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Record W2522981703 · doi:10.1155/2016/4608328

Vibration Analysis and Experimental Research of the Linear-Motor-Driven Water Piston Pump Used in the Naval Ship

2016· article· en· W2522981703 on OpenAlexaff
Huang Ye-qing, Songlin Nie, Hui Ji, Shuang Nie

Bibliographic record

VenueShock and Vibration · 2016
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Toronto
FundersBeijing Municipal Natural Science FoundationChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsPiston (optics)Piston pumpVibrationAxial piston pumpModal analysisEngineeringLinear motorHarmonicAcousticsDamperControl theory (sociology)Mechanical engineeringStructural engineeringVariable displacement pumpPhysicsHydraulic pumpComputer scienceControl (management)

Abstract

fetched live from OpenAlex

Aiming at the existing problems of traditional water piston pump used in the naval ship, such as low efficiency, high noise, large vibration, and nonintelligent control, a new type of linear-motor-driven water piston pump is developed and its vibration characteristics are analyzed in this research. Based on the 3D model of the structure, the simulation analyses including static stress analysis, modal analysis, and harmonic response analysis are conducted. The simulation results reveal that the mode shape under low frequency stage is mainly associated with the eccentricity swing of the piston rod. The vibration experiment results show that the resonance frequency of linear-motor-driven water piston pump is concentrated upon 500 Hz and 800 Hz in the low frequency range. The dampers can change the resonance frequency of the system to a certain extent. The vibration under triangular motion curve is much better than that of S curve, which is consistent with the simulation conclusion. This research provides an effective method to detect the vibration characteristics and a reference for design and optimization of the linear-motor-driven water piston pump.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.111

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 head0.037
GPT teacher head0.296
Teacher spread0.259 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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