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Record W2321501397 · doi:10.1115/fedsm2014-21928

Numerical Simulation of Cavitating Flow Around a Hydrofoil

2014· article· en· W2321501397 on OpenAlexaff
Tan Dung Tran, B Nennemann, Thi Cong Vu, François Guibault

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsHydro-QuébecAndritz (Canada)Polytechnique Montréal
Fundersnot available
KeywordsCavitationReynolds-averaged Navier–Stokes equationsTurbulenceMechanicsReynolds numberTurbulence modelingComputational fluid dynamicsCompressibilityMaterials sciencePhysics

Abstract

fetched live from OpenAlex

The objective of this paper is to evaluate the applicability of different cavitation models and determine appropriate numerical parameters for cavitating flows around a hydrofoil. The simulations are performed for a NACA 66 foil at 6 degrees angle of attack, Reynolds number of 750 000 and for a cavitation number of 1.49 corresponding to the partial sheet cavitating regime. The incompressible, multiphase Reynolds-averaged Navier-Stokes (RANS) equations are solved by the CFD solver CFX with Kubota and Merkle cavitation models. As part of the work, the Merkle model is implemented into CFX by User Fortran code because this model has shown good cavitation prediction capability according to the literature. The effects of the k-ε and SST turbulence models on the cavitating flow dynamics are compared. Also, an investigation on structured and hybrid meshes with different mesh sizes and concentrations is carried out in order to better understand the mesh influence for this cavitation simulation. The local compressibility effect is considered by correcting the turbulent eddy viscosity inside the mixture vapor/liquid zones. The numerical results are validated by experiments conducted in a cavitation tunnel at the French Naval Academy.

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: Empirical · Consensus signal: none
Teacher disagreement score0.631
Threshold uncertainty score0.301

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 head0.009
GPT teacher head0.227
Teacher spread0.219 · 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
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

Citations2
Published2014
Admission routes1
Has abstractyes

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