ТЕОРИЯ ТУРБУЛЕНТНОСТИ И МОДЕЛЬ ВЛИЯНИЯ ПЛОТНОСТИ ШЕРОХОВАТОСТИ TURBULENCE THEORY AND ROUGHNESS DENSITY EFFECT MODEL Трунев Александр Петрович к. ф.-м. н., Ph.D., директор
Bibliographic record
Abstract
A&E Trounev IT Consulting, Toronto, Canada В работе представлена модель турбулентного по-граничного слоя над шероховатой поверхностью . Модель основана на специальном преобразовании уравнения Навье -Стокса . Турбулентный погранич-ный слой в этой модели рассматривается как тече-ние над шероховатой поверхностью , генерируемой вязким подслоем ( эффект динамической шерохо-ватости ). Дана оценка влияния шероховатой стен-ки на параметры логарифмического профиля в случае 2D и 3D элементов шероховатости The model of the turbulent boundary layer over a rough surface is presented. The model is based on the special type of transformation of the Navier-Stokes equation. The turbulent boundary layer in this model is considered as a flow above the rough surface gener-ated by the viscous sublayer (the dynamic roughness effect). The roughness density effect on the shift of the mean velocity logarithmic profile has been estimated in the case of 2D and 3D roughness elements Ключевые слова : ТУРБУЛЕНТНЫЙ ПОГРАНИЧНЫЙ СЛОЙ , ЛОГАРИФМИЧЕСКИЙ ПРОФИЛЬ , ПЛОТНОСТЬ ШЕРОХОВАТОСТИ Keywords: TURBULENT BOUNDARY LAYER, LOGARITHMIC PROFILE, ROUGHNESS DENSITY EFFECT
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".