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Record W4392599123 · doi:10.5194/egusphere-egu24-4662

Generation of internal wave breathers by steady flow over an obstacle

2024· preprint· en· W4392599123 on OpenAlexaff
Kevin G. Lamb

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBreatherObstacleFlow (mathematics)MechanicsPhysicsClassical mechanicsBusinessPolitical scienceNonlinear systemQuantum mechanics

Abstract

fetched live from OpenAlex

In certain stratifications internal wave breathers can exist. Such stratifications include symmetric double-pycnocline continuous stratifications provided the two pycnoclines are sufficiently far apart. Here the generation of breathers by steady flow over a small obstacle is investigated using a two-dimensional non-hydrostatic primitive equation numerical model under the Boussinesq approximation. The symmetric stratification consists of two thin hyperbolic tangent pycnoclines which separate the fluid into three nearly constant density layers. The two pycnoclines have the same thickness and the same density jump across them. The upper and lower constant density layers have the same thickness. Nonlinear internal waves in this type of stratification can be modelled with the modified KdV (mKdV) equation. If the pycnoclines are far enough apart the cubic coefficient in the mKdV equation is positive and weakly-nonlinear theory predicts the existence of breathers. In this numerical study a large number of simulations have been undertaken, varying the speed of the upstream flow, the amplitude and width of the obstacle and the thickness of the upper and lower layers. Multiple upstream propagating internal wave breathers are generated in a small region of parameter space. Their formation appears to arise from nonlinear-interactions in the lee wave field downstream of the obstacle. 

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 score0.038
Threshold uncertainty score0.826

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 head0.051
GPT teacher head0.283
Teacher spread0.232 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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