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Record W2141168552 · doi:10.1115/imece2004-60131

Modeling of Thermal Joint Resistance of Polymer-Metal Rough Interfaces

2004· article· en· W2141168552 on OpenAlexaff
Majid Bahrami, M. M. Yovanovich, E. Marotta

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceJoint (building)PolymerComposite materialThermalThermal resistanceThermal contact conductanceMetalDeformation (meteorology)Interfacial thermal resistanceContact resistanceStructural engineeringMetallurgyThermodynamicsLayer (electronics)

Abstract

fetched live from OpenAlex

A compact analytical model is developed for predicting thermal joint resistance of rough polymer-metal interfaces in a vacuum. The joint resistance includes two components: i) bulk resistance of the polymer and ii) micro, constriction/spreading resistance of the microcontacts at the interface. Performing a deformation analysis, it is shown that the deformation mode of the polymer asperities is plastic. The required input parameters of the model can be measured in the laboratory and/or found in the open literature. It is observed that the thermophysical properties of the polymer control the thermal joint resistance and the metallic body properties have a second order effect on the thermal joint resistance. A non-dimensional parameter, i.e., ratio of microcontacts over bulk thermal resistances, is proposed as a criterion to specify the relative importance of the microcontacts thermal resistance. The present model is compared with more than 140 experimental data points collected for a selected number of polymers and showed good agreement.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.330

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 head0.017
GPT teacher head0.219
Teacher spread0.202 · 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.

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

Citations8
Published2004
Admission routes1
Has abstractyes

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