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Record W4400806788 · doi:10.1063/5.0213889

Bleeding simulation with physical viscoelasticity in smooth particle hydrodynamics

2024· article· en· W4400806788 on OpenAlexaff
Pengyu Sun, Peter Liu

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsCarleton University
Fundersnot available
KeywordsPhysicsViscoelasticitySmoothed-particle hydrodynamicsParticle (ecology)MechanicsClassical mechanicsThermodynamics

Abstract

fetched live from OpenAlex

Being composed of blood cells and plasma, the blood flow has different rheological properties from common fluids. The viscoelastic properties of blood not only affect the flow characteristics of blood but also influence the shape of bleeding. In order to achieve the bleeding simulation with physical viscoelastic, we consider the effect of the aggregation behavior of red blood cells on the bleeding process. The elastic force caused by viscoelastic fluid elasticity is incorporated into the standard Navier–Stokes momentum equation, and an improved momentum equation that characterizes the rheological characteristics of viscoelastic blood is constructed. The effect of introducing elastic force on the particles motion is analyzed from the microscopic particle perspective by designing a two-dimensional particle system. The results of bleeding simulation show that the improved method eliminates the discontinuous boundary due to blood gushing out of the wound and forms irregular bleeding shapes in the bleeding process. In addition, the viscoelastic bleeding simulation exhibits a similar bleeding effect without distortion when the number of particles decreases, which further proves the reliability of proposed method.

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 score0.230
Threshold uncertainty score0.410

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 head0.254
Teacher spread0.244 · 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
Published2024
Admission routes1
Has abstractyes

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