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Fluid planetary dynamics in Jupiter and Saturn

2020· preprint· fa· W4238476665 on OpenAlexaffabout
Simranjeet Singh

Bibliographic record

Venuenot available
Typepreprint
Languagefa
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAdenylate kinaseAdenine nucleotideAmpereAMP deaminaseChemistryEndocrinologyInternal medicineBiologyNucleotideBiochemistryEnzymeMedicinePhysics

Abstract

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Title: Fluid planetary dynamics in Jupiter and Saturn Simranjeet Singh*, Dr. Moritz Heimpel University of Alberta, Edmonton, AB T6G 2R3, Canada Abstract: Despite around numerous years of observations in jets and vortices in the atmosphere of giant planets like Jupiter and Saturn are still debatable. Bands of Jupiter consists of powerful winds. High latitude jets flow in eastward direction. The equatorial flow model has been shown here but not the shallow layered model due to more focused on the radial flow field of Jupiter and Saturn. Our findings are based on the interpretation of output data provided by Juno and Cassini spacecrafts. We used MagIC code to run the planetary models with the solutions of Navier-Stokes equations coupled with anelastic and Boussinesq approximations. To analyse the fluid interaction between zonal flows in Saturn and Jupiter and the result of the circulation models of these planets dominated by zonal flow. This manifest itself as a pattern similar to the Jupiter’s band. But Saturn has somewhat a different storyline as near to the equator, Saturn’s bands gradually gets wider. By making use of the conservation of momentum (Navier-Stokes) (MagIC): ρ((∂u/∂t) + u.∇u) = -∇p + (1/µ) + (∇xB)xB + ρg - 2ρxu + ∇.S U and B are the respective velocity and magnetic fields in the system, p is the pressure (includes centrifugal forces) and S signifies the rate of strain tensor. The above equations set the base line for the flow field. These flow fields can be read by the generation of output files, which involves the overall scenario of the planets, which includes Kinetic energy of the system varying with time, exchange of magnetic flux, dipole movement, heat exchange with Nusselt number, angular momentum conservation of inner core and mantle and many other parameters, which demonstrate the precise picture of belts in Jupiter and Saturn. Since the output is in terms of non-dimentional quantity, which makes easy to visualise the results for the giant planets. Results are dynamic and so are the interpretation when Ra varies with the factor of two. To conclude here, although there is loads to describe but precisely the flow of our numerical simulation is indeed dominated by geostrophic zonal flows. The flow is directly proportional to the radial symmetry, as it grows and diminishes accordingly. The effect of these codes suggest that zonal flow scaling can be analysed separately with high degree of magnitude precision inside and outside the tangential cylinder. The jet streams in these giant planets are insensitive and will continue to remain similar over next few centuries, according to this research. Important References (limited to space): Effects of compressibility on driving zonal flow in gas giants, T. Gastine, J. Wicht (2011). Simulation of equatorial and high-latitude jets on Jupiter in a deep convection model, Moritz Heimpel (2005). Simulation of deep-seated zonal jets and shallow vortices in gas giant atmospheres, Moritz Heimpel (2015). Zonal flow scaling in rapidly-rotating compressible convection, T. Gastine (2013). Anelastic convection-driven dynamo benchmarks, Jones (2011)

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.200
Teacher spread0.190 · 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 designTheoretical or conceptual
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

Citations0
Published2020
Admission routes2
Has abstractyes

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