3D hydrodynamic simulations of C ingestion into a convective O shell
Bibliographic record
Abstract
Interactions between convective shells in evolved massive stars have been linked to supernova impostors, to the production of the odd-Z elements Cl, K, and Sc, and they might also help generate the large-scale asphericities that are known to facilitate shock revival in supernova explosion models. We investigate the process of ingestion of C-shell material into a convective O-burning shell, including the hydrodynamic feedback from the nuclear burning of the ingested material. Our 3D hydrodynamic simulations span almost 3 dex in the total luminosity $L_\mathrm{tot}$. All but one of the simulations reach a quasi-stationary state with the entrainment rate and convective velocity proportional to $L_\mathrm{tot}$ and $L_\mathrm{tot}^{1/3}$, respectively. Carbon burning provides $14\,$--$\,33\%$ of the total luminosity, depending on the set of reactions considered. Equivalent simulations done on $768^3$ and $1152^3$ grids are in excellent quantitative agreement. The flow is dominated by a few large-scale convective cells. An instability leading to large-scale oscillations with Mach numbers in excess of $0.2$ develops in an experimental run with the energy yield from C burning increased by a factor of 10. This run represents most closely the conditions expected in a violent O-C shell merger, which is a potential production site for odd-Z elements such as K and Sc and which may seed asymmetries in the supernova progenitor. 1D simulations may underestimate the energy generation from the burning of ingested material by as much as a factor two owing to their missing the effect of clumpiness of entrained material on the nuclear reaction rate.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".