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Record W1934512863 · doi:10.1109/stherm.2005.1412158

Thermal contact resistance: effect of elastic deformation

2005· article· en· W1934512863 on OpenAlexafffund
Majid Bahrami, M. M. Yovanovich, J. R. Culham

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceThermal contact conductanceDeformation (meteorology)Contact resistanceParametric statisticsMicroelectronicsComposite materialThermalMechanicsStructural engineeringForensic engineeringThermal resistanceEngineeringThermodynamicsPhysicsMathematics

Abstract

fetched live from OpenAlex

Existing models over-predict the thermal contact resistance of conforming rough joints at low contact pressures. However, the applicable pressure range in the microelectronics industry is low due to load constraints. In this paper, a new model is presented which is more suitable for low pressures. The present model assumes plastic deformation at microcontacts. The effect of elastic deformations beneath the microcontacts is determined by superimposing normal deformations in an elastic half-space due to adjacent microcontacts. The model also accounts for the variation of the effective microhardness. A parametric study is conducted to investigate the effects of main contact input parameters on the elastic effect. The study reveals that the elastic deformation effect is an important phenomenon especially in low contact pressures. The present model is compared with experimental data and good agreement is observed at low contact pressures.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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

Citations3
Published2005
Admission routes2
Has abstractyes

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