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Record W2901852109 · doi:10.5194/tc-2018-192

Modeling Sea Ice fracture at very high resolution with VP rheologies

2018· article· en· W2901852109 on OpenAlexaff
Damien Ringeisen, Nils Hutter, Martin Lösch, Bruno Tremblay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsMcGill University
FundersDivision of Mathematical SciencesEngineering and Physical Sciences Research CouncilDeutsche ForschungsgemeinschaftIsaac Newton Institute for Mathematical Sciences
KeywordsGeologyDilatantShear (geology)Overburden pressureFracture (geology)Sea iceGeometryMechanicsGeotechnical engineeringPhysicsMathematicsPetrology

Abstract

fetched live from OpenAlex

Abstract. Recent high resolution pan-Arctic sea ice simulations show fracture patterns (Linear Kinematic Features – LKFs) that are typical of granular materials but with intersection (fracture) angles wider than those observed from high-resolution satellite images (with a modal value of θ = 20°). In this article, We investigate the mechanism of formation and parameter dependencies of ice fracture in simple numerical bi-axial test on a 8 km x 25 km ice floe at an unprecedented resolution of 25m for two different yield curves: an elliptical (VP) and a Coulombic yield curve both with normal flow rule. In the standardVP model, the simulated angle of fracture is θ = 33.9°, compared to 20° in observations. The dependence of the angle of fracture on the ice shear strength is also contrary to that of typical granular materials with larger angle of fracture for higher shear strength – think of a wet sand castle with steeper walls than a dry sand castle. In this model, the divergence along the fracture lines (or LKFs) is entirely dictated by the ice shear strength used in the model with high shear strength resulting in convergence along LKFs and low shear strength resulting in divergence along LKFs. This is again contrary to typical granular materials where divergence (or dilation) is linked with the orientation of contacts normals that oppose the flow with divergence present for larger shear resistance and convergence for lower shear resistance. Moreover, the angle of fracture depends on the confining pressure in the uni-axial test with more convergence as the confining pressure increases, again contrary to granular material that have an angle of fracture that is independent of the confining pressure. We note that all three behaviors of the VP model are linked with the use of an associative (normal) flow rule. In the Coulombic model, the angle of fracture is smaller (θ = 23.5°), but the solution is unstable when the compressive stresses are too large because of the discontinuity between the straight limbs of the yield curve and the elliptical capping. Our results show that while the VP model gives angles of fracture that are visually correct, the bias in the magnitude of the angle of fracture and the physical dependencies of the angle of fracture on mechanical strength parameters and stress fields couple the sea ice mechanical strength parameters, the sea-ice drift, sea-ice deformation (strain-rate) field in an inconsistent way. We consider this evidence to move away from the elliptical yield curve and associative (normal) flow rule, a deformation law that is not applicable to pressure-sensitive and dilatant granular material such as sea ice.

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 score0.012
Threshold uncertainty score0.025

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.196
Teacher spread0.185 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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