The Explicit Algebraic Reynolds Stress Models for Turbulent Flows
Bibliographic record
Abstract
The explicit algebraic Reynolds stress models are obtained from second-order closure models that are valid for three-dimensional turbulent flows in non-inertial frames. The purpose of this present research is to simplify the development of the Reynolds stress anisotropy tensor. This anisotropy stress tensor has seven scalar coefficients and has seven tensor polynomial groups that are the integrity basis for the functions of both symmetric and antisymmetric tensors. This research will also explicitly determine the six independent invariants of the mean strain rate tensor and of the mean rotation rate tensor. The resulting algebraic equation for the anisotropy tensor depends on the choice of the model that is used to determine the dissipation rate and pressure-strain correlation. These equations also represent the slow pressure strain rate and an isotropic dissipation rate tensor of the Rotta model. The results of present research can be compared with the results of Gatski and Speziale that give the complete expression for a traceless symmetric second order tensor which depended on the symmetric and the antisymmetric tensor that involved ten tensor polynomial groups with five independent invariants. The present work reduces the ten tensor polynomial groups down to seven groups which drastically decreases computational time.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".