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Record W3016471886 · doi:10.1063/1.5144388

Particle Residence Time in pulsatile post-stenotic flow

2020· article· en· W3016471886 on OpenAlexafffund
Mark D. Jeronimo, David E. Rival

Bibliographic record

VenuePhysics of Fluids · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStrouhal numberReynolds numberPhysicsMechanicsPulsatile flowStreamlines, streaklines, and pathlinesLaminar flowJet (fluid)TurbulenceMedicine

Abstract

fetched live from OpenAlex

Particle Residence Time (PRT), a measure of a fluid element’s transit time through a region of interest, is a clear indicator of recirculation. The PRT of fluid recirculating downstream of an idealized stenosis geometry is found to vary dramatically under pulsatile flow conditions. Two-dimensional particle tracking velocimetry is used to track particles directly as they exit the stenosis geometry and are entrained into the region of recirculation immediately downstream. A Lagrangian approach permits long pathlines to be drawn, describing each particle’s motion from the instant they enter the domain. PRT along each pathline is compared here for three mean Reynolds numbers; specifically, Rem = 4800, 9600, and 14 400. The pulsatile waveforms are characterized by Strouhal numbers of 0.04, 0.08, and 0.15 and amplitude ratios of 0.50 and 0.95. As the mean Reynolds number is increased, higher fluid velocities are shown to lower PRT. However, the strength of PRT is truly revealed when highlighting the influence pulsatility has on the degree of mixing beyond the stenosis throat. Higher Strouhal numbers correlate with roll-up across the shear layer and increased PRT distribution at all Reynolds numbers in consideration. Similarly, strong temporal velocity gradients generated by a high amplitude ratio carry large volumes of fluid from the jet deep into the recirculation region, contributing to greater PRT.

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: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.446

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 head0.195
Teacher spread0.185 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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