Linear Discontinuity Sharpening for Highly Resolved and Robust Magnetohydrodynamics Simulations
Bibliographic record
Abstract
ABSTRACT This study applies a reconstruction scheme, “hybrid MUSCL–THINC” for finite volume methods developed by Chiu et al., to magnetohydrodynamics (MHD) simulations. The scheme is a hybrid of monotone upstream‐centered schemes for conservation law (MUSCL) and a tangent of hyperbola interface capturing (THINC) scheme. THINC sharply captures discontinuous distributions of physical quantities by using a hyperbolic tangent function. Our investigation reveals that hybrid MUSCL–THINC is more oscillatory in MHD simulations than in gas dynamics simulations, owing to the greater number of physical variables and associated complex waves in MHD. Analytical results demonstrate that artificial compression by THINC is excessive for MHD shock waves, whereas it is effective for linear discontinuities, such as contact discontinuities. Therefore, we propose a modification in which the artificial compression by THINC is weakened in the vicinity of nonlinear discontinuities and applied only to linear regions. The new scheme is tested using one‐ and two‐dimensional MHD problems, and the results demonstrate that the scheme sharply captures linear discontinuities while avoiding numerical oscillations due to excessive artificial compression.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".