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Record W4404377258 · doi:10.1063/5.0234476

Impact of depth-ratio on shear-layer dynamics and wake interactions around wall-mounted prisms

2024· article· en· W4404377258 on OpenAlexaff
Shubham Goswami, Arman Hemmati

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysicsWakeDynamics (music)MechanicsShear (geology)Classical mechanicsComposite materialAcoustics

Abstract

fetched live from OpenAlex

This numerical investigation explores the flow dynamics around wall-mounted prisms with small aspect-ratio (AR=0.25−1.5) and changing depth-ratio (streamwise length, DR=1−4) at a Reynolds number of Re=1000−2500. This study focuses on understanding the formation and evolution of Kelvin–Helmholtz Instability (KHI) and its interactions with coherent wake structures, e.g., hairpin-like vortices. Additionally, it examines the influence of depth-ratio on prism surface pressure distribution and the origin of pressure fluctuations. The results, driven from the extreme geometrical cases of AR=1, DR=1 and 4 at Re=2500, reveal distinct KHI rollers originating from the leading edge shear layer. These impact prism surface pressure distribution and contribute to downstream wake structures. Interactions between KHI rollers and coherent wake structures are more pronounced for larger depth-ratio prisms, leading to a complex wake system. These interactions are quantified using turbulence–mean-shear interaction and turbulence–turbulence interaction from analyzing the Poisson equation. Cross-spectral density analysis highlights the influence of KHI rollers on coherent structures in the wake. These findings emphasize the significance of depth-ratio in shaping prism flow dynamics.

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 score0.418
Threshold uncertainty score0.522

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 head0.287
Teacher spread0.274 · 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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