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Record W4404828331 · doi:10.3390/mi15121432

CFD Analysis of Particle Dynamics in Accelerated Toroidal Systems for Enhanced PIVG Performance

2024· article· en· W4404828331 on OpenAlexafffund
Ramy Elaswad, Naser El‐Sheimy, A. A. Mohamad

Bibliographic record

VenueMicromachines · 2024
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputational fluid dynamicsToroidMechanicsParticle (ecology)PhysicsDisplacement (psychology)Particle image velocimetryParticle displacementFlow (mathematics)SettlingParticle tracking velocimetryComputational physicsMaterials sciencePlasmaTurbulenceNuclear physicsOpticsThermodynamics

Abstract

fetched live from OpenAlex

This study investigates the movements of particles in an accelerated toroidal flow channel filled with water, with specific applications for a particle imaging velocimetry gyroscope (PIVG). We used computational fluid dynamics (CFD) to simulate particle behavior under different angular accelerations. These angular accelerations were 4 rad/s2, 6 rad/s2, and 8 rad/s2 for particles densities of 1100 kg/m3, 1050 kg/m3, and 980 kg/m3. An examination was performed on the particles’ concentration distribution, velocity profiles, and displacement patterns with respect to the toroidal geometry, which had a volume fraction of 1.5% and was sized at 50 microns. Our results show that particle density significantly affects behavior and displacement within the toroidal flow, with heavier particles (1100 kg/m3) settling more quickly and concentrating near the lower z values over time, while lighter particles (980 kg/m3) maintain a more uniform distribution. This understanding is crucial for optimizing PIVG accuracy and reliability.

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 score0.475
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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 routes2
Has abstractyes

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