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Record W4404394118 · doi:10.1063/5.0236386

Study on hydrodynamic characteristics of multiple fish based on smoothed particle hydrodynamics

2024· article· en· W4404394118 on OpenAlexaff
X. J. Wang, Can Huang, Wenhui Yan, Quanliang Zhao, Gaoning He

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsPolytechnique Montréal
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsPhysicsSmoothed-particle hydrodynamicsMechanicsParticle (ecology)Statistical physicsClassical mechanics

Abstract

fetched live from OpenAlex

In this paper, the effect of spatial configurations and quantities of fish school on the hydrodynamic characteristics of fish is numerically investigated by smoothed particle hydrodynamics, revealing the hydrodynamic mechanism of the fish school in terms of shedding vortices and channel effects. In this study, the spatial configuration of fish school includes three types, namely, side-by-side, triangle, and staggered; the number of fish varies from one to six. The pressure, velocity, and vortex contours are discussed to investigate the hydrodynamic parameters of fish school. The results show that the shedding vortex has a greater effect on the hydrodynamic characteristics of fish school than the channel effect; as the longitudinal distance increases, the channel effect rapidly declines while the shedding vortex still generates an effect on the hydrodynamic characteristics of fish school at a relatively big longitudinal distance; the inverted triangular configuration has a stronger channel effect than the positive triangle configuration; the number of fish has a greater impact on the hydrodynamic characteristics of fish located at the back of the fish school than on the hydrodynamic characteristics of fish located at the front of the fish school.

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: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.735

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 head0.013
GPT teacher head0.246
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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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