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Record W2158827177 · doi:10.1109/tcapt.2003.811469

Effect of surface asperity truncation on thermal contact conductance

2003· article· en· W2158827177 on OpenAlexaff
Fernando H. Milanez, M. M. Yovanovich, J. R. Culham

Bibliographic record

VenueIEEE Transactions on Components and Packaging Technologies · 2003
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThermal contact conductanceAsperity (geotechnical engineering)Materials scienceConductanceSurface roughnessSurface finishContact areaThermalMechanicsTruncation (statistics)GaussianSurface (topology)ProfilometerThermal conductivityOpticsComposite materialThermal resistanceThermodynamicsGeometryCondensed matter physicsChemistryPhysicsMathematics

Abstract

fetched live from OpenAlex

This paper presents studies on thermal contact conductance at light contact loads. Surface profilometry measurements are presented which show that actual surface asperity height distributions are not perfectly Gaussian. The highest asperities are truncated, causing existing thermal contact conductance models to underpredict experimental data. These observations have been incorporated into modifications of existing contact conductance models. The truncation leads to an enhancement of thermal contact conductance at light contact pressures. The preliminary model has been compared against thermal contact conductance data presented in the open literature, and good agreement is observed. The results show that the truncation is a function of the roughness level: the rougher the surface, the more truncated the surface height distribution.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.230
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations26
Published2003
Admission routes1
Has abstractyes

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