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Record W2325647521 · doi:10.1115/imece2005-79761

Experimental Simulation of Piston Leakage in an Axial Piston Pump

2005· article· en· W2325647521 on OpenAlexaff
Zeliang Li, Richard Burton, P.N. Nikiforuk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAxial piston pumpRadial piston pumpPiston pumpLeakage (economics)Piston (optics)Reciprocating pumpPlunger pumpMechanical engineeringVariable displacement pumpDischarge pressureWaveformMechanicsServoMaterials scienceEngineeringAcousticsElectrical engineeringHydraulic pumpVoltagePhysicsOpticsGas compressor

Abstract

fetched live from OpenAlex

A method used to introduce “artificial leakage” into an axial piston pump to simulate leakage from a worn piston is described in this paper. A pressure control servo valve with a very high frequency response was employed to divert flow from the pump outlet in a prescribed waveform directly to tank. The purpose was to simulate piston leakage from the high pressure discharge chamber to the pump case drain chamber as the “simulated worn piston” made contact with the high pressure chamber. The system and associated control algorithms mimiced the action of a single worn piston at various degrees of wear. The experimental results indicated that the experimental system could successfully introduce artificial leakage into the pump which was consistent with a unit with a “real” worn piston. Comparisons of the pressure ripples from an actual faulty pump (with one worn piston) and the artificial faulty pump (with one simulated worn piston) are presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.267
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 source (direct Gemma or distilled Codex), 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
Published2005
Admission routes1
Has abstractyes

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