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Record W2024598688 · doi:10.1115/1.1351168

Infinitesimal-Area Radiative Analysis Using Parametric Surface Representation, Through NURBS

2000· article· en· W2024598688 on OpenAlex
Kyle J. Daun, K.G.T. Hollands

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Heat Transfer · 2000
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Analysis Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsInfinitesimalEnclosureRepresentation (politics)Parametric statisticsSurface (topology)MathematicsParametric surfaceRadiative transferKernel (algebra)Applied mathematicsIntegral equationCode (set theory)Computer scienceMathematical analysisAlgebra over a fieldGeometryPure mathematicsProgramming languageSet (abstract data type)Physics

Abstract

fetched live from OpenAlex

The use of form factors in the treatment of radiant enclosures requires the radiosity be approximated as uniform over finite areas, and so when higher accuracy is required, an infinitesimal-area analysis should be applied. This paper describes a generic infinitesimal-area formulation suited in principle for any enclosure containing a transparent medium. The surfaces are first represented parametrically, through “non-uniform rational B-spline” (NURBS) functions, the industry standard in CAD-CAM codes. The kernel of the integral equation is obtained without user intervention, using NURBS algorithms, and then the integral equation is solved numerically. The resulting general-purpose code, which proceeds directly from surface specification to solution, is tested on problems taken from the literature.

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: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.291
Teacher spread0.264 · 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