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Record W7124156425 · doi:10.37665/lekeygs14724

A Scale Analysis Approach to Thermal Contact Resistance

2004· article· W7124156425 on OpenAlexaff
Majid Bahrami, J. R. Culham, M. M. Yovanovich

Bibliographic record

VenueSpecialized and Legacy Electronics Manufacturing Conferences · 2004
Typearticle
Language
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThermal contact conductanceAsperity (geotechnical engineering)Surface roughnessSurface finishRange (aeronautics)Contact resistanceGaussian surfaceThermalSuperposition principleThermal conduction

Abstract

fetched live from OpenAlex

ABSTRACT A new analytical model is developed for predicting thermal contact resistance (TCR) of non-conforming rough contacts of bare solids in a vacuum. Instead of using probability relationships to model the size and number of microcontacts of Gaussian surfaces, a novel approach by employing the “scale analysis methods” is taken. It is shown that the mean size of the microcontacts is proportional to the surface roughness and inversely proportional to the surface asperity slope. A general relationship for determining TCR is derived by superposition of the macro and the effective micro thermal resistances. The present model allows TCR to be predicted over the entire range of nonconforming rough contacts from conforming rough to smooth Hertzian contacts. It is demonstrated that the geometry of heat sources on a half-space for microcontacts is justifiable and that the effective micro thermal resistance is not a function of surface curvature. A comparison of the present model with 604 experimental data points, collected by many researchers during the last forty years, shows good agreement for the entire range of TCR. The data covers a wide range of materials, mechanical and thermophysical properties, micro and macro contact geometries, and similar and dissimilar metal contacts.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.234
Teacher spread0.222 · 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
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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

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