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Record W3196474353 · doi:10.18280/mmep.080406

Blood Flow with Multiple Stenoses in a Force Field

2021· article· en· W3196474353 on OpenAlexvenueno aff
Mallinath Dhange, G. C. Sankad, Umesh Bhujakkanavar

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2021
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsShear stressPressure dropMechanicsBlood flowStenosisFlow (mathematics)RheologyMaterials scienceMedicineCardiologyPhysicsComposite material

Abstract

fetched live from OpenAlex

In the present manuscript, a mathematical model of steady and incompressible Casson fluid in a non-uniform tube having many stenoses in the presence of a force field is analyzed. Using mild stenosis approximation and appropriate boundary conditions, analytical expressions for velocity, pressure drop, impedance, and wall share stress have been computed due to their importance in the rheology of blood. The Casson fluid is used to depict the behavior of blood flow. The effects of different physical constraints on resistance to the flow and wall shear stress of the fluid are examined. The study ascertains that resistance to the flow and wall shear stress is maximum at duck of stenosis. It is also explored that an increase in the size of the stenosis in the artery affects the normal flow of the blood through vessels in the heart, body, and brain and this may lead to major cardiac disease problems like stroke, heart attack, etc.

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.011
GPT teacher head0.171
Teacher spread0.160 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueMathematical Modelling and Engineering ProblemsSame topicNanofluid Flow and Heat TransferFrench-language works237,207