Heat transfer optimization of two phase modeling of nanofluid in a sinusoidal wavy channel using Artificial Bee Colony technique
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it