Impact of submerged substrate roughness on nanofluid swirling impinging jet arrays
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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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 | 0.001 | 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 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".