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Record W2018846067 · doi:10.1080/10407782.2011.616777

Prediction of the Bank Formation in High Temperature Furnaces by a Sequential Inverse Analysis with Overlaps

2011· article· en· W2018846067 on OpenAlexaff
M.-A. Marois, Martin Désilets, M. Lacroix

Bibliographic record

VenueNumerical Heat Transfer Part A Applications · 2011
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsInverseInertiaConjugate gradient methodPhase (matter)Simple (philosophy)Feature (linguistics)Temperature gradientInverse problemThermalControl theory (sociology)Phase changeComputer scienceAlgorithmMaterials scienceMathematicsPhysicsMathematical analysisMeteorologyThermodynamicsArtificial intelligenceGeometry

Abstract

fetched live from OpenAlex

This article presents a simple sequential inverse method for predicting time-varying thickness of the phase change protective bank found on the inside surface of a wall of a high temperature furnace. The main feature of the proposed overlapping procedure is its unique capability to increase the diagnostic frequency for phase change processes with large thermal inertia. The inverse method rests on the adjoint problem and the conjugate gradient method, and it relies on nonintrusive temperature measurements. Results also indicate that under typical melting furnace operating conditions, the proposed overlapping procedure doubles the allowable diagnostic frequency for predicting the time-varying bank thickness.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.199
Teacher spread0.182 · 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

Citations15
Published2011
Admission routes1
Has abstractyes

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