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Record W2331282909 · doi:10.1115/imece2015-53213

Application of an Optimized SLW Model to Calculation of Non-Gray Radiation Heat Transfer in a Furnace

2015· article· en· W2331282909 on OpenAlexaff
Masoud Darbandi, Bagher Abrar, G. E. Schneider

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsUniversity of Waterloo
FundersSharif University of Technology
KeywordsHeat transferGray (unit)Thermal radiationRadiative transferCombustionComputer scienceMechanicsPhysicsOpticsChemistryThermodynamics

Abstract

fetched live from OpenAlex

The spectral line based weighted sum of gray gases (SLW) model is considered as an advanced model, which can solve the radiative transfer equation (RTE) in non-gray participating media by dividing the absorption cross section spectrum into a limited number of intervals. Each interval is then treated as a gray gas medium, in which the attributed RTE should be solved separately. Therefore, the SLW model would be computationally more efficient than the other non-gray participating media solvers because it is faced with a small number of RTE solutions. In this work, we present a novel optimized SLW model and applied it to radiation heat transfer calculation in a model furnace. The current optimized SLW model with only 3 gray gases can provide accuracy close to the line-by-line (LBL) method. This is while the classic nonoptimized SLW model cannot provide the same level of accuracy imposing only 3 gray gases. Therefore, we strongly recommend the optimized SLW model to calculate the radiation heat transfer in non-gray combustion gas mixtures.

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)0.000
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 score0.549
Threshold uncertainty score0.427

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 head0.020
GPT teacher head0.263
Teacher spread0.243 · 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 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
Published2015
Admission routes1
Has abstractyes

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