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Record W4210421207 · doi:10.1063/5.0079404

Equations of state for single-component and multi-component multiphase lattice Boltzmann method

2022· article· en· W4210421207 on OpenAlexaff
Saleh S. Baakeem, Saleh A. Bawazeer, A. A. Mohamad

Bibliographic record

VenuePhysics of Fluids · 2022
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMultiphase flowComponent (thermodynamics)Lattice Boltzmann methodsStatistical physicsPhysicsHagen–Poiseuille equationMechanicsCohesion (chemistry)AzeotropeMixing (physics)ThermodynamicsFlow (mathematics)ChemistryChromatography

Abstract

fetched live from OpenAlex

The lattice Boltzmann method is an alternative method for conventional computational fluid dynamics. It has been used for simulating single-phase and multiphase flows and transport phenomena successfully and efficiently. In the current work, single-component and multi-component multiphase systems are studied. A methodology that differentiates between types of fluids is developed. Moreover, an approach for a multi-component multiphase system is developed in which a single distribution function is used regardless of the number of components. The value of the cohesion parameter (Gf) in the multi-component multiphase model becomes unimportant, like the cohesion parameter (Gp) in the single-component multiphase model, because their effects cancel when calculating the cohesion force. The fluids and mixtures are treated as real, so that mixing rules are used for the mixtures. Several types of fluids and mixtures are considered to investigate the capability of the proposed approach in dealing with miscible mixtures in both azeotrope and non-azeotrope situations. The layered Poiseuille flow and falling droplet on a liquid film are presented to evaluate the model developed. We conclude that this methodology can distinguish between different types of fluids when modeling single-component and multi-component multiphase systems.

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 score0.621
Threshold uncertainty score0.733

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 head0.061
GPT teacher head0.309
Teacher spread0.248 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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