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Record W2511100296 · doi:10.1002/cjce.22647

Towards a unified approach for modelling uniform and non‐uniform bubbly flows

2016· article· en· W2511100296 on OpenAlexvenueno aff
Thomas Ziegenhein, Roland Rzehak, Tian Ma, Dirk Lucas

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersHelmholtz-Gemeinschaft
KeywordsTurbulenceBubbleClosure (psychology)MechanicsScale (ratio)Flow (mathematics)Euler's formulaStatistical physicsWork (physics)Set (abstract data type)Closure problemPhysicsK-epsilon turbulence modelMathematicsComputer scienceThermodynamicsMathematical analysis

Abstract

fetched live from OpenAlex

Abstract An important ingredient of closure relations for the Euler‐Euler two‐fluid model is the description of turbulent fluctuations. Models proposed in the literature disagree concerning the treatment of such on all scales. The large‐scale fluctuation structures as well as the bubble‐induced turbulence might be neglected, resolved, and/or modelled respectively in different ways. Each treatment has been demonstrated to work for a certain application but a unified perspective is lacking so far. To this end a set of closure relations for the fluid dynamics of bubbly flow has been collected that represents the best available knowledge and may serve as a baseline for further improvements and extensions. This model comprises a set of bubble forces as well as a turbulence model including turbulence modification due to the bubbles and has been successfully validated for bubbly flows in pipes and bubble columns. Here it is applied to two sets of data representing non‐uniform and uniform flows in bubble columns which are dominated by large‐scale fluctuations and bubble‐induced turbulence respectively.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.169
Teacher spread0.159 · 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 source (direct Gemma or distilled Codex), 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

Citations57
Published2016
Admission routes1
Has abstractyes

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