MOGLI: model for multiphase gas using multifluid hydrodynamics
Bibliographic record
Abstract
ABSTRACT Multiphase gas, with hot ($\sim 10^6$ K) and cold ($\sim 10^4$ K) gas, is ubiquitous in astrophysical media across a wide range of scales. However, simulating multiphase gas has been a long-standing challenge, due to large separation between the size of cold gas structures and scales at which such gas impacts the evolution of associated systems. In this study, we introduce a new subgrid framework for such multiphase gas, MOGLI: model for multiphase Gas using multifluid hydrodynamics, in multifluid arepo . We develop this approach using theoretical results from previous studies with resolved small-scale simulations, leading to a minimal number of free parameters in the formulation. We divide the interactions in the model into three sources: drag, turbulent mixing, and cold gas growth. As part of the model, we also include two methods for estimating local turbulent velocities, one using the Kolmogorov scaling, and the other using local velocity gradients. We verify different components of the framework through extensive comparison with benchmark single-fluid simulations across different simulation parameters, such as how resolved the cold gas is initially, turbulent Mach number, spatial resolution, and random initialization of turbulence. We find a very good qualitative and quantitative agreement across different simulation parameters and diagnostics for both local turbulent velocity estimation methods. We also reproduce behaviour like the cold gas survival criteria as an emergent property. We discuss applications and possible extensions of MOGLI and demonstrate its capability by running a simulation which would be computationally prohibitive to run as a resolved single-fluid simulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".