part of the interaction process and it can be modelled using a hybrid approach of constitutive modelling of ice behaviour and explicit numerical solution1. In this paper, a brief summary for the constitutive model, damage formulation, failure criterion, and numerical solution is presented. The subject of how both micro and macro cracks are modelled and used in the simulations of typical ice-structure interaction problems (and subsequently to calculate maximum ice loads) is discussed in the light of the results of two different examples. The 1st example is a numerical simulation of an ice sheet (100 by 60 by 0.5 m) impacting a large fixed concrete structure (120 by 40 by 40 m) in the Belle Isle Strait (BIS), Newfoundland, Canada. The 2nd second example, however, is a simulation of a cylindrical rigid indentor impacting an ice block (10 by 2 by 2 m) at high speed. The results from both examples are discussed in the light of the 'damage and fracture' formulation of the present constitutive model and failure criterion for ice. Conclusions and recommendations for future work are provided.

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 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 score0.028
Threshold uncertainty score0.305

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)0.0000.000

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 head0.016
GPT teacher head0.223
Teacher spread0.207 · 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 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

Citations4
Published2005
Admission routes2
Has abstractyes

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Same venueNPARCSame topicArctic and Antarctic ice dynamicsFrench-language works237,207