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Record W2793222362 · doi:10.1002/cjce.23178

Modelling gas‐liquid mass transfer in a two‐phase jet flow

2018· article· en· W2793222362 on OpenAlexvenueno aff
Susanne Heithoff, Ulf Daniel Kück, Pascal Volkmer, Udo Fritsching, Norbert Räbiger

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsMass transferMass transfer coefficientMixing (physics)MechanicsJet (fluid)Flow (mathematics)ThermodynamicsChemistryPhysics

Abstract

fetched live from OpenAlex

ABSTRACT The efficiency and selectivity of chemical reactions are influenced by the mixing characteristic of the reactor. Existing models for reactor precalculation are often based on mean values, e.g. for the energy dissipation rate or the volumetric mass transfer coefficient, and assume a homogeneous mass transfer behaviour. As a result, process optimization by avoiding mass transfer limitations is not efficient, due to the missing knowledge of local limitations. Jet reactors typically provide inhomogeneous mixing and mass transfer behaviour, where the highest rates occur in the jet vicinity. Hence, the mass transfer processes in the jet flow, which cannot be properly characterized in gas‐liquid two‐phase flow, dominate the reactor performance. Experimental results concerning the volumetric mass transfer coefficient in the jet vicinity of the Jetzone‐Loopreactor are discussed, and a model approach for describing the phenomenon is presented. It is shown that the mass transfer rates strongly depend on the hydrodynamics of the loop flow. The model is adapted to the loop flow determining mechanisms, the drag coefficient, and the density difference between the circulation and the jet flow.

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 score0.164
Threshold uncertainty score0.538

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 science0.0000.000
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 head0.192
Teacher spread0.184 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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