A numerical study of the subgrid-scale fluid motion in turbulent flows
Bibliographic record
Abstract
The numerical solution of the Navier–Stokes equations for different turbulence regimes has become more prevalent for many years. This thesis has investigated the large eddy simulation method for solving the Navier–Stokes equations. A primary goal is to design a subgrid-scale model for small-scale coherent vortices while statistically maintaining an accurate dissipation rate. Past investigations of turbulence indicate that the vortex stretching mechanism can transport the turbulence kinetic energy from large to small scales. Thus, a turbulence model can learn the energy dissipation rate from the statistics of the velocity gradient tensor. Following such a hypothesis, this thesis validates how vortex stretching can directly account for the subgrid-scale dissipation rate while solving the Navier–Stokes equations on a relatively coarse mesh. Current findings suggest a potential subgrid-scale model based on invariants of the square of the velocity gradient tensor. The turbulence statistics obtained from the proposed model agree well with the three commonly used dynamically adaptive large eddy simulation techniques. The results also suggest that statistics of the velocity gradient tensor dynamically adapt the dissipation rate to the local variation of turbulence. Furthermore, considering the square of the deformation tensor, this thesis suggests that the singular values of the snapshots of the velocity gradient may improve the model in future studies of more challenging turbulent flows.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".