Experimental Investigation of Heat Transfer with Various Aqueous Mono/Hybrid Nanofluids in a Multi-Channel Heat Exchanger
Bibliographic record
Abstract
The use of nanofluids for heat transfer has been examined in recent years as a potential method for augmentation of heat transfer in different systems. Often, the use of nanoparticles in a working fluid does not disrupt the system in significant ways. As a result of this general improvement of a system’s heat transfer capabilities with relatively few detrimental factors, nanofluids and hybrid nanofluids have become an area of considerable research interest. One subcategory of this research area that has been under consideration is the concentration of each of the nanoparticles, leading to either successful augmentation or hindrance. The focus of the current experimental investigation was to examine the resulting impact on heat transfer performance as a result of each nanofluid implemented in an identical three-channel heat exchanger. This work examined the experimental impacts of 0.5 wt% titania (TiO2), 1 wt% titania, a mixture of 0.5 wt% titania and 0.5% silica, and a 0.5 wt% hybrid nanofluid of titania synthetically modified with copper-based nanostructures (Cu + TiO2). The experimental work examined a range of heat flux densities from 3.85 W cm−2 to 7.51 W cm−2, and varying flow rates. Each of the nanoparticles were suspended in distilled water and then mixed using an ultrasonic water bath. The performances of each nanofluid were determined using the local Nusselt number to evaluate the possible thermal enhancement offered by each nanofluid mixture. While the 0.5 wt% Cu + TiO2 hybrid nanofluid did significantly increase performance, the use of a 0.5 wt% TiO2/SiO2 double nanofluid in a three-channel heat exchanger exhibited the greatest performance enhancement, with an average increase of 37.3% as compared to water.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".