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 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 | 0.001 |
| 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 | 0.001 | 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".