Viscous capturing of heated particles in one- and two-way coupledturbulence
Bibliographic record
Abstract
Inertial particles are known to form clusters in turbulence. In this regard, the previous literature employs a dimensionless parameter, Stokes number (St), for characterizing particle clustering. However, it was recently found that in heated particle-laden flows, temperature-driven fluid viscosity causes the particle clustering to depart from their St based behaviour, and deliver higher clustering than the classical predictions. This is due to a phenomenon called viscous capturing (VC), where particles locally get captured by viscous gas clouds upon heating which results in greater clustering. The aim of this study is to use Direct Numerical Simulations (DNS) for testing VC in one-way coupling (OWC) and two-way coupling (TWC) in momentum, to understand the validity of VC. It is found that the VC holds well even in TWC, where particle are capable of resisting VC. Hence, VC should be considered an important parameter in clustering sensitive thermal applications, along with St.
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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 | 0.001 | 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".