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Record W4391531244 · doi:10.11159/jffhmt.2024.003

Numerical Study of a Planar Micromixer with Circular and Fin Obstacles

2024· article· en· W4391531244 on OpenAlexvenueno aff
Md Readul Mahmud

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMicromixerFinPlanarMechanicsMaterials scienceComputer sciencePhysicsNanotechnologyComposite materialMicrofluidicsComputer graphics (images)

Abstract

fetched live from OpenAlex

The design and characterization of a passive planar O mixer with two different kinds of barriers to improve mixing performance are reported in this study.The computational fluid dynamics (CFD) program ANSYS 15 is used to perform computational studies on mixing and fluid flow in microchannels over a broad range of Reynolds numbers, from 1 to 100.The outcomes demonstrate that the O mixer with obstacles performs significantly better at mixing than the O mixer without obstacles.The explanation is that obstacles cause the fluid path length to increase, giving the fluids more time to diffuse.The O mixer with circular and fin obstacles is three times more efficient than the O mixer without obstacles in all scenarios.Due to the absence of any obstructions within the channel, the O mixer has the lowest pressure drop.The O mixer with circular & fin obstacles is the most economical one since it has the lowest mixing cost, which is a crucial feature for incorporation into intricate, cascading microfluidic systems.The proposed O mixer with obstacles can be easily manufactured and integrated into devices for a variety of macromixing applications due to its low mixed cost and simple planar construction.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.337

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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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 designBench or experimental
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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