MétaCan
Menu
Back to cohort
Record W2105156488 · doi:10.1002/cjce.22296

The parameter domain of convective instability of the adiabatic packed‐bed reactor

2015· article· en· W2105156488 on OpenAlexafffundvenue
V. Z. Yakhnin, Sorathep Rattanayotsakun, Attasak Jaree, Michael Menzinger

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldComputer Science
TopicNonlinear Dynamics and Pattern Formation
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInstabilityExothermic reactionPacked bedAdiabatic processConvective instabilityConvectionMechanicsConvective flowParameter spacePerturbation (astronomy)Convective heat transferSensitivity (control systems)Parametric statisticsMaterials scienceThermodynamicsPhysicsChemistryChromatographyMathematicsEngineeringGeometry

Abstract

fetched live from OpenAlex

Abstract Packed‐bed reactors (PBR) can exhibit two types of convective instability: dynamic or differential‐flow instability (DIFI), and static instability, commonly referred to as parametric sensitivity (PS). DIFI and PS both amplify external perturbations and may result in reactor temperature excursions that can damage the product and the reactor itself. The convective character of DIFI and PS manifests itself physically in the return of the reactor to its initial stationary state after the external perturbation subsides. In this paper we describe a method of mapping the convective instability domain in the parameter space of a PBR and illustrate its application using a simple reactor model involving the standard exothermic reaction A → B + heat.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.012
GPT teacher head0.193
Teacher spread0.181 · 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

Citations3
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicNonlinear Dynamics and Pattern FormationFrench-language works237,207