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Record W2025018755 · doi:10.1002/cjce.5450820502

Liquid Holdup and Pressure Drop Determination in Structured Packing with CFD Simulations

2004· article· en· W2025018755 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsPressure dropComputational fluid dynamicsTurbulenceMechanicsDrop (telecommunication)Materials scienceLiquid dropFluid dynamicsThermodynamicsMechanical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract It is discussed how Computational Fluid Dynamics (CFD) can be used for hydrodynamics calculations within structured packings. Three dimensional (3D) simulations, in which the influence of mesh size and turbulence models were tested, have been carried out for dry pressure drop determination. Two dimensional (2D) Volume of Fluid gas‐liquid flow simulations have been used for liquid holdup determination. Numerical results are compared to tomographic liquid holdup measurements. Then, a comparison with data from a gas treatment pilot plant shows that wet pressure drop can be predicted by combining these two types of information (dry pressure drop and liquid holdup).

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.

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 score0.084
Threshold uncertainty score0.230

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 head0.004
GPT teacher head0.176
Teacher spread0.172 · 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