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Numerical Simulations of Blood Flow in Artificial and Natural Hearts With Fluid–Structure Interaction

2008· article· en· W1580134939 on OpenAlexaff
Matthew G. Doyle, Jean‐Baptiste Vergniaud, Stavros Tavoularis, Yves Bourgault

Bibliographic record

VenueArtificial Organs · 2008
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of Ottawa
FundersInstitut national de recherche en informatique et en automatique (INRIA)
KeywordsPulsatile flowFluid–structure interactionDiaphragm (acoustics)MechanicsFluid dynamicsFlow (mathematics)Blood flowCurrent (fluid)Computer simulationComputer scienceBiomedical engineeringSimulationPhysicsEngineeringAcousticsMedicineCardiologyFinite element methodThermodynamics

Abstract

fetched live from OpenAlex

This article describes two ongoing numerical studies of fluid-structure interaction in the cardiovascular system: an idealized pulsatile ventricular assist device (VAD), consisting of two fluid chambers separated by a flexible diaphragm; and blood flow and heart wall motion during passive filling of a canine heart. Simulations have been performed for the VAD and compared with the results of a previous study and to our own preliminary experimental results. Detailed measurements of the flow field in the VAD model and additional simulations are in progress. Preliminary simulations using both an idealized model of the natural heart as well as a realistic model have identified the limitations of the current numerical methods in dealing with large three-dimensional deformations. Ongoing research aims at extending the range of simulations to include large deformations and to incorporate an anisotropic material model for the heart wall to account for the muscle fibers.

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)0.002
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.012
GPT teacher head0.216
Teacher spread0.203 · 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

Citations12
Published2008
Admission routes1
Has abstractyes

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