Impact of submerged substrate roughness on nanofluid swirling impinging jet arrays
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Bibliographic record
Abstract
Analyzing turbulent swirling jet impingement poses significant challenges, especially when incorporating nanofluids into the analysis, which further exacerbates the complexity. The limited body of research in this specific domain primarily focuses on turbulent swirling/non-swirling air or water jets, or laminar-impinging nanofluid jets. This study delves into investigating the thermos-hydrodynamic behavior of low-concentration non-aqueous nanofluid swirling jets impingement on submerged heated rough surfaces for high Reynolds number. Ethylene glycol-based aluminum oxide [CH 2 OH) 2 +Al 2 O 3 ] nanofluid is considered along with water for different controlling parameters including swirl intensity (0 ∼ 1), and surface roughness height (0 ∼ 1500 μ m ). The findings reveal that (CH 2 OH) 2 +Al 2 O 3 exhibits superior heat transfer performance compared to water, attributed to enhanced nanoparticle resolution in (CH 2 OH) 2 . A rough surface enhances heat transfer by disrupting the thermal boundary layers and increasing the interaction area between a hot solid surface and a cold fluid. However, excessive roughness can impede heat transfer. Swirling flow contributes to more uniform cooling by intensifying turbulence and inducing recirculation zones with stronger vortices, particularly noticeable on rough surfaces. Implementing a staggered array configuration improves cooling performance by minimizing interference between jets. Notably, heat transfer rates are higher at shorter impinging distances, and high swirl conditions generate increased turbulence and turbulence kinetic energy. A correlation is developed between various controlling parameters and the average Nusselt number. Finally, through Gaussian process regression (GPR), this study achieved a highly accurate predictive model for local Nusselt number estimation in swirling nanofluid jet cooling systems, reaching an optimal cross-validation root mean squared error (RMSE) of 0.04342 and a final test RMSE of 0.0596.
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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) | 0.000 | 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