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Record W2015612742 · doi:10.1115/ihtc14-22531

1D Heat Transfer Model for the Chain Section of a Lime Kiln

2010· article· en· W2015612742 on OpenAlexaff
Michael Massad, Samer Hassan, Masahiro Kawaji, Honghi Tran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsKilnLimeHeat transferHeat transfer coefficientMaterials scienceThermodynamicsConvective heat transferWork (physics)Thermal conductionMechanicsMechanical engineeringComposite materialMetallurgyEngineeringPhysics

Abstract

fetched live from OpenAlex

This work was aimed at gaining a better understanding of heat transfer within lime kilns, by both experiments and detailed modeling of heat transfer phenomena in the chain section. Experiments were conducted using a laboratory mockup of a rotating kiln to obtain convective heat transfer coefficient data for cooling of a steel rod in dry or wet lime mud. For moisture contents of 0% and 30%, the mud heat transfer coefficient was determined to be 170 and 320 W/m2°C, respectively. A 1-D, unsteady heat conduction model was used to predict the temperature variations of all the chain rings in the chain system and calculate the amount of heat transferred by each chain ring to the lime mud. A thermal model was then developed to predict the steady axial temperature profiles of lime mud, gas and kiln wall throughout a rotating lime kiln equipped with a typical chain system.

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: none
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.214
Teacher spread0.202 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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