MétaCan
Menu
Back to cohort
Record W4311046201 · doi:10.1002/cjce.24789

The velocity field characteristics of high‐viscosity fluids falling film flow down clamped channels

2022· article· en· W4311046201 on OpenAlexvenueno aff
Yongjun Wang, Chen Su, Shichang Chen, Xianming Zhang, Wenxing Chen

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMechanicsFalling (accident)ViscosityMaterials scienceSurface tensionFlow (mathematics)Flow velocityField (mathematics)Fictitious forceComposite materialPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract The complexity of falling film flow has been studied in many industrial applications. In this work, the velocity field of high‐viscosity fluids falling film flow down clamped channels was investigated numerically and experimentally. The results show that the numerical simulation results are consistent with the experimental results, and the characteristics of the velocity field are related to the fluid properties, operating conditions, and structure of the clamped channels. When the fluid viscosity is greater than or equal to 10 Pa ⋅ s, the type of velocity field changes into I shape, U shape, and V shape. While the fluid viscosity drops to 0.89 Pa ⋅ s, the viscous force cannot resist the inertial force and gravity, resulting in a cardioid velocity field. By adjusting the structure of the clamped channels and operating conditions, the tension of the liquid film can be changed, and the velocity distribution of the liquid film can be manipulated. Significantly, under the fluctuating curtain flow, the liquid film coalesces and breaks frequently, which enlarges the surface area of the liquid film and strengthens the surface renewal frequency. Hence, this form of falling film flow can be applied to process intensification of high‐viscosity materials.

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)0.000
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 score0.034
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.161
Teacher spread0.156 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicFluid Dynamics and Thin FilmsFrench-language works237,207