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Record W2789783356 · doi:10.1139/cjp-2017-0914

Steady three-dimensional flows past hollow fiber membrane arrays – cross flow arrangement

2018· article· en· W2789783356 on OpenAlexvenueno aff
Mustafa Usta, Michael Morabito, Mohammed Alrehili, Alaa Hakim, Alparslan Öztekin

Bibliographic record

VenueCanadian Journal of Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMechanicsComputational fluid dynamicsHollow fiber membranePhysicsReynolds numberTurbulenceMembraneVolumetric flow rateFlow (mathematics)Work (physics)FiberMaterials scienceThermodynamicsComposite materialChemistry

Abstract

fetched live from OpenAlex

Hollow fiber membrane (HFM) modules are among the most common separation devices employed in membrane separation applications. Three-dimensional steady-state computational fluid dynamics (CFD) simulations are carried out to study flow past hollow fiber membrane banks (HFMB). The current study investigates the effects of flow behavior on membrane performance during binary mixture separations. Carbon dioxide (CO 2 ) removal from methane (CH 4 ) is examined for various arrangements of HFMs in staggered and inline configurations. The common HFM module arrangement is the axial flow configuration. However, this work focuses on the radial cross-flow configuration. The HFM surface is a functional boundary where the suction rate and concentration of each species are coupled and are functions of the local partial pressures, the permeability, and the selectivity of the HFM. CFD simulations employed the turbulent k–ω shear stress transport (SST) model to study HFM performance for Reynolds numbers, 200 ≤ Re ≤ 1000. The efficiency of the inline and staggered arrangements in the separation module is evaluated by the coefficient of performance and the rate of mass flow per unit area of CO 2 passing across the membrane surface. This work demonstrates that the module with staggered arrangement outperforms the module with the inline arrangement.

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 score0.475
Threshold uncertainty score0.792

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 head0.021
GPT teacher head0.237
Teacher spread0.216 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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