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Record W4234437841 · doi:10.32920/ryerson.14663115

Modelling Compressible Blood Flow with Slip in a Constricted Rectangular Flow Domain

2021· preprint· en· W4234437841 on OpenAlexaff
Matthew DeClerico

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsMcMaster UniversityToronto Metropolitan University
FundersStrong
KeywordsCompressibilityMechanicsSlip (aerodynamics)Newtonian fluidFlow (mathematics)MicrochannelPartial differential equationCompressible flowNumerical analysisPhysicsOpen-channel flowMathematicsClassical mechanicsMathematical analysisThermodynamics

Abstract

fetched live from OpenAlex

The abnormal narrowing of blood vessels is known to affect the characterization of blood flow through these constricted regions. Both theoretical and clinical research has suggested that these changes in flow are associated with cardiovascular related diseases. Analytic, numerical, and particle based methods have been employed to solve the Navier-Stokes momentum integral equations associated with compressible, Newtonian fluid flow. In this thesis, the Karman-Pohlhausen method is used to transform a system of partial differential equations into a single second-order, non-linear differential equation in terms of the density. Numerical solutions are presented and important flow features, including the role of slip and compressibility, are discussed. The choice to use a symmetric rectangular channel, rather than a cylindrical one, is largely motivated by the opportunity to compare the numerical solutions with experimental data collected from a rectangular microchannel. The numerical results also indicate similar trends in the flow characteristics for the rectangular channel as compared to previous results using cylindrical models.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.008
GPT teacher head0.180
Teacher spread0.171 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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