Heat Transport and Convective Velocities in Compositionally Driven Convection in Neutron Star and White Dwarf Interiors
Bibliographic record
Abstract
Abstract We investigate heat transport associated with compositionally driven convection driven by crystallization at the ocean–crust interface in accreting neutron stars, or growth of the solid core in cooling white dwarfs. We study the effect of thermal diffusion and rapid rotation on the convective heat transport, using both mixing length theory and numerical simulations of Boussinesq convection. We determine the heat flux, composition gradient, and Péclet number, Pe (the ratio of thermal diffusion time to convective turnover time) as a function of the composition flux. We find two regimes of convection with a rapid transition between them as the composition flux increases. At small Pe, the ratio between the heat flux and composition flux is independent of Pe, because the loss of heat from convecting fluid elements due to thermal diffusion is offset by the smaller composition gradient needed to overcome the reduced thermal buoyancy. At large Pe, the temperature gradient approaches the adiabatic gradient, saturating the heat flux. We discuss the implications for cooling of neutron stars and white dwarfs. Convection in neutron stars spans both regimes. We find rapid mixing of neutron star oceans, with a convective turnover time of the order of weeks to minutes depending on rotation. Except during the early stages of core crystallization, white dwarf convection is in the thermal-diffusion-dominated fingering regime. We find convective velocities much smaller than recent estimates for crystallization-driven dynamos. The small fraction of energy carried as kinetic energy calls into question the effectiveness of crystallization-driven dynamos as an explanation for observed magnetic fields in white dwarfs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".