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Record W2111088832 · doi:10.1002/cjce.22183

3D CFD modelling and optimization of single‐phase flow in rotating packed beds

2015· article· en· W2111088832 on OpenAlexvenueno aff
Yucheng Yang, Yang Xiang, Yingang Li, Guang‐Wen Chu, Hai‐Kui Zou, Moses Arowo, Jian‐Feng Chen

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBaffleMechanicsPressure dropComputational fluid dynamicsFlow (mathematics)Packed bedPorosityInletCentrifugal forceTwo-phase flowWork (physics)Mass transferVolumetric flow rateFluid dynamicsRotational speedMixing (physics)Materials scienceThermodynamicsChemistryEngineeringMechanical engineeringPhysicsGeotechnical engineeringChromatography

Abstract

fetched live from OpenAlex

Rotating Packed Beds (RPBs) are novel reactors used for intensification of mass transfer and mixing since they provide adjustable centrifugal force to simulate high gravity. In this work, in order to analyze and optimize fluid flow in RPBs, a three‐dimensional single‐phase flow was simulated and validated with previous experimental data. The results show that pressure drop increases with an increasing gas flow rate and rotation speed, and reveal the distribution of total pressure and velocity magnitude. A RPB with radial gas inlet, one of the generic types of RPBs, which is widely applied in the chemical industry but has poor gas distribution on the surface of packing, was optimized using various baffles. The width, distance, shape, and opening porosity of the baffles were examined by adopting a criterion, and their effect on gas distribution was illustrated. Finally, a possible optimum structure of the RPB was formulated under optimal operational conditions.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.020
GPT teacher head0.199
Teacher spread0.179 · 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

Citations56
Published2015
Admission routes1
Has abstractyes

Explore more

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