Surface Micro-Profiling for Reduced Energy Dissipation and Exergy Loss in Convective Heat Transfer
Bibliographic record
Abstract
This article examines the role of slip conditions within surface-embedded microchannels for reducing entropy production of external flows with convective heat transfer. Viscous dissipation of mechanical energy into internal energy within the boundary layer leads to pressure losses and other irreversible losses of energy availability. These exergy losses entail additional input power needed to deliver a fixed mass flow across the surface, subject to a specified rate of heat transfer to/from the wall. By selectively altering geometrical and surface parameters which minimize the net entropy production, the benefits of drag reduction due to the slip-flow conditions can outweigh the higher irreversibility arising from added microchannel area. Predicted results illustrate the changes of optimal Reynolds number and entropy generation number with varying surface parameters for embedded parallel and diverging microchannels. Based on these results, it is viewed that surface micro-profiling offers a useful new technique of taking advantage of slip-flow microfluidic conditions for reducing drag and simultaneously increasing heat transfer effectiveness in external flows.
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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) | 0.001 | 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".