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Record W3845406 · doi:10.1522/12599100

A 2-D random walk model for predicting ice accretion on a cylindrical conductor =

2001· book· en· W3845406 on OpenAlexfundno aff
Yongmin Chen

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of AlbertaHydro-QuébecUniversité du Québec à Chicoutimi
KeywordsConductorRandom walkAccretion (finance)PhysicsStatistical physicsGeologyMechanicsAstrophysicsGeometryMathematicsStatistics

Abstract

fetched live from OpenAlex

Ce mémoire de maîtrise présente un modèle en 2-D qui permet de prédire le profile et le poids de l'accumulation de glace sur un conducteur cylindrique, par la méthode du «cheminant aléatoire», et non pas par l'approche continue classique qui est basée sur la résolution des équations de conservation. Dans cette approche, les gouttelettes sont considérées comme ayant un mouvement aléatoire le long du conducteur ou à la surface de la glace accumulée. Les paramètres du modèle utilisé («cheminant aléatoire»), incluant la probabilité du gel de l'eau, du délestage et du mouvement de celle-ci, sont exprimés en fonction des conditions météorologiques et environnementales. Quelques nouveaux paramètres, notamment les pertes Joule causées par le passage du courant électrique et la direction de la collusion des gouttes d'eau sur le cylindre, sont considérés pour la première fois dans cette approche.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.520
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.242
Teacher spread0.212 · 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.

Study designSimulation or modeling
Domainnot available
GenreOther

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

Citations5
Published2001
Admission routes1
Has abstractyes

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