Correlation for Transitional Reynolds Number and Assessment of RANS for Bypass Transition
Bibliographic record
Abstract
Abstract Direct numerical simulations (DNSs) of bypass transition are performed for zero-pressure-gradient flat-plate boundary layers with inlet freestream turbulence intensity (FSTI) ranging from 0.75% to 6%. The DNS database exhibits excellent agreement with the Blasius solution in the laminar region, with deviation occurring only near transition onset, providing improved fidelity compared to previous computational studies. A novel skin-friction-based intermittency definition is proposed and validated against conventional temporal intermittency measurements, demonstrating equivalent transition prediction capability without requiring time-resolved data. Using this definition, a new transition Reynolds number correlation is developed that incorporates the effects of FSTI, turbulent length scales, and intermittency threshold. The correlation reduces to the classical Abu-Ghannam and Shaw formulation at zero intermittency and achieves 16.2% average error when validated against independent experimental data from Fransson and Shahinfar (2020). Reynolds-averaged Navier–Stokes (RANS) simulations using the k−ω shear-stress transport (SST) and γ−Reθ transition models reveal significant sensitivity to inlet length scale specification, with integral length scale predictions deviating from DNS in both pretransitional and transitional regions. The transported intermittency in RANS shows wall-normal-dependent transition onset that differs from the skin-friction-based definition. This comprehensive DNS database and associated correlations provide improved benchmarks for transition model development and validation.
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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".