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Record W2804053719 · doi:10.1029/2017je005497

The Influence of Curvature on Convection in a Temperature‐Dependent Viscosity Fluid: Implications for the 2‐D and 3‐D Modeling of Moons

2018· article· en· W2804053719 on OpenAlexafffund
Joshua Guerrero, J. P. Lowman, Frédéric Deschamps, Paul Tackley

Bibliographic record

VenueJournal of Geophysical Research Planets · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationCompute Canada
KeywordsConvectionCurvatureAnnulus (botany)Spherical shellRayleigh–Bénard convectionViscosityRayleigh numberShell (structure)PhysicsConvection cellMechanicsNewtonian fluidNatural convectionCombined forced and natural convectionGeometryThermodynamicsMaterials scienceMathematics

Abstract

fetched live from OpenAlex

Abstract Convection in terrestrial bodies occurs within spherical shells described by the ratio, f , of their bounding radii. Previous studies that have modeled convection with a temperature‐dependent viscosity noted the strong effect of f on transition to the stagnant‐lid regime. Here we analyze stagnant‐lid convection in 2‐D and 3‐D systems with curvatures including relatively small‐core shells ( f as small as 0.2) as well as in thin shell and plane‐layer cases. Several peculiarities of convection in a strongly temperature‐dependent viscosity fluid are identified for both high and low curvature systems. We demonstrate that effective Rayleigh numbers may differ by orders of magnitude in systems with different curvatures, when all other parameters are maintained at fixed values. Furthermore, as f is decreased, the nature of stagnant‐lid convection in small‐core bodies shows a divergence in the temperature and velocity fields found for 2‐D annulus and 3‐D spherical shell systems. In addition, substantial differences in the behavior of thin shell ( f = 0.9) and plane‐layer (Cartesian geometry) models occur in both 2‐D and 3‐D, indicating that the latter (emulating a toroidal topology rather than spherical) may be inappropriate approximations for modeling variable viscosity convection in thin spherical shells. Our findings are especially relevant to understanding and accurately modeling the thermal structure that may exist in bodies characterized by thin shells (e.g., f = 0.9) or relatively small cores, such as shells comprising the Galilean satellites and other moons.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.039
GPT teacher head0.350
Teacher spread0.311 · 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 designObservational
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

Citations22
Published2018
Admission routes2
Has abstractyes

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