Forced convection heat transfer from confined circular/semi‐circular heaters and coolers with various orientations
Bibliographic record
Abstract
Abstract The combined implementation of porous medium and hybrid nanofluid with heaters and coolers can be an effective technique to improve the efficiency of several types of electric equipment. In this regard, the present study has been conducted to analyze the forced convection heat transfer of water‐based hybrid nanofluid in porous channel with pairs of heaters and coolers of various shapes. The circular and semi‐circular heaters and coolers with distinct orientations are considered. The Peclet number , Darcy number , porosity , and volume fraction of hybrid nanoparticles are chosen as the governing parameters. The governing equations are solved by using the finite element method based commercial software COMSOL Multiphysics. The acquired results exhibit that the heat transfer from heaters and coolers is enhanced by decreasing and for all the cases and values of and . The lowest heat transfer has been obtained by circular heaters and coolers (case 1). Moreover, the semi‐circular heaters and coolers with curved facing towards channel inlet (case 2) and flat surface towards the bottom channel wall (case 4) show higher heat transfer compared to other cases. The average Nusselt number for case 4 is around 3.63% higher from case 2 at the highest values of the considered parameters. Case 4 shows the minimum drag coefficient and maximum heat transfer at the highest values of the governing parameters.
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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".