MétaCan
Menu
← Back to cohort
Record W4220809351 · doi:10.18280/jesa.550116

Electromagnetic Forces Effects of MHD Micropump on the Blood Movement

2022· article· en· W4220809351 on OpenAlexvenueno aff
Fatima Merdj, Saïd Drid

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2022
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
Fundersnot available
KeywordsMicropumpMagnetohydrodynamic driveMagnetohydrodynamicsMechanicsFinite element methodMagnetic fieldLorentz forceMechanical engineeringMagnetMagnetoMaterials sciencePhysicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

The magnetohydrodynamic pump is an attractive solution, in particular for biomedical applications. In an MHD pump, an electromagnetic force is created by the applied magnetic field, which causes the fluid movement. The main advantage of the MHD pump is there are no mobile (mechanical) parts and it can place directly on veins. The present paper deals with the blood behaviour in the MHD micropump. A neodymium permanent magnet is used for applying a magnetic field to the channel in the MHD micropump. The numerical study examines the influence of the channel dimensions, the flux magnetic density and the electrode potentials on the blood velocity. This micropump can be easily controlled by a low voltage source. The numerical simulation analysis for the adopted model was implemented in order to verify the micropump operation. The magnetic and electrical fields have a strong influence on blood velocity in the MHD micropump. Finite element modelling software was used for this process. The second objective of this work is the possibility to exploit the properties of this pump in hemodialysis to pump blood and cleaning fluid.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.186
Teacher spread0.180 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal Européen des Systèmes Automatisés→Same topicFuel Cells and Related Materials→French-language works237,207→