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Record W4251313412 · doi:10.1504/pcfd.2018.094621

A numerical study of Rayleigh-Taylor instability for various Atwood numbers using ISPH method

2018· article· en· W4251313412 on OpenAlexaff
Amin Rahmat, Nima Tofighi, Mehmet Yıldız

Bibliographic record

VenueProgress in Computational Fluid Dynamics An International Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInstabilitySmoothed-particle hydrodynamicsMechanicsSurface tensionRayleigh–Taylor instabilityPhysicsMomentum (technical analysis)Bounded functionViscosityClassical mechanicsRayleigh scatteringStatistical physicsMathematical analysisMathematicsThermodynamics

Abstract

fetched live from OpenAlex

In this paper, the wall bounded single-mode Rayleigh-Taylor instability (RTI) for a two-phase immiscible fluid system in a confined domain is investigated numerically for various Atwood numbers ranging from At = 0.2 to At = 0.8. Governing equations are discretised using the smoothed particle hydrodynamics (SPH) method. A robust numerical scheme is used to simulate the RTI phenomenon and in order to model the fluid-flow in the vicinity of the interface, transport parameters such as density and viscosity are smoothed using colour function. The surface tension force is coupled to the momentum equation using continuum surface force (CSF) model. It is shown that in general the RTI evolves in three distinct stages, namely linear stability, mushroom-head formation and long-term evolution. The growth rate in the first stage, i.e. the linear instability, shows good agreement with the analytical solution in the literature. The qualitative and quantitative results of second and third stages are introduced and relevant discussions are made.

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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.361
Teacher spread0.338 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProgress in Computational Fluid Dynamics An International JournalSame topicFluid Dynamics Simulations and InteractionsFrench-language works237,207