Experimental Investigation on Heat Transfer Performance of Titanium Oxide - Water and Ethylene Glycol Nanofluid in a Plate Heat Exchanger
Bibliographic record
Abstract
Plate heat exchangers play vital roles in industrial applications, the use of nanofluids is essential in heat transfer due to its effective performance, the performance of nanofluids in mixture base fluids is yet to be explored.Hence, the nanofluids made of water and Ethylene glycol mixtures (60:40) as base fluid and 21 nm sized Titanium Oxide were investigated experimentally on thermophysical properties and heat transfer performance with different nanoparticle concentrations from 0.4% -1.0% under flowrates from 0.2 -0.6 l/min at nanofluid bulk temperature from 30 -70°C.The results indicate that thermal conductivity, specific heat, viscosity, and density increase with the concentration of nanofluid, however, the viscosity change with concentration is not significant.The significant improvement of heat transfer rate by using the nanofluid is observed, the heat transfer rate increases with the concentration and bulk temperature of nanofluid.At 30°C when the concentration is changed from 0.4% to 1.0%, the heat transfer rate is improved by 42.9% -69.7%, at the flowrate of 0.2 -0.6 L/min; at 70°C when the concentration is changed from 0.4% to 1.0%, the heat transfer rate is improved by 11.9 -24.0% at the flowrate of 0.2 -0.6 L/min.The overall heat transfer coefficient increases as flowrate and nanoparticle concentration increase, the overall heat transfer coefficient increases by 8.7%, 6.9%, 7.0 %, and 6.7% when the concentration is increased from 0.4 to 1.0% at flowrate of 0.2, 0.3, 0.4, and 0.6L/min, respectively.Nanofluid can improve the effectiveness of plate heat exchanger significantly, the improvement of effectiveness of heat exchanger by using nanofluid at the flow rate of 0.2L/min is between 21.7-36.2%,36.7-53.3%,44.6-64.3%,47.8-66.1%,and 52.9-76.5% at 30, 40, 50, 60 and 70 °C, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".