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Record W2948191624 · doi:10.11159/ffhmt19.132

Assessment of SLW model in 3D non-gray gas radiation calculation

2019· article· en· W2948191624 on OpenAlexaffvenue
Masoud Darbandi, Mohammad Bagher Barezban, G. E. Schneider

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2019
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsUniversity of Waterloo
FundersSharif University of Technology
KeywordsGray (unit)Computer science

Abstract

fetched live from OpenAlex

In this paper, the radiation heat transfer is calculated numerically in a three dimensional enclosure containing non-gray gases. The radiation heat transfer equation is solved using the finite-volume formulation of discrete ordinate method and the SLW model is employed to calculate the radiation absorption coefficients. Three cases are simulated here including the isothermal/homogeneous, isothermal/nonhomogeneous, and the non-isothermal/homogeneous media. The calculated radiation source terms and the wall heat fluxes are compared with the results of SNB method. It is shown that the SLW results are in very good agreement with the SNB in case of isothermal/homogeneous. In the two other cases, the SLW results show more deviations than this one. It is because the SLW formulation employs more additional assumptions to calculate the radiation in media with non-uniform distribution of temperature or mixture composition and multicomponent gas mixture. However, comparing the results of SLW with those of the WSGG model, it indicates that the former case would be more accurate. This is because the parameters of SLW model are calculated directly from the high resolution spectroscopic database of gas molecules.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.578

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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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