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Record W4410800312 · doi:10.1088/1748-3190/adde07

Characterizing the role of hind flippers in hydrodynamics of a harbor seal

2025· article· en· W4410800312 on OpenAlexafffund
Amirhossein Fardi, Hamayun Farooq, Imran Akhtar, Arman Hemmati, Muhammad Saif Ullah Khalid

Bibliographic record

VenueBioinspiration & Biomimetics · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversity of AlbertaLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStrouhal numberWakeVortex sheddingDragThrustMechanicsVortexKármán vortex streetImmersed boundary methodReynolds numberPhysicsAerospace engineeringEngineeringBoundary (topology)MathematicsTurbulence

Abstract

fetched live from OpenAlex

In this paper, we investigate the hydrodynamic characteristics of harbor seal locomotion, focusing on the role of hind flippers in thrust generation and wake dynamics. Through three-dimensional numerical simulations using an immersed boundary method at Reynolds number of 3000, we analyze the impact of varying Strouhal number (St = 0.2-0.35) and propulsive wavelength (λ∗= 1.0-1.2) on swimming performance. Our findings reveal two distinct wake patterns: a single-row structure at lower Strouhal numbers (St⩽0.25) and a double-row configuration at higher St (St⩾0.3). Increasing wavelength generally enhances thrust production by reducing both pressure and friction of drag components. Additionally, we identify critical vortex interactions between the front and hind flippers, with destructive interference occurring at lower St and constructive patterns emerging at higher St. Circulation analysis confirms stronger vortex formation at higher St andλ∗, particularly during the left stroke phase. These results provide novel insights into the hydrodynamic mechanisms underlying seal locomotion and contribute to our understanding of efficient aquatic propulsion systems.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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 designObservational
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

Citations3
Published2025
Admission routes2
Has abstractyes

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