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Record W2094776095 · doi:10.1115/imece2003-42707

Analysis and Optimization Design of Pressure Compensated Flow Control Valves

2003· article· en· W2094776095 on OpenAlexafffund
Duqiang Wu, Richard Burton, Greg Schoenau, Doug Bitner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBody orificePressure dropGlobe valveFlow control valveControl valvesButterfly valveFlow coefficientValve actuatorBall valveVolumetric flow rateControl theory (sociology)Flow control (data)Flow (mathematics)Pressure controlMaterials scienceMechanicsComputer scienceEngineeringMechanical engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

A pressure compensated valve (PC valve) is a type of flow control device that is a combination of a control orifice and a compensator (often called a hydrostat). The compensator orifice modulates its opening to maintain a constant pressure drop across the control orifice. In other words, the PC valve is so designed that the flow rate through the valve is governed only by the opening of the control orifice and is independent of the total pressure drop across the valve. Because of the high non-linearities associated with this type of valve, it is impossible, in practice, to design such a valve where the flow rate is completely unaffected by the pressure drop across the valve. In this paper, the effect of the non-linerities on the performance of the PC valve is investigated. First, a generic non-liner model of a PC valve is developed. Using this model, all possible operating conditions can be determined. Then a linearized model is developed and used to analyze the dynamic behavior of the PC valve. The model can then be used to optimize the design and operation of the valve for specific applications.

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: Methods · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.262

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 head0.191
Teacher spread0.182 · 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
GenreMethods

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
Published2003
Admission routes2
Has abstractyes

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