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Record W4399045170 · doi:10.1080/10618562.2024.2351897

Flow Around a Pile Under Highly Non-linear Waves Using an Unstructured Finite-Volume Technique

2023· article· en· W4399045170 on OpenAlexaff
Wei Zhang, Miguel Uh Zapata, Ziheng Huang, Kim Dan Nguyen, Damien Pham Van Bang

Bibliographic record

VenueInternational journal of computational fluid dynamics · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsFinite volume methodMechanicsPileVolume (thermodynamics)Flow (mathematics)Volume of fluid methodGeologyPhysicsGeotechnical engineeringThermodynamics

Abstract

fetched live from OpenAlex

This study has coupled a wave generating model with the existing in-house 3D Large Eddy Simulation (LES) hydrodynamic model. The primary focus is to simulate the hydrodynamics around a circular cylinder under non-linear waves. To accurately capture the behaviour of non-linear waves, a relaxation method is implemented on an unstructured grid, and its effectiveness is examined in a numerical wave tank with a vertical cylinder. To validate the accuracy of the proposed model, we compare the simulation results with experimental data, specifically analysing the transmission of the wave surface across the cylinder and examining the phased-averaged pressure distribution. The simulations also consider the shedding of lee-wake vortices, and the results are extensively discussed. Significantly, this paper represents the first known investigation of hydrodynamics under non-linear waves using the large-eddy simulation technique on unstructured grids. This approach opens up new possibilities for studying complex wave-structure interactions with improved accuracy and realism.

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 score0.005
Threshold uncertainty score0.010

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 communication0.0010.000
Open science0.0010.001
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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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