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NON-GRAY GAS MODELING IN COMPLEX ENCLOSURES: APPLICATION OF THE HYBRID SNB-CK METHOD

2001· article· en· W2042617810 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsComputationRadiative transferCombustionGray (unit)Computational physicsStatistical physicsMathematicsAlgorithmGeometryApplied mathematicsComputer scienceBiological systemChemistryPhysicsOpticsPhysical chemistry

Abstract

fetched live from OpenAlex

This study compares the relative efficiencies (accuracy and computation time) of three versions of the hybrid CK/statistical narrow band method (SNB-CK) for characterizing radiative exchanges in enclosures containing a mixture of non-gray gases (CO2 and H2O). One of these versions, which is characterized by a selective regrouping of bands, leads to very good results while the computation time is significantly decreased with respect to the original SNB-CK version. Complex 2D cases, close to real combustion chambers, are analysed. These cases include irregular geometry and non-uniform concentration and temperature fields. The radiative transfer equation is solved by the discrete ordinates method. The importance of the presence of CO as a third component is also assessed.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.765
Threshold uncertainty score0.330

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 head0.031
GPT teacher head0.271
Teacher spread0.240 · 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

Quick stats

Citations2
Published2001
Admission routes1
Has abstractyes

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