Toward a Machine Learning-Facilitated LES of Turbulent Flow Around a Solid Body
Bibliographic record
Abstract
In this research, we introduce a machine-learning framework aimed at refining the dynamic Smagorinsky model used in LES. This study analyzes two approaches: a multi-layer feedforward artificial neural network, which is applied in a point-wise fashion, and a convolutional neural network (CNN), which processes selectively chosen snapshots of fluid dynamics. The primary objective is to explore the potential of a data-driven methodology for the accurate determination of the Smagorinsky coefficient, which is crucial for effective turbulence modelling. Our machine learning models were rigorously tested by comparing their performance against the traditional dynamic Smagorinsky model, particularly in scenarios involving flow past a sphere at a Reynolds number of 103. This comparison focused on evaluating different neural network architectures and their dependence on the volume of training data and the nature of the input variables used. We conducted a detailed analysis of the results, where the correlation between the true and predicted stress tensors was calculated to assess model accuracy. The cross-correlation coefficient demonstrated that the model achieves acceptable precision for simulations at a Reynolds number of 103. Furthermore, our study highlights that the incorporation of velocity gradient information and resolved stress components into the convolutional neural networks significantly enhances the capability of the model to handle the LES of flows with massive separation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".