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Record W2065128310 · doi:10.1115/1.1541632

Valve-Plate Design for an Axial Piston Pump Operating at Low Displacements

2003· article· en· W2065128310 on OpenAlexfundno aff
Noah D. Manring

Bibliographic record

VenueJournal of Mechanical Design · 2003
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPiston (optics)Quadratic growthGeometryConstant (computer programming)Principal (computer security)Piston pumpPrincipal axis theoremMechanical engineeringMathematicsComputer scienceEngineeringPhysicsMathematical analysisOptics

Abstract

fetched live from OpenAlex

The objectives of this research are to investigate the principal advantages of using various valve-plate slot geometries within an axial piston pump. In particular, three types of geometries are considered: a constant area slot geometry, a linearly varying slot geometry, and a quadratically varying slot geometry. By analyzing the pressure transients that are associated with each design at low pump displacements, it is shown that the magnitude of the pressure transition itself and the maximum pressure time rate-of-change may be specified for each design. In conclusion, it is shown that the constant area slot design exhibits the principal advantage of minimizing the required discharge area of the slot, the linearly varying slot design exhibits the principal advantage of utilizing the shortest slot length, while the quadratically varying slot design exhibits no principal advantage over either of the other two designs. The results of this research suggest that the use of quadratically varying slot geometry is not justified since it offers no obvious performance improvement.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.269
Teacher spread0.222 · 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 designBench or experimental
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

Citations87
Published2003
Admission routes1
Has abstractyes

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