Применение двухпараметрической к ю-модели турбулентности для исследования явления термобара
Bibliographic record
Abstract
In this paper, the phenomenon of the thermal bar in Kamloops Lake (Canada) is studied with a nonhydrostatic mathematical model. A thermal bar is a narrow zone in a lake in temperate latitudes where maximum-density waters sink from the surface to the bottom. Two different turbulence models are compared: the algebraic model of Holland P. R. et al. [1] and the two-equation k-ю model of Wilcox D.C. [2]. The two-parameter model of turbulence developed by D.C. Wilcox consists of equations for turbulence kinetic energy (k) and specific dissipation rate (ro).The mathematical model which includes the Coriolis force due to the Earth''s rotation, is written in the Boussinesq approximation with the continuity, momentum, energy, and salinity equations. The Chen-Millero equation [8], adopted by UNESCO, was taken as the equation of state. The formulated problem is solved by the finite volume method. The numerical algorithm for finding the flow and temperature fields is based on the Crank-Nicholson difference scheme. The convective terms in the equations are approximated by a second-order upstream QUICK scheme [10]. To calculate the velocity and pressure fields, the SIMPLED procedure for buoyant flows [11], which is a modification of the well-known Patankar''s SIMPLE method [9], has been developed. The systems of grid equations at each time step are solved by the under-relaxation method or N.I. Buleev''s explicit method [12]. The turbulence models were applied to predict the evolution of the spring thermal bar in Kamloops Lake. The numerical experiments have shown that the application of the k-ю turbulence model leads to new effects in the thermal bar evolution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".