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Record W1978152667 · doi:10.1115/1.4004024

On the Modeling and Simulation of Ion Drag Electrohydrodynamic Micropumps

2011· article· en· W1978152667 on OpenAlexaff
S. M. Mozammil Hasnain, A. Bakshi, P. Ravi Selvaganapathy, C.Y. Ching

Bibliographic record

VenueJournal of Fluids Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectrohydrodynamicsMicropumpMechanicsElectric fieldDragMicrochannelPoisson's equationElectric potentialBoundary value problemMaterials sciencePhysicsVoltageElectrical engineeringEngineeringNanotechnology

Abstract

fetched live from OpenAlex

A numerical model for ion-drag electrohydrodynamic (EHD) micropumps has been developed. The Poisson and charge conservation equations are solved to determine the electric body force within the flow domain. The charge distribution at the electrodes is assumed to depend on the magnitude and the gradient of the electric field at the surface of the electrode. The flow field is then determined by solving the momentum equation with the inclusion of the electric body force. Simulations were performed for micropump configurations that consisted of a series of planar electrode pairs embedded along the bottom wall of a microchannel. A two-dimensional segment of the channel with a single electrode pair is simulated using periodic boundary conditions at the inlet and outlet for the charge and electric fields. An empirical model was developed to estimate the charge boundary condition for the simulations. The simulation results were in good agreement with existing experimental data. The model was then used to perform a parametric study of the effect of channel height on the pump performance.

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: none
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.008
GPT teacher head0.184
Teacher spread0.176 · 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

Citations13
Published2011
Admission routes1
Has abstractyes

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