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Record W3118404540

Prandtl Number Dependence of Stratified Turbulence

2019· article· en· W3118404540 on OpenAlexaff
Jesse D. Legaspi, Michael L. Waite

Bibliographic record

VenueUWSpace (University of Waterloo) · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPrandtl numberTurbulenceDownloadMathematicsTurbulent Prandtl numberDistribution (mathematics)Statistical physicsEconometricsPhysicsMechanicsMathematical analysisComputer scienceReynolds numberWorld Wide WebHeat transfer
DOInot available

Abstract

fetched live from OpenAlex

Stratified turbulence is affected by buoyancy forces that suppress vertical motion, resulting in a horizontally-layered structure with quasi-two-dimensional vortices. The Prandtl number Pr quantifies the relative strengths of viscosity and buoyancy diffusivity, which damp small-scale fluctuations in velocity and buoyancy at different microscales. Direct numerical simulations (DNS) must resolve the smallest flow features, requiring very high resolution if Pr is large. In most oceanic and atmospheric flows, Pr>1; e.g. Pr=7 in heat-stratified water, and Pr=700 in salt-stratified water. To reduce the computational demand in simulations of ocean flows and lab experiments of stratified turbulence, Pr=1 is often assumed, possibly introducing discrepancies between the DNS and real geophysical stratified turbulence. In this thesis, we explore how stratified turbulence is affected by varying Pr. DNS of homogeneous forced stratified turbulence with a fixed viscosity and Pr= 0.7, 1, 2, 4, and 8 are performed for different stratification strengths by changing the buoyancy frequency N, for a range of Froude numbers Fr_h from 0.009 to 0.1, and buoyancy Reynolds numbers Re_b from 0.5 to 32. Energy spectra, buoyancy flux spectra, spectral energy flux, and snapshots of physical space fields are compared as Pr increases to explore scale-specific Pr-sensitivity. Probability density functions and statistical moments of velocity component and temperature derivative fields are also compared to the Pr=1 findings. Small-scale Pr-dependence was found in the kinetic energy spectra that extended further upscale as stratification increased. The potential energy and potential energy flux exhibited more prominent Pr-sensitivity, extending into the large horizontal scales for some stratifications. Interestingly, the kinetic energy flux exhibited no Pr-dependence at small scales. The buoyancy flux was most sensitive to Pr in the small scales, except for the most strongly stratified case, which notably had Re_b<1. As Pr increased, all spectra showed a pattern of diminishing increase, suggesting eventual convergence to a limiting spectra shape at large but finite Pr. The spectra in the most strongly stratified case, where Re_b<1, were very different from the rest, suggesting regime-dependence of Pr-sensitivity (i.e. whether Re_b<1, or Re_b>1). \nThe probability density functions and statistical moments for Pr different from 1 were consistent with previous work for Pr=1. \nIncreasing Pr significantly affected the temperature derivative fields while the velocity component derivative fields were mostly unchanged. These findings suggest that, for DNS of stratified turbulence in fluids with Pr>1, the assumption of Pr=1 does not produce realistic results: the Pr-sensitivity at intermediate, and in some cases, large horizontal scales must be considered for accurate stratified turbulence DNS, though the excessive computational demand can be prohibitive.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.167
Teacher spread0.162 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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