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Simulation of oil spreading flows in water by mesh-free improved EI-MPS method

2025· article· en· W7082398150 on OpenAlexaff

Bibliographic record

VenueOcean Engineering · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFlow (mathematics)Computer simulationComputational fluid dynamicsOil productionPetroleum

Abstract

fetched live from OpenAlex

Oil spreading in water as two-phase flows can trigger severe interface deformation and breakup, leading to complex flow structures. In this study, an Explicit Incompressible version of the Moving Particle Semi-implicit (EI-MPS) method is employed to simulate the oil-water two-phase flows. By using the mesh-free method, different pressure gradient models are examined to simulate the flows. Numerical simulations are conducted to simulate oil slick spreading and continuous oil leakage spreading. In the simulations, different particle distances are implemented. The simulated interface evolution and oil front movements are analyzed. It shows that the first-order accuracy pressure gradient model can significantly improve the simulation results, and using smaller particle distances can better reflect the oil spreading in the water. By using the first-order accuracy pressure gradient model, the oil spreading by different initial oil heights in the oil slick and continuous oil leakage spreading is captured, in good agreement with experimental measurements.

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: Methods · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.391

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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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