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Record W3203807759 · doi:10.18280/acsm.450401

Contribution to the Study of Solid-Solid Thermal Contact Resistances--A Comparative Study

2021· article· en· W3203807759 on OpenAlexvenueno aff
Rahmouna Cheriet, Bourassia Bensaad, Fatiha Bouhadjela, Soufyane Belhenini, Mohammed Belharizi

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2021
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsThermal contact conductanceFinite element methodMaterials scienceThermalThermal contactContact areaContact resistanceMechanicsEmpirical modellingComposite materialThermodynamicsThermal resistanceEngineeringSimulationPhysics

Abstract

fetched live from OpenAlex

This study presents a mixed numerical / semi-empirical approach that primarily aimed to estimate the thermal contact resistance between two solids. The results obtained by this mixed method were compared and validated by experimental measurements of this resistance. Three semi-empirical models were used, namely the Mikic model, the Yovanovich model and the Antonetti model. The three-dimensional finite element numerical simulation was used to estimate the contact pressure between the two solids. Then this contact pressure obtained numerically was compared to the hardness of the solids in contact. The findings indicated that the numerically obtained contact pressures were close to hardness. Therefore, the hardness, which is usually used as an input variable in semi-empirical models, was replaced by the contact pressure. The thermal contact resistance obtained by this mixed method was then compared with the experimental one. The outcomes obtained from this comparison turned out to be very conclusive and can therefore be used to reinforce our approach which can actually be viewed as a reliable and low-cost method for estimating the thermal contact resistance between solids in contact.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.048
GPT teacher head0.334
Teacher spread0.286 · 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 designObservational
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

Citations2
Published2021
Admission routes1
Has abstractyes

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Same venueAnnales de Chimie Science des MatériauxSame topicAdhesion, Friction, and Surface InteractionsFrench-language works237,207