Transition to turbulent boundary layer for heat transfer enhancement from a tube using inverted flags
Bibliographic record
Abstract
Inverted flags have emerged as a promising solution for enhancing convective heat transfer. This paper investigates the heat transfer enhancement potential of an inverted flag attached to a tube in crossflow. Experimental tests were conducted to analyze the impact of flag dimensions on heat transfer and pressure drop. Additionally, the study proposes a semi-analytical model based on integral methods to solve the governing equations for fluid flow and heat transfer around the tube. The model incorporates boundary layer attachment and transition effects. Results indicate that boundary layer attachment due to flag-induced perturbations delays separation, leading to heat transfer enhancement. The pressure gradient parameter, determined through the model, aligns closely with existing experimental findings for both laminar and turbulent boundary layers. The model predicts a maximum heat transfer enhancement of approximately 14 %. Experimental results demonstrated a maximum heat transfer enhancement of 10 % under forced air convection, attributed to the transition from the stretched to flapping mode of the flag. However, the heat transfer is suppressed in the fully deflected mode as the flag blocks the air impinging on one side of the tube. The heat transfer improvement comes at a negligible pressure drop cost, making this technique more attractive.
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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".