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Record W2561446372 · doi:10.1002/aic.15614

Mass and heat transfer behavior of oscillating helical coils in relation to heterogeneous reactor design

2016· article· en· W2561446372 on OpenAlexafffund
M.H. Abdel‐Aziz, Inderjit Nirdosh, Gomaa H. Sedahmed

Bibliographic record

VenueAIChE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExothermic reactionMass transferHeat exchangerMechanicsHeat transferDiffusionElectromagnetic coilTube (container)ThermodynamicsFlow (mathematics)AmplitudeVibrationMaterials scienceChemistryPhysicsAcousticsComposite materialOptics

Abstract

fetched live from OpenAlex

Rates of mass and heat transfer at vibrating helical coils were studied by the electrochemical technique with the object of using helical coils as heat exchanger/reactor for conducting liquid–solid diffusion controlled reactions. Variables studied were frequency and amplitude of vibration, tube diameter, and superimposed axial flow velocity. The data for vibrating coil (batch reactor) were correlated for 59 < < 4965; Sc = 2314 by the equation: urn:x-wiley:00011541:media:aic15614:aic15614-math-0002 The data were found to be consistent with the analogy model. For vibrated helical coils with superimposed axial flow, the data were correlated by the equation: urn:x-wiley:00011541:media:aic15614:aic15614-math-0003 Importance of the present results in the design and operation of heterogeneous reactors used to conduct diffusion controlled exothermic reactions involving heat sensitive materials was pointed out. Also the importance of the present results in the design and operation of shell and helical tube heat exchangers of improved performance was highlighted. © 2017 American Institute of Chemical Engineers AIChE J , 63: 3141–3149, 2017

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.245

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 head0.058
GPT teacher head0.290
Teacher spread0.232 · 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

Citations8
Published2016
Admission routes2
Has abstractyes

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