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Record W2124407516 · doi:10.1177/8756087907087466

A Study of Heat Transfer in the Blown Film Process

2007· article· en· W2124407516 on OpenAlexaff
Z. Zhang, Pierre G. Lafleur

Bibliographic record

VenueJournal of Plastic Film & Sheeting · 2007
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMechanicsHeat transferMaterials scienceHeat transfer coefficientAirflowTurbulenceFluentBody orificeFilm temperatureThermodynamicsBubbleAerodynamicsReynolds numberComputational fluid dynamicsMechanical engineeringPhysicsNusselt numberEngineering

Abstract

fetched live from OpenAlex

The air ring design directly affects the aerodynamic phenomena in the film blowing process. In this study, the effects of impinging jets on heat transfer are explored taking into account the influence of cooling on the bubble shape, by comparing the characteristics and performance of flows produced by single and dual orifice air rings. The aerodynamic characteristics of air-cooling flow around the bubble surface are investigated utilizing a finite volume numerical method and renormalization group (RNG) theory based on the k—ε turbulence model coupled with enhanced wall treatment method of the FLUENT commercially available software. The numerical analysis provided a detailed description of fluid flow pattern as well as heat transfer coefficients for different air ring designs, and under different processing conditions. The results of calculation indicate that the heat transfer rate critically depends on the air ring design. A simple correlation for the heat transfer coefficient and the maximum air velocity function (i.e., h = aV b max proposed in the literature) was established. The boundary conditions dominated by airflow rates are equally important for cooling efficiency. The correlation between Reynolds number and the heat transfer in the numerical solution is also reported.

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.

How this classification was reachedexpand

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 score0.138
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Plastic Film & SheetingSame topicFluid Dynamics and Turbulent FlowsFrench-language works237,207