Mean Velocity Scaling of High-Speed Turbulent Flows Under Nonadiabatic Wall Conditions
Bibliographic record
Abstract
The problem of scaling the near-wall mean velocity profiles of turbulent flows and collapsing them with the law of the wall has been traditionally studied using the conservation of momentum, by employing various levels of assumptions. In the van Driest transformation (“Turbulent Boundary Layer in Compressible Fluids,” Journal of the Aeronautical Sciences, Vol. 18, No. 3, 1951, pp. 145–216), the viscous stress was neglected, whereas in the Trettel and Larsson transformation (“Mean Velocity Scaling for Compressible Wall Turbulence with Heat Transfer,” Physics of Fluids, Vol. 28, No. 2, 2016, Paper 026102), the Reynolds stress was assumed to cancel out. Recent work by Griffin, Fu, and Moin (“Velocity Transformation for Compressible Wall-Bounded Turbulent Flows with and Without Heat Transfer,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 118, No. 34, 2021, Paper e2111144118) used a quasi-equilibrium assumption between turbulence production and dissipation in the log layer and demonstrated success for a wide variety of canonical flows. However, the extent of quasi-equilibrium is not verified, particularly for noncanonical flows. In this work, an alternate transformation is developed using the semilocal gradient of the van Driest transformed velocity, which is principally tied to the dynamics of vorticity transport in the boundary layer. In addition, a modified stress balance is utilized to directly account for and scale the buffer region. The transformation was benchmarked using a similar dataset as in Griffin et al., and comparable performance was obtained. Moreover, at supercritical pressures, the present transformation is found to produce significantly better collapse than existing works.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".