A comparison of two-fluid and one-fluid dust solvers for dusty, supersonic turbulence
Bibliographic record
Abstract
Dust is an important observational tracer of gas in molecular clouds and different dust solvers have led to conflicting conclusions about dust distributions. For this reason, We model 3 and 10 μm dust grains in supersonic, turbulent molecular cloud conditions to compare two different numerical methods solving dust coupled to gas through a drag term. One method models dust and gas as two separate species (two-fluid) and the other the combination of dust and gas as a single mixture (onefluid). Simulations are performed in a 3D periodic box using the Phantom code. The gas probability distributions are consistent with a log-normal distribution for both methods and grain sizes. The dust distributions are different in the two methods, showing discrepancies especially in low densities. The most significant difference between the two methods is in the dust-to-gas ratio distributions. Both methods peak at the mean dust-to-gas ratio of 0.01, but the two-fluid method has wider distributions than the one-fluid method suggesting excess dust concentration in dense filaments. Filaments are where the one-fluid method results are most accurate, but the narrowing of the distribution is also caused by the one-fluid limiter used to maintain the terminal velocity approximation. This artificially makes the mixture more coupled in lowdensity regions. Our overall conclusion is that both methods are viable for the study of dust in molecular clouds, but that the correct method should be chosen based on the Stokes number regime of the calculation to avoid numerical artefacts.
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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 | 0.001 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".