The Power-Law Fluids Staggered Circular Cylinders In Laminar Forced Convection: An Optimization Study
Bibliographic record
Abstract
In this study, for power-law fluids, a two-dimensional heat transfer analysis was performed in a circular cylinder to determine the ideal distance between cylinders in equilateral triangle configurations for forced convection in free stream crossflow.The cylinder array is in contact with a free stream of a specific temperature and velocity while occupying a set volume.The optimal cylinder-to-cylinder spacing is determined by maximizing the overall thermal conductance between all the cylinders and the free stream.The numerical study was conducted to maximize the heat transfer rate over the range of Reynolds number, 40 ≤ Re ≤ 200; power-law index, 0.2 ≤ n ≤ 1.3; Prandtl number, 1 ≤ Pr ≤ 100; and geometries with spacing from cylinder-to cylinder, 0.5 ≤ S/D ≤ 2. The governing equations have been solved for the steady state flow over the range of parameters by employing finite-element numerical scheme.The flow and thermal field by using hot cylinder arranged in triangular array is analysed by plotting the streamlines and isotherms.The thermal heat conductance increases for the shear thinning fluids as Reynolds number increases and on further increasing the Prandtl number.The relation for thermal heat conductance with Prandtl number for extreme values of Reynolds number is also shown for different values of power-law index.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".