Computational fluid dynamic evaluation of heat transfer enhancement in microchannel solar collectors sustained by alumina nanofluid
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
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 Al 2 O 3 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 Al 2 O 3 /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.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 it