Forced, natural and mixed convection of Non-Newtonian fluid flows in a square chamber with moving lid and discrete bottom heating
Bibliographic record
Abstract
The aim of the present study is to examine the thermal performance of a power-law fluid for a discretely heated lid-driven enclosure. This numerical outcome can determine the ideal heat transfer medium for a given design, thus reducing unnecessary expenditures on experiments. The chamber's side walls move at a constant speed under constant ambient conditions. The finite element approach is used to solve governing Navier-Stokes and energy equations. A parametric investigation is carried out by varying the Richardson number (Ri) within the range of 2 × 10−3 ≤ Ri ≤ 105 at a fixed Reynolds or Grashof number in different cases and changing them simultaneously while keeping the Richardson number at unity. In all cases, the power-law index (n) is varied within 0.6 ≤ n ≤ 1.4. The results have been presented in terms of a quantitative evaluation of the average Nusselt number of the heated source, the average fluid temperature of the chamber, the maximum temperature of the heat source, and the normalized average Nusselt number. Furthermore, qualitative contour plots of stream function and temperature for different cases are presented. This research shows that the higher the power-law index, the greater the heat transfer for forced and pure mixed convection and that heat transfer performance differs with the increment/decrement of the Richardson number while keeping either Reynolds or Grashof number constant. Besides, the highest heat transfer performance is observed during the natural convection case of the pseudo-plastic fluid, and the lowest heat is transferred when the fluid is dilatant.
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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 | 0.001 |
| 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".