Optimizing high-concentrator photovoltaic efficiency: Numerical study of hybrid nanofluid and porous wavy walled mini channel heat sink
Bibliographic record
Abstract
This study investigates advanced thermal management for high-concentration photovoltaic (HCPV) systems through the combined use of hybrid nanofluids and wavy-walled mini-channel heat sinks. Numerical simulations of 36 configurations, examining wave amplitudes (100–300 μm), Reynolds numbers (300–500), and nanoparticle concentrations (0–0.1 %wt) under a concentration ratio of 1200 and 1000 W/m 2 irradiance, demonstrate significant performance improvements. The optimal configuration achieves 41.15 % electrical efficiency and 224 W power output (i.e., 26 % higher than comparable systems) while maintaining exceptionally low pumping power (i.e., 0.007 W). Integrating wavy-walled channels with porous inserts yields substantial heat transfer enhancement by disrupting the boundary layer, promoting secondary vortices, and intensifying fluid-solid thermal interactions. This combined approach boosts thermal performance while markedly lowering the required pumping power. Artificial neural networks and genetic algorithms , successfully optimize the system by balancing electrical efficiency, temperature non-uniformity, and energy consumption. These findings provide a practical framework for implementing efficient cooling solutions in high-performance HCPV applications, offering technical advancements and sustainable energy benefits.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".