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Large time step numerical modelling of the flow of Maxwell materials

2005· article· en· W2118987944 on OpenAlexaff
R. C. Bailey

Bibliographic record

VenueGeophysical Journal International · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsViscoelasticityMaxwell's equationsMechanicsGeologyClassical mechanicsPhysics

Abstract

fetched live from OpenAlex

A R Y Maxwell viscoelastic materials are commonly simulated numerically in order to model the stresses and deformations associated with large-scale earth processes, such as mantle convection or crustal deformation. Both implicit and explicit time-marching methods require that the time steps used be small compared with the Maxwell relaxation time if accurate solutions are to be obtained. For crustal tectonic modelling, where Maxwell times in a ductile lower crust may be of order of a decade or less, the large number of time steps required to model processes lasting many millions of years imposes a huge computational burden. This burden is avoidable. In this paper I show that, with the appropriate formulation of the problem, time steps may be taken which are much larger than the Maxwell time without loss of accuracy, as long as they are not large compared with the times over which strain rates vary significantly ('tectonic' timescales) in the model. The method relies on explicit analytic integration of the Maxwell constitutive relation for the stress over time intervals, which may be longer than the relaxation time as long as they are short compared with the timescale over which crustal stresses and geometries change. The validity of the formulation is also demonstrated numerically by comparison with the analytic solutions for three simple plane-strain models: extension of a uniform block, shear of a composite layer and development of a Rayleigh-Taylor instability.

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 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 score0.063
Threshold uncertainty score0.996

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)0.0050.000

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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.

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

Citations14
Published2005
Admission routes1
Has abstractyes

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