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Record W2789214168 · doi:10.1139/cjp-2017-0606

Effect of intermediate fluid layer on Kelvin–Helmholtz instability

2018· article· en· W2789214168 on OpenAlexvenueno aff
Ying Zhang, Wenqiang Shang, Mengjun Yao, Boheng Dong, Peisheng Li

Bibliographic record

VenueCanadian Journal of Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsnot available
FundersNatural Science Foundation of Jiangxi ProvinceNational Natural Science Foundation of China
KeywordsInstabilityPhysicsMechanicsGrowth rateCompressibilityFluid dynamicsFlow (mathematics)Geometry

Abstract

fetched live from OpenAlex

Two-dimensional K-H (Kelvin–Helmholtz) instability of the three-component immiscible fluids with an intermediate fluid layer is numerically studied using the front-tracking method (FTM). The instability is governed by the Navier–Stokes equations and the conservation of mass equation for incompressible flow. A finite difference method is used to discretize the governing system. This study focuses on the influence of flow configuration, the thickness of intermediate fluid layer and the distribution of intermediate fluid layer on K-H instability. It is shown that the larger the initial horizontal velocity difference is, the faster the internal disturbance increases, and the characteristic form of K-H instability becomes more obvious for different flow configuration. It is also observed that the thickness of the intermediate fluid layer is negatively correlated to the billow height and the numerical growth rate. In addition, when the intermediate fluid layer is thicker than 0.4 times the disturbance wavelength, the billow height and the numerical growth rate for the K-H instability of the upper and lower interfaces change over time synchronously. The higher the initial height of the lower interface is, the greater the growth rate and billow height of the upper interface are. Besides, the upper and lower interfaces are rolled up synchronously over time when the intermediate fluid layer is symmetrically distributed with y = 0.5 in the fluid system.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.418

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 head0.014
GPT teacher head0.248
Teacher spread0.233 · 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 designBench or experimental
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

Citations4
Published2018
Admission routes1
Has abstractyes

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