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Record W2140300897 · doi:10.1002/cjce.20351

Numerical studies on effects of bubbles regular array on the liquid‐phase turbulence

2010· article· en· W2140300897 on OpenAlexvenueno aff
Mingjun Pang, Jinjia Wei, Bo Yu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsDragMechanicsTurbulencePhysicsAdded massBubbleClassical mechanicsReynolds numberTwo-phase flowTurbulence kinetic energyLift (data mining)Flow (mathematics)

Abstract

fetched live from OpenAlex

Abstract It is well known that additive drag‐reducing methods have broadly developing prospects, so studies on mechanisms of additive drag reduction are very necessary. We hypothesised that the main reason for bubbles induced drag reduction is the modification on liquid‐phase turbulence structure by the addition of bubbles, and therefore such modification is the focus of our investigation. In this paper, effects of bubbles on liquid‐phase turbulence under the circumstance of regular bubble array were investigated by using Euler–Lagrange two‐way numerical simulations. The liquid‐phase velocity field was solved by using direct numerical simulations (DNS) in Euler frame of reference, and the bubble motion was tracked by using Newtonian motion equations that took into account interaction forces including drag force, shear lift force, gravity force, buoyant force, and inertia force in Lagrange frame of reference. The coupling between the phases was realised by regarding the interphase forces as momentum source terms of the continuous phase. Similarities and differences for effects of bubbles and surfactants on liquid turbulent flows were also analysed. The study indicated that addition of bubbles enhances the mean streamwise velocity, greatly reduces the Reynolds stress, and shows anisotropic suppression to the velocity fluctuations. The interphase force has a great influence on budget of energy balance. It is a gain term near the wall and is a loss term in a wide range of the channel core.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.196
Teacher spread0.190 · 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 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

Citations4
Published2010
Admission routes1
Has abstractyes

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