Turbulent mixing and nuclear burning in stellar interiors
Bibliographic record
Abstract
The turbulent burning of nuclei is a common phenomenon in the evolution of stars. Here we examine a challenging case: the merging of the neon and oxygen burning shells in a 23 M⊙ star. A previously unknown quasi-steady state is established by the interplay between mixing, turbulent transport, and nuclear burning. The resulting stellar structure has two burning shells within a single convection zone. We find that the new neon burning layer covers an extended region of the convection zone, with the burning peak occurring substantially below where the average convective turnover timescale becomes equal to the burning timescale. These characteristics differ from those predicted by 1D stellar evolution models of similar ingestion events. We develop the mean-field turbulence equations that govern compositional evolution, and use them to interpret our data set. An important byproduct is a means to quantify sub-grid-scale effects intrinsic to the numerical hydrodynamic scheme. For implicit large eddy simulations, the analysis method is particularly powerful because it can reveal where and how simulated flows are modified by resolution, and provide straightforward physical interpretations of the effects of dissipation or induced transport. Focusing on the mean-field composition variance equations for our analysis, we recover a Kolmogorov rate of turbulent dissipation without it being imposed, in agreement with previous results which used the turbulent kinetic energy equation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".