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Record W2561186920 · doi:10.4172/2168-9873.1000242

Assessment of the Homogeneous Approach to Predict Unsteady Flow Characteristics of Sheet and Cloud Cavitation

2016· article· en· W2561186920 on OpenAlexfundno aff
Patricia Côté, Guy Dumas

Bibliographic record

VenueJournal of Applied Mechanical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCompute CanadaFonds de recherche du Québec – Nature et technologiesUniversité Laval
KeywordsHomogeneousCavitationCloud computingFlow (mathematics)Computer scienceUnsteady flowMechanicsData scienceStatistical physicsPhysics

Abstract

fetched live from OpenAlex

In this work, the homogeneous approach, frequently used to simulate cavitation in hydraulic machinery, is used to compute unsteady cavitating flows for two simplified geometries. After a quick review of the literature and a rigorous presentation of the proposed methodology, the detailed computed physics of sheet and cloud cavitation are compared with experimental observations and with theory. Results suggest that the assumption of a homogeneous medium is not suitable to predict the fine physics of attached cavitation and thus to predict its precise unsteady characteristics. However, the inhomogeneous approach, in which a momentum equation is solved for both phases under a volume of fluid (VOF) approach, is shown to be more promising. Although it is numerically less stable, such an approach allows the effective body to be modified by the presence of vapor in contrast with the homogeneous approach. The resulting flow topology around the vapor cavity is found to better agree with the experimental observations, and thus the inhomogeneous approach offers the potential to better predict the unsteady characteristics of attached cavitation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.370

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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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