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Record W2902862457 · doi:10.1139/tcsme-2017-504

AXIAL-FLOW BLOOD PUMP ACCELERATION CONTROL METHOD BASED ON A START-UP PROCESS DYNAMIC MODEL

2017· article· en· W2902862457 on OpenAlexvenueno aff
Yan Xu, Youxia Pang, Cheng Li-zhi, Liang Liang

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
FundersNatural Science Foundation of Hunan ProvinceNational Natural Science Foundation of China
KeywordsAccelerationControl theory (sociology)Process (computing)MechanicsVolumetric flow rateDynamic simulationAcceleration timeFlow (mathematics)Blood flowSimulationEngineeringComputer sciencePhysicsControl (management)

Abstract

fetched live from OpenAlex

The principle of a large gap magnetic drive system was used to achieve control of an axial-flow blood pump. A dynamic model of the start-up process of the axial-flow blood pump was established. It was analyzed and simulated. An acceleration control method for the blood pump was proposed based on the start-up process dynamic model. A corresponding parameter measurement test system was set up, and experimental data were compared with the results of the theoretical simulation. Results indicated that the experimental values obtained for the blood pump outlet pressure and flow rate changed similarly with the values obtained using theoretical simulation. These changes occurred simultaneously with the change in speed of the blood pump over time, and the driving control target value was reached within 4 seconds.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.013
GPT teacher head0.245
Teacher spread0.232 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicOil and Gas Production TechniquesFrench-language works237,207