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Record W4403540119 · doi:10.1063/5.0235072

A smoothed particle hydrodynamics method for two-phase electrohydrodynamics modeling with Nernst–Planck equations

2024· article· en· W4403540119 on OpenAlexaff
Mehran Vakilha, Mostafa Safdari Shadloo

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversité de Sherbrooke
FundersRégion Normandie
KeywordsPhysicsElectrohydrodynamicsPlanckNernst equationSmoothed-particle hydrodynamicsClassical mechanicsMechanicsParticle (ecology)Statistical physicsQuantum mechanicsElectric field

Abstract

fetched live from OpenAlex

The widely used leaky dielectric model often overlooks the rate of change in electric charges, leaving the impact of the charge conservation mechanism on two-phase electro-hydrodynamics (EHD) flows inadequately explored. In this study, we address this gap by introducing a charge-conservative model (CCM) for simulating such EHD systems within the framework of the smoothed particle hydrodynamics (SPH) method. Our methodology employs a fully explicit incompressible SPH (EISPH) approach to discretize the pressure Poisson, the electric potential Poisson, and the Nernst–Planck (N–P) equations. This work presents two notable contributions: (i) the introduction of the charge-conservative model into the incompressible SPH framework and (ii) the achievement of its discretization through a fully explicit methodology. To validate the proposed CCM, we conduct a comprehensive comparison with analytical solutions, as well as existing numerical and experimental results. The results affirm that the CCM consistently produces accurate outcomes across various test cases.

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: none
Teacher disagreement score0.809
Threshold uncertainty score0.760

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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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