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Record W3151598107 · doi:10.1115/1.4050719

The Past and Future of the Monte Carlo Method in Thermal Radiation Transfer

2021· article· en· W3151598107 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

VenueJournal of Heat Transfer · 2021
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVariance reductionMonte Carlo methodRadiative transferComputer scienceThermal radiationProbabilistic logicStatistical physicsEnergy transferFidelityPhysicsEngineering physicsTelecommunicationsMathematicsOptics

Abstract

fetched live from OpenAlex

Abstract Since its initial development as a specialty technique for modeling neutron transport in fissile materials almost 80 years ago, the Monte Carlo method has since been deployed in almost every area of science and engineering, including radiative transfer. This paper reviews the history and progress in Monte Carlo methods for simulating radiative energy transfer, with emphasis on advances over the past 25 years. A short historical review that emphasizes the probabilistic foundations of the method, is followed by discussions of recent extensions and applications, including variance reduction techniques, high fidelity simulations in complex media, and a discussion of unresolved issues. The article concludes with an outlook for the method as impacted by advancements in algorithm development as well as massively parallel and quantum computing.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.369

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 head0.007
GPT teacher head0.223
Teacher spread0.215 · 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