A-priori evaluations of subgrid-scale terms for large-eddy simulation of compressible turbulent flows
Bibliographic record
Abstract
The subgrid-scale terms for different formulations of the energy equation are evaluated from a-priori tests using the direct numerical simulation (DNS) data of a compressible mixing layer at a moderate Mach number of M = 0.65. To extend the generality of the results, the simulations were performed with three different initial conditions for the velocity fields. To examine the impact of strong temperature variations on the subgrid scales, a non-isothermal mixing layer with lower to upper free-stream temperature ratio of 3 is also considered. For cold simulations, with equal free-stream temperatures, the total energy equation is shown to be the best choice in view of the accuracy and the subgrid-scale modelling requirements. For hot simulations, with the free-stream temperature ratio equal to 3, the total enthalpy equation is found to be the best formulation for the energy equation. Furthermore, it is shown that the subgrid-scale pressure dilatation term, which has been largely neglected so far, is of the same order of the subgrid-scale heat flux. Based on the present results, the contribution of the subgrid-scale pressure dilatation can be up to 46% of the total sugbrid-scale activity. Moreover, the time evolutions of the volume-average mean kinetic energy, turbulent kinetic energy, production, dissipation, and pressure dilatation terms are considered. Unlike the subgrid-scale pressure dilatation term, the volume-average pressure dilatation terms are negligible, and compressibility does not affect the large-scale evolutions of the mean and turbulent kinetic energies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".