Computational fluid dynamic evaluation of heat transfer enhancement in microchannel solar collectors sustained by alumina nanofluid
Bibliographic record
Abstract
Abstract Nanofluids have produced a wide range of researches for various cooling/heating purposes, owing to the enhanced thermophysical properties they bring by suspending nanoparticles in the base fluid. This work proposes a detailed computational fluid dynamic (CFD) study of heat transfer enhancement in microchannel solar collectors coupled with nanofluid. The accuracy of the numerical model is ensured through a reliable finite element analysis considering the complexity of the three‐dimensional structure of microchannel solar collector. The thermophoretic motion induced by the suspension of Al2O3 nanoparticles was also evaluated to further understand the thermal enhancement observed in forced convection regimes. The accuracy of the model was first validated with respect to propylene glycol/water fluid, and then applied to evaluate the performance for Al2O3/water nanofluid. A detailed comparison of the performance of the two fluids with an assessment of the temperature and velocity profiles, was adopted to evaluate the thermal efficacy of adding nanofluids. A further investigation of the effect of solar collector inclination angles (0, π/6, π/4,and π/3) at the optimal volumetric concentration of the nanofluid was also done to determine the impact of the system geometry on the efficacy of the heat removal. It was established that the optimal heat removal is achieved at 2% nanoparticle concentration. Finally, it was also detected that increasing the inclination angle of the solar collector (from 0 to π/3) obstructed the heat removal efficiency.
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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) | 0.001 | 0.000 |
| 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".