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Record W4399365860 · doi:10.1016/j.jnnfm.2024.105264

Turbulent impingement jet cleaning of thick viscoplastic layers

2024· article· en· W4399365860 on OpenAlexafffund
Hossein Hassanzadeh, D.I. Wilson, I.A. Frigaard, Seyed Mohammad Taghavi

Bibliographic record

VenueJournal of Non-Newtonian Fluid Mechanics · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsUniversity of British ColumbiaUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaPetroleum Technology Alliance CanadaUniversité Laval
KeywordsViscoplasticityTurbulenceJet (fluid)Materials scienceMechanicsPhysicsThermodynamicsFinite element method

Abstract

fetched live from OpenAlex

An experimental study is conducted on the use of a normally impinging turbulent water jet (with the Reynolds number of Re≈11800), for cleaning thick layers of a Newtonian fluid and two viscoplastic fluids (i.e., transparent Carbopol solutions). The layer thickness is larger than the jet radius. Non-intrusive techniques are used to track the geometrical features of the cleaning process in real time. The effects of layer thickness and fluid yield stress on removal behavior, including cleaning radius, cavity radius, and angle, are investigated. A yield stress promotes the initial formation of a blister rather than a cavity, and the rate of removal decreases with increasing layer thickness and yield stress. A relation is presented for the growth of the cavity radius, which fits our experimental observations well. A comparative analysis of submerged and impinging jets reveals, for the first time, the role of air entrainment in the process, with bubble characteristics such as trajectory, size distribution (diameter), and velocity being determined by the yield stress.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.230
Teacher spread0.221 · 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.

Study designBench or experimental
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

Citations8
Published2024
Admission routes2
Has abstractyes

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