Lower Entropy Generation in Microchannels With Laminar Single Phase Flow
Bibliographic record
Abstract
In this study the entropy generation minimization method is used to find the optimum channel dimensions in micro heat exchangers with a uniform heat flux. With this approach, pressure drop and heat transfer in the micro channels are considered simultaneously during the optimization analysis. A computational model is developed to find the optimum channel depth knowing other channel geometry dimensions and coolant inlet properties. The flow is assumed laminar and both hydrodynamically and thermally fully developed and incompressible. However, to take into account the effect of the developing length in the friction losses, the Hagenbach’s factor is introduced. The micro channels are assumed to have an isothermal or isoflux boundary condition, non-slip flow, and fluid properties have dependency on temperature accordingly. For these particular case studies, the pressure drop and heat transfer coefficient for the isoflux boundary condition is higher than the isothermal case. Higher heat transfer coefficient and pressure drop were found when the channel size decreased. The optimum channel geometry that minimizes the entropy generation rate tends to be a deep, narrow channel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".