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Record W2765099882 · doi:10.1115/1.4038242

Investigation of Wave Characteristics in Oscillatory Motion of Partially Filled Rectangular Tanks

2017· article· en· W2765099882 on OpenAlexaff
Murat Özbulut, Nima Tofighi, Ömer Gören, Mehmet Yıldız

Bibliographic record

VenueJournal of Fluids Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversity of Victoria
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsDiscretizationSmoothed-particle hydrodynamicsRobustness (evolution)MechanicsKinematicsDisplacement (psychology)Equations of motionNumerical analysisPhysicsMotion (physics)Classical mechanicsMathematical analysisMathematics

Abstract

fetched live from OpenAlex

Simulations of oscillatory motion in partially filled rectangular tanks with different tank geometries, fullness ratios, and motion frequencies are presented. Smoothed particle hydrodynamics (SPH) method is used to discretize the governing equations together with new velocity variance-based free surface (VFS) and artificial particle displacement (APD) algorithms to enhance the robustness and the accuracy of the numerical scheme. Two-dimensional (2D) oscillatory motion is investigated for three different scenarios where the first one scrutinizes the kinematic characteristics in resonance conditions, the second one covers a wave response analysis in a wide range of enforced motion frequencies, and the last one examines the dynamic properties of the fluid motion in detail. The simulations are carried on for at least 50 periods in the wave response analysis. It is shown that numerical results of the proposed SPH scheme are in match with experimental and numerical findings of the literature.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.217
Teacher spread0.202 · 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

Citations30
Published2017
Admission routes1
Has abstractyes

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