Instabilities and Turbulence in Two-Dimensional Magnetohydrodynamics
Bibliographic record
Abstract
The solar tachocline is a dynamically important thin region in the Sun, located between the convective and radiative zones and characterised by strong shear in both the radial and latitudinal directions. Furthermore, it is believed to play a key role in the solar dynamo process through the shearing of a poloidal field into a stronger toroidal component. Motivated by the dynamics of the tachocline we have conducted a detailed numerical exploration of the dynamics of sheared MHD turbulence. Specifically, we have implemented a parallelized numerical model using the "shenfun" Python library to solve the nonlinear two-dimensional Magnetohydrodynamic (MHD) equations to study the dynamics of unstable jets and turbulence in astrophysical plasmas. In particular, we study details of how the jet becomes unstable and the resulting cascade of energy in the case of MHD turbulence. In addition to studying the evolution of the physical quantities, we also investigate the evolution of the spectral slopes and spectral fluxes. As has been found in previous studies of MHD turbulence, a very weak large-scale magnetic field can play a key dynamical role through its amplification on small scales. For extremely weak fields, the behaviour is essentially hydrodynamic. However, once the field is dynamic, the nature of the resulting MHD solution is very different. We are able to classify the various flows and quantify the nature of the solutions in the two regimes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".