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Record W2512172170 · doi:10.1021/acs.iecr.6b02234

Correlation for Calculating Heat Transfer Coefficient in Conical Spouted Beds

2016· article· en· W2512172170 on OpenAlexaff
Juan F. Saldarriaga, Roberto Aguado, Aitor Atxutegi, John R. Grace, Javier Bilbao, Martı́n Olazar

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2016
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of British Columbia
FundersEuropean Regional Development FundEuskal Herriko UnibertsitateaMinisterio de Economía y Competitividad
KeywordsConical surfaceAnnulus (botany)Heat transferMechanicsThermodynamicsHeat transfer coefficientMaterials scienceMathematicsGeometryPhysicsComposite material

Abstract

fetched live from OpenAlex

A correlation has been proposed for predicting local bed-to-surface heat transfer coefficients in conical spouted beds based on an experimental study conducted using beds made up of sand, sawdust, and their mixtures under various operating conditions, namely, different static bed heights and gas velocities over minimum spouting. A comparison of the results with those obtained using the correlations in the literature proved that they provide very poor predictions and, furthermore, they are not able to predict local coefficients in the bed. Based on a statistical analysis, the significant moduli have been identified in the relevant literature correlations and two new moduli related to the radial and longitudinal positions have been contemplated. The analysis identified two groups of heat transfer coefficients: those within the bed and those on its surface. The correlation proposed is specifically suitable for ascertaining the best location of heat transfer devices within the annulus of the spouted beds.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.256
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.067
GPT teacher head0.303
Teacher spread0.236 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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