Magneto-Rayleigh–Taylor instability and feedthrough in a resistive liquid-metal liner of finite thickness
Bibliographic record
Abstract
The effect of magnetic tension and diffusion on the perturbation growth of a liquid-metal liner subjected to magneto-Rayleigh–Taylor (MRT) instability is investigated. An initially magnetic-field-free liquid-metal slab of finite thickness is surrounded by two lower-density regions. Within the lower region, a constant axial magnetic field of arbitrary magnitude is applied. The numerical examination of the MRT instability growth, initiated by a seeded perturbation parallel to the magnetic field at the liner's unstable interface, is performed for both perfectly conductive and resistive liners. To this end, a novel level set-based two-phase incompressible solver for ideal/resistive magnetohydrodynamic (MHD) flows within the finite-difference framework is introduced. Utilizing the implemented numerical toolkit, the impact of different Alfvén numbers and magnetic Reynolds numbers on the MRT growth rate and feedthrough at the upper interface of the liner is studied. Accounting for the finite resistivity of the liner results in an increase in the MRT growth and feedthrough compared to the ideal MHD case. The results indicate that magnetic diffusion primarily affects the MRT growth rate for higher wavenumbers, while for smaller wavenumbers, the effect of finite resistivity is only observed over a longer duration of instability development. We further demonstrate that decreasing the Alfvén number results in faster emergence of the magnetic diffusion effect on the MRT growth rate. It is also observed that a greater electrical conductivity jump across the liner results in an increased perturbation growth. Finally, the impact of surface tension on MRT instability growth for both ideal and resistive MHD cases is studied across different wavenumbers, specifically for Bond numbers related to fusion applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".