Turbulence energy cascade: relating turbulent scales with vehicle scales
Bibliographic record
Abstract
Transport involves a vehicle moving through a fluid, routinely invoking significant turbulence. The changes in the flow regime, the drag coefficient, the Strouhal number, and others can be expressed in terms of the Reynolds number. These relationships, however, may be significantly altered depending on the characteristics of the turbulent flow. Other than the salient turbulence intensity effect, the scales of turbulence and of the vehicle can also play some noticeable roles. This is particularly the case when the turbulence scales are of the order of the scale of the vehicle, such as the boundary-layer thickness and the wake size. The impacts of the relative scales tend to increase with turbulent intensity or, more specifically, the intensity of the turbulent scale in play. The influence of a turbulent scale can be profound when it interacts with, or is in tune with, a sensitive factor such as a flow transition or separation point. A small shift in a sensitive factor can amplify into a big end effect. This is the introductory chapter to the Flow Turbulence in Engineering Transport volume. It aims to provide the conceptual framework underlying transport vehicle aerodynamics in turbulent flow, considering the turbulence energy cascade.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".