Turbulence and thermo‐flow behavior of air in a rectangular channel with partially inclined baffles
Bibliographic record
Abstract
Abstract In this paper, the thermal performance improvements of a heat removal system like an electronic system have been analyzed. The studied case is a horizontal channel in which two partially inclined baffles are attached with variable height and number. The channel is crossed by a forced convective flow of a cooling fluid (air). This numerical work evaluates the influences of the height and number of the baffles on the enhancement of the heat transfer rate. The mathematical model of this system is composed of nonlinear partial equations that the analytical solution for them is very complex, hence the need for numerical analysis is mandatory with the aid of a finite volume method. Accordingly, The numerical results are presented in axial and transverse velocity, temperature, local and average Nusselt number, local friction coefficient, pressure drop, heat transfer rate, and turbulence kinetic energy. The results revealed that it is possible to improve the thermal performance of the considered system by adopting designs that allow the maximum heat transfer rate with the minimum energy loss. In addition, results show that at the lowest Reynolds number (Re = 10,000), as the height of baffles rises from 0.01 to 0.03 m (growth by 200%), the heat transfer rate augments about 59.09%. Moreover, at the highest evaluated Reynolds number (Re = 87,300), by increasing the height of baffles up to 200%, the heat transfer rate increases by approximately 50.53%. Furthermore, employing a higher number of baffles leads to more heat transfer rates and a significant pressure drop.
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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".