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Record W2986988524 · doi:10.1139/tcsme-2019-0174

Isothermal hydrodynamic pressure and load carrying capacity of parallel rough surfaces based on FFT techniques

2019· article· en· W2986988524 on OpenAlexvenueno aff
Wan Ma

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsFast Fourier transformCurvatureSurface roughnessMechanicsMaterials scienceLubricationSurface finishFluid bearingIsothermal processWork (physics)RADIUSSurface (topology)Fourier transformGeometryMechanical engineeringPhysicsComputer scienceMathematicsMathematical analysisAlgorithmEngineeringComposite materialThermodynamics

Abstract

fetched live from OpenAlex

In lubricated contacts, the component macrogeometry (radius of curvature) determines the pressure generation, and the surface microgeometry (i.e., roughness) alters it somewhat. However, for parallel surfaces, the microgeometry completely determines the hydrodynamic lubrication. This paper extends earlier work to numerically solve the isothermal hydrodynamic pressure generation and load carrying capacity (LCC) of surfaces with more complicated roughness features. A fast Fourier transform (FFT)-based method is described to quickly obtain the pressure distribution. The method is applicable to both real surface topographies and artificially generated rough surfaces. Results show that it enables one to predict the hydrodynamic pressure, when cavitation is negligible. The relative error of the LCC over the central domain is smaller than 8% and a 500× time saving, compared with the numerical method, is obtained.

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 score0.834
Threshold uncertainty score0.576

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 head0.006
GPT teacher head0.178
Teacher spread0.172 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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