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Record W2015204633 · doi:10.1115/fedsm-icnmm2010-31149

Validation of a Smoothed Particle Hydrodynamics Code for Internal Flow Simulations: Application to Hemodynamics in a Realistic Left Heart Cavity Model

2010· article· en· W2015204633 on OpenAlexaff
Shahrokh Shahriari, Ibrahim Hassan, Lyes Kadem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsConcordia University
Fundersnot available
KeywordsSmoothed-particle hydrodynamicsReynolds numberMechanicsFlow (mathematics)Finite volume methodPulsatile flowComputational fluid dynamicsInflowPhysicsSimulationClassical mechanicsComputer scienceTurbulence

Abstract

fetched live from OpenAlex

A numerical simulation of flow in the left heart cavity, left ventricle, based on Smoothed Particle Hydrodynamics, a meshfree particle method is presented. Most of the works using this numerical method have been dedicated to simulation of free surface flows or internal flows with low Reynolds number. The present study is the first work dedicated to simulate the complex flow in a realistic rigid model of left ventricle applying the realistic pulsatile inlet velocity (having moderate Reynolds number) using a meshfree particle method. The numerical validation of our code is performed through the simulation of flow in a cavity at a Reynolds number equal to 1000. Also, the comparison of the results of flow simulation in a simplified geometry of left ventricle with the finite volume results is presented. The smoothed particle hydrodynamics method was able to resolve the flow patterns showing its potential to be applied in complex cardiovascular flow simulations.

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: Empirical · Consensus signal: none
Teacher disagreement score0.452
Threshold uncertainty score0.545

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 head0.010
GPT teacher head0.270
Teacher spread0.260 · 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
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
Published2010
Admission routes1
Has abstractyes

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