NUMERICAL INVESTIGATION OF HEAT TRANSFER AND TURBULENT FLUID FLOW FOR TRANSVERSE VORTEX GENERATORS WITH NANOPARTICLES
Bibliographic record
Abstract
This paper proposes a simple design and easy-to-install vortex generator (VG) for heat exchangers to enhance heat transfer rates. The aim is to maintain low pressure drops and high heat transfer rates. The effects of the VG's geometrical parameters on thermal performance and pressure drop are investigated in this paper for divergent and convergent schemes using commercial software Comsol Multiphysics version 6. The effects of Reynolds number, VG angle, and the quantity of VGs on the response (i.e., Nusselt number and pressure drop) are investigated based on variance analysis. The nanoparticle concentration varies from 1 to 6%. The results indicate that the quantity of VGs is the most significant factor affecting pressure drop. The critical factor for heat transfer enhancement of divergent and convergent VGs are the Reynolds number and the quantity of VGs, respectively. Finally, the optimal conditions are predicted by the response surface methodology (RSM). The optimal requirements for using VG type A are β = 0°, the quantity is two, and the Reynolds number should be 9160. Furthermore, the optimal conditions for VG type B are β = 0°, Reynolds number is 10,000, and the number of VGs is two. The VG proposed in this study has a simple structure that can be efficiently designed and installed on heat exchangers. It is more efficient and applicable than the designs suggested in the literature.
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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 | 0.001 |
| 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 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".