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Record W2618688213 · doi:10.3847/1538-4357/aa6d60

Magnetically Modulated Heat Transport in a Global Simulation of Solar Magneto-convection

2017· article· en· W2618688213 on OpenAlexaff
Jean-François Cossette, Paul Charbonneau, Mark Rast

Bibliographic record

VenueThe Astrophysical Journal · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPhysicsConvectionMagnetoConvection zoneMagnetohydrodynamicsAstrophysicsAstronomyMagnetic fieldComputational physicsMechanicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract We present results from a global MHD simulation of solar convection in which the heat transported by convective flows varies in-phase with the total magnetic energy. The purely random initial magnetic field specified in this experiment develops into a well-organized large-scale antisymmetric component undergoing hemispherically synchronized polarity reversals on a 40 year period. A key feature of the simulation is the use of a Newtonian cooling term in the entropy equation to maintain a convectively unstable stratification and drive convection, as opposed to the specification of heating and cooling terms at the bottom and top boundaries. When taken together, the solar-like magnetic cycle and the convective heat flux signature suggest that a cyclic modulation of the large-scale heat-carrying convective flows could be operating inside the real Sun. We carry out an analysis of the entropy and momentum equations to uncover the physical mechanism responsible for the enhanced heat transport. The analysis suggests that the modulation is caused by a magnetic tension imbalance inside upflows and downflows, which perturbs their respective contributions to heat transport in such a way as to enhance the total convective heat flux at cycle maximum. Potential consequences of the heat transport modulation for solar irradiance variability are briefly discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.375

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 head0.007
GPT teacher head0.243
Teacher spread0.237 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

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