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Record W2099928850 · doi:10.1175/1520-0485-32.9.2441

An Analytical Model of Wave Bottom Boundary Layers Incorporating Turbulent Relaxation and Diffusion Effects

2002· article· en· W2099928850 on OpenAlexaff
Qingping Zou

Bibliographic record

VenueJournal of Physical Oceanography · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTurbulenceTurbulence modelingMechanicsViscoelasticityBoundary layerTurbulent diffusionShear velocityShear stressEddy diffusionPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract To calculate the effects of turbulent relaxation on oscillatory turbulent boundary layers, a viscoelastic term is added to an eddy viscosity model. The viscoelastic term parameterizes the lag of turbulent properties in response to imposed oscillatory shear and is proportional to the ratio between the timescales of eddy dissipation and of the oscillating flow. It is found that the turbulent relaxation plays an important role in the phase variations of velocity and shear stress with elevation, and that it decreases the friction factor and the phase lead of bed shear stress over free stream velocity. To assess the effects of turbulent diffusion in this problem, the viscoelastic model is extended by further introducing a turbulent diffusion term in the model. The comparisons between these two models indicate that turbulent diffusion significantly reduces the magnitudes of shear stress and velocity perturbation in the outer region of the boundary layer. It is also found that the effects of turbulent relaxation and diffusion increase with increasing relative roughness. As a result, the analytical solutions demonstrate an overall improvement over the eddy viscosity model in predicting the observed temporal evolution of velocity and shear stress profiles; this improvement is more distinct for rough beds than smooth beds.

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 score0.861
Threshold uncertainty score0.326

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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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

Citations15
Published2002
Admission routes1
Has abstractyes

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