The physics of multiphase gas flows: fragmentation of a radiatively cooling gas cloud in a hot wind
Bibliographic record
Abstract
Galactic winds exhibit a multiphase structure that consists of hot-diffuse and cold-dense phases. Here we present high-resolution idealized simulations of the interaction of a hot supersonic wind with a cold cloud with the moving-mesh code arepo in setups with and without radiative cooling. We demonstrate that cooling causes clouds with sizes larger than the cooling length to fragment in 2D and 3D simulations. We confirm earlier 2D simulations by McCourt et al. (2018) and highlight differences of the shattering processes of 3D clouds that are exposed to a hot wind. The fragmentation process is quantified with a friends-of-friends analysis of shattered cloudlets and density power spectra. Those show that radiative cooling causes the power spectral index to gradually increase when the initial cloud radius is larger than the cooling length and with increasing time until the cloud is fully dissolved in the hot wind. A resolution of around 1 pc is required to reveal the effect of cooling-induced fragmentation of a 100 pc outflowing cloud. Thus, state-of-the-art cosmological zoom simulations of the circumgalactic medium fall short by orders of magnitudes from resolving this fragmentation process. This physics is, however, necessary to reliably model observed column densities and covering fractions of Lyman α haloes, high-velocity clouds, and broad-line regions of active galactic nuclei.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".