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Record W4403033199 · doi:10.11159/jffhmt.2024.034

Impact of Superficial Gas Velocity on Gas Holdup in a Cu-Cl Cycle Thermochemical Oxygen Bubble Column Reactor

2024· article· en· W4403033199 on OpenAlexvenueno aff
Mohammed W. Abdulrahman, Nashaat N. Nassar

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2024
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBubbleGas bubbleOxygenBubble column reactorColumn (typography)ChemistrySuperficial velocityMaterials scienceMechanicsThermodynamicsNuclear engineeringAnalytical Chemistry (journal)ChromatographyPhysicsMechanical engineeringFlow (mathematics)Engineering

Abstract

fetched live from OpenAlex

The necessity to investigate a variety of hydrogen production technologies has been prompted by the increasing popularity of hydrogen as an alternative fuel.This investigation investigates the hydrodynamics of the specific substances utilized in the oxygen generation reactor, specifically molten CuCl and oxygen gas, in the context of hydrogen production through the copper-chlorine (Cu-Cl) cycle.In order to accomplish this, a three-dimensional Eulerian-Eulerian Computational Fluid Dynamics (CFD) model is implemented.The primary objective of the study is to verify the precision of material simulations that were conducted in a previous investigation for the oxygen reactor.Helium gas at 90C and liquid water at 20C were employed in that investigation to simulate the hydrodynamic behaviour of the actual materials.The three-dimensional O2-CuCl CFD model effectively simulates variations in gas holdup that occur as a result of changes in superficial gas velocity, with a maximum error of 29.9%.This error is the result of the complexity of the 3D multiphase system and the cumulative percentage errors associated with the hydrodynamic dimensionless parameters used in the previous material substitutions.Furthermore, the model shows that the gas holdup values of the actual materials are generally underestimated in comparison to those of the simulated materials.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.561

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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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 designBench or experimental
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

Citations1
Published2024
Admission routes1
Has abstractyes

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