A mixed Lagrangian-Eulerian approach for numerical simulation of mixing layers and uniformly sheared flow.
Bibliographic record
Abstract
The vortex-in-cell (VIC) method is applied for the prediction of the turbulent characteristics and flow development of the two-dimensional spatially growing mixing layer, the two-dimensional uniformly sheared flow and the three-dimensional uniformly sheared flow. The VIC method has the advantage that it requires less computational tune compared with the Lagrangian method. In the first part of thesis, the two-dimensional VIC method is validated by simulating a spatially growing mixing layer. The results show that the VIC method is capable of predicting the characteristics of the spatially growing mixing layers as well as the grid-free Lagrangian method. In the second part of thesis, the two-dimensional VIC method is used to simulate the uniformly sheared flow. In this new approach a combination of several adjacent mixing layers simulate the initial condition used to generate the uniformly sheared flow. Turbulent characteristics such as the mean velocity, the r.m.s. longitudinal and lateral velocity fluctuations and the Reynolds shear stress are predicted and compared with previous numerical and experimental works. In the third part of this work, three-dimensional simulation of uniformly sheared flows is performed as an extension of the two-dimensional simulation in order to take into account the effect of stretching which is a major contributor to turbulence production. The methodology is based on a mixed Lagrangian-Eulerian three-dimensional vortex-in-cell method. Histograms and two-angle probability distribution of the inclination angle of the vorticity vectors at the grid points with the horizontal plane indicate the presence of vortical structures at a 35°--40° angle with horizontal plane which is consistent with the results of Rogers and Moin (1987). The time evolution of the component energy ratios K11, K22 and K33 are calculated and compared with the previous works. Sensitivity to the numerical parameters is investigated and the results exhibited robustness to the numerical parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".