The Impact of Collisions on Heat Transfer in a Particle-Laden Shearless Turbulent Flow
Bibliographic record
Abstract
mental investigation for decades. On the other hand, particleparticle collisions play a significant role in particulate turbulent flows even in relatively diluted suspensions. The effect of collision has been under investigation since the state-of-theart work of Saffman For instance, collisions between water droplets in clouds are a necessary condition for precipitation formation from cloud droplets and ice crystals, while, particle-particle collisions have a profound impact on the onset and evolution of sandstorms In these processes, the background turbulence of the carrier flow favors inter-particle collisions. The mechanisms of the collision rate enhancement by background turbulence have only become clear in the past few years, and the underlying physics is currently qualitatively well understood, although quantifying the rate of small particles collisions suspended in a turbulent flow may require more advancement. As a pioneering work on the collision effect in particle-laden turbulent flows, Saffman et al., developed the theory of collision of water droplet in cloud physics and they could formulate the droplet collision rates for identical small low-inertial droplets in terms of droplet dimension and turbulence characteristics (the rate of turbulent kinetic energy dissipation and the kinematic viscosity of fluid ). Their findings suggested that the collision frequency of the small droplet suspended in clouds is independent of droplet properties However, in the subsequent works like the work of Sundaram et al., it was found that droplet properties also influence the collision rate. The results of Sundaram et al., showed that particle parameters such as particle response time, number density and size can impact collision frequency as well as background turbulence. They showed the significant dependency of the collision rate on the droplet Stokes number There have also been detailed theoretical investigations of the collision rate, a particularly effective description of the collisionrate enhancement in terms of a stochastic model for the prob-Abstract -In this research, we undertake an investigation of a turbulent flow seeded with heavy inertial particles, employing Eulerian-Lagrangian point-particle direct numerical simulations in the twoway coupling regime. The primary objective of our investigation is to assess the influence of inter-particle collisions on heat transfer within the time-evolving thermal mixing layer that develops between two regions with distinct temperatures in a homogeneous and isotropic turbulent flow. Our findings encompass a range of Stokes numbers spanning from 0.2 to 3, while maintaining a thermal Stokes number to Stokes number ratio of 4.43, at a Taylor microscale Reynolds number up to 124. Our results reveal that particle collisions tend to diminish the correlation between particle temperature and velocity, consequently leading to a marginal reduction in the average heat transfer when compared to a collisionless regime at higher Stokes numbers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".