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Record W2795730771 · doi:10.1139/tcsme-2013-0079

ATOMISTIC SCALE SIMULATION OF TEXTURED SURFACES ON DRY SLIDING FRICTION

2013· article· en· W2795730771 on OpenAlexvenueno aff
Mingyuan Chen, Zheng-Han Hong, Te‐Hua Fang, Shao-Hui Kang, Li-Min Kuo

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceTribologySurface (topology)Molecular dynamicsDry frictionSubstrate (aquarium)Composite materialScale (ratio)Atom (system on chip)Friction coefficientGeometryComputational chemistryChemistryComputer sciencePhysics

Abstract

fetched live from OpenAlex

Fe sliding on a Fe substrate with surface texturing is investigated using molecular dynamics simulation. The modified embedded-atom method many-body potential is used to describe the interaction of Fe atoms. The tribological properties of surface texturing during nanosliding are discussed. Results indicate that a textured surface has lower friction than that of a flat surface. In addition, a surface with parallel grooves has lower friction than that of a dimpled surface. Hence, surface texturing greatly affects friction.

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: none
Teacher disagreement score0.914
Threshold uncertainty score0.494

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 head0.009
GPT teacher head0.193
Teacher spread0.185 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicTribology and Lubrication EngineeringFrench-language works237,207