Heat transfer optimization of two phase modeling of nanofluid in a sinusoidal wavy channel using Artificial Bee Colony technique
Bibliographic record
Abstract
The present study represents the heat transfer optimization of two-dimensional incompressible laminar flow of Al2O3-water nanofluids in a duct with uniform temperature corrugated walls. A two phase model is applied to investigate different governing parameters, namely: Reynolds number (100 ≤ Re ≤ 1000), nonofluids volume fraction (0% ≤ ϕ ≤ 5%) and amplitude of the wavy wall (0 ≤ α ≤ 0.04 m). For optimization process, a recent spot-lighted method, called Artificial Bee Colony (ABC) algorithm, is applied, and the results are shown to be in a good accuracy in comparison with another well-known heuristic method, i.e. particle swarm optimization (PSO). The results indicate that the effect of utilizing nanoparticles and increasing Reynolds number is more intensified on growing the average Nusselt number than variations of the amplitude of the wavy wall. To prevent the worst possible heat transfer, the specific amplitude which leads to a minimum average Nusselt number is detected. The effect of using nanoparticles on thermal-hydraulic performance factor (j/f) is presented which considers both heat transfer and hydrodynamics aspects. The results showed that volume fraction has a direct and the wavy wall's amplitude has a converse effect on the thermal-hydraulic performance factor. Furthermore, an optimum value for Reynolds number is found to maximize the thermal-hydraulic performance factor.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".