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Numerical modeling of rough contact interfaces with trapped compressive liquid pockets

2025· article· en· W4413883226 on OpenAlexaff
P. Alavi, Guillaume Anciaux, Lorenzo Rocchi, J. Richard, Jean‐François Molinari

Bibliographic record

VenueTribology International · 2025
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsNovelis (Canada)
FundersInnosuisse - Schweizerische Agentur für Innovationsförderung
KeywordsMaterials scienceTribologyRough surfaceComposite materialMechanicsMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

We introduce a novel numerical model that integrates a Boundary Element Method (BEM) code in Fourier space to solve the problem of lubricated frictional contact between rough surfaces. This model accounts for scenarios where the lubricant quantity is significant yet discontinuous, leading to the formation of trapped, compressible lubricant pockets at the contact interface. Additionally, it incorporates the plastic behavior of solid surfaces through a simple plastic saturation method, enabling comprehensive analysis of contact area, pressure distribution, and variations in the friction coefficient across a wide range of conditions, such as lubricant type, density, and surface roughness. Comparison with experimental strip-drawing tests shows that, despite its simplicity, the model successfully captures the observed trend of a decreasing friction coefficient with increasing normal pressure in a mixed-lubrication regime. Notably, the study reveals that when the amount of lubricant is insufficient to fully fill the interfacial gap, even minor adjustments in its distribution can significantly influence the friction behavior of aluminum during forming processes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.490

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 head0.011
GPT teacher head0.262
Teacher spread0.250 · 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 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

Citations3
Published2025
Admission routes1
Has abstractyes

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