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Record W4388470014 · doi:10.11159/jffhmt.2023.018

The Impact of Collisions on Heat Transfer in a Particle-Laden Shearless Turbulent Flow

2023· article· en· W4388470014 on OpenAlex
Hamid Reza Zandi Pour, Michele Iovieno

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
Fundersnot available
KeywordsTurbulenceMechanicsHeat transferParticle flowFlow (mathematics)Environmental scienceParticle (ecology)PhysicsAtmospheric sciencesStatistical physicsMeteorologyGeology

Abstract

fetched live from OpenAlex

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 [1].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 [2].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 [1].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 [3].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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.255
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it