Time-averaging and temporal-filtering in wall-modeled large eddy simulation
Bibliographic record
Abstract
A turbulent channel flow at a Reynolds number of Reτ=2000 is solved based on the spatially filtered Navier–Stokes equations using large eddy simulation and an in-house code. A nonequilibrium wall model is implemented to predict the flow in the wall layer based on the Reynolds-averaged approach. To mitigate the log-layer mismatch, which is often encountered in wall modeling, two temporal schemes are introduced to average the wall layer solution and to filter the flow information input to the wall layer. It is found that the time periods used for the time-averaging and temporal-filtering schemes affect the performance of the wall model. The results show that shorter time periods enable the wall model to respond to the flow structures in the outer layer and correctly predict the friction velocity. However, the prediction of the friction velocity also depends on the location of the matching point. Locating the matching point further from the wall results in better performance due to the compatibility of the subgrid scale model with the grid resolution further from the wall. The temporal-filtering scheme is used to remove nonessential high-frequency wavelengths that can disturb the functionality of wall modeling. Various combinations of the time-averaging and temporal-filtering time periods are investigated for different locations of the matching point. Overall, it is concluded that using a shorter period for time-averaging and a temporal-filtering period comparable to the turbulent diffusion timescale leads to improved results.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".