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Record W4389584883 · doi:10.17118/11143/20713

Application of the entropically damped artificial compressibility methodto ventricular assist devices

2023· article· en· W4389584883 on OpenAlexaff
Marie-Pier Bolduc, Ramin Ghoreishi, Lyes Kadem, Brian C. Vermeire

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsConcordia University
Fundersnot available
KeywordsCompressibilityArtificial heartComputer scienceMaterials scienceMechanical engineeringMechanicsEngineeringPhysicsCardiology

Abstract

fetched live from OpenAlex

Abstract: Ventricular assist devices (VADs) represent one of the many applications (hemodynamics, vehicle aerodynamics, hydrofoils, wind turbines, and energy-saving bio-locomotion) that require accurate, efficient, and stable solutions for incompressible flows around complex moving and deforming geometries. The article addresses the challenges in solving such flows. The flux reconstruction (FR) method is presented as a suitable solution technique and its application to incompressible flows using the entropically damped artificial compressibility (EDAC) method is introduced as a promising pressure-dampening alternative to traditional methods. The article describes the implementation of the EDAC solver with the Arbitrary Lagrangian-Eulerian (ALE) form of the compressible Navier-Stokes equations in the High-Order Unstructured Solver (HORUS). Verification and validation of the solver have previously been performed, and results demonstrated of the method’s suitability for VADs and its use in determining the optimal placement, shape, and actuation of VAD membranes in the heart’s ventricle.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.149

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 head0.020
GPT teacher head0.251
Teacher spread0.231 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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