Targeted turbulent structure control in wall-bounded flows via localized heating
Bibliographic record
Abstract
A targeted turbulent flow control strategy, based on selective heating of streamwise-aligned heat strips, is assessed for drag reduction using direct numerical simulations of variable viscosity and compressible turbulent channel flows. As increasing the temperature of a gas increases its viscosity, heating is generally an unfavorable drag mitigation approach. However, through a selective spatial arrangement of the heating array, the slight increase in viscosity and decrease in density can serve to modify the organization of the streamwise-aligned structures and the likelihood of the ejection and sweep events near the wall. This can, under specific conditions, lead to a very modest drag reduction. The optimal spatial arrangement is identified using a bidimensional empirical mode decomposition and targets the near-wall, large-scale turbulent motion. The drag coefficient, at constant mass flow rate, remains unchanged with heating despite up to an 11% increase in the local viscosity above the heating strips. When accounting for the viscosity variation in the drag reduction calculation, an effective drag reduction of 6% is observed.
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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 | 0.000 | 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 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".