Bibliographic record
Abstract
Abstract It is known that the drag for flows driven by a pressure gradient in heated channels can be reduced below the level found in isothermal channels. This reduction occurs for spatially modulated heating and is associated with the formation of separation bubbles which isolate the main stream from direct contact with the solid wall. It is demonstrated that the use of a proper combination of spatially distributed and spatially uniform heating components results in an increase in the horizontal and vertical temperature gradients which lead to an intensification of convection which, in turn, significantly increases the drag reduction. An excessive increase of the uniform heating leads to breakup of the bubbles and the formation of complex secondary states, resulting in a deterioration of the system performance. This performance may, under certain conditions, still be better than that achieved using only spatially distributed heating. Detailed calculations have been carried out for the Prandtl number $\mathit{Pr}=0.71$ and demonstrate that this technique is effective for flows with a Reynolds number $\mathit{Re}<10$ ; faster flows wash away separation bubbles. The question of net gain remains to be settled as it depends on the method used to achieve the desired wall temperature and on the cost of the required energy. The presented results provide a basis for the design of passive flow control techniques utilizing heating patterns as controlling agents.
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 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) | 0.002 | 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 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".