Physiochemical studies of functionalized MWCNT/transformer oil nanofluid utilized in a double pipe heat exchanger
Bibliographic record
Abstract
The transformer-oil based nanofluid suspensions were prepared by adding 0.05 to 0.80 wt% multi-wall carbon nanotubes (MWCNTs) functionalized with a –COOH group. Sodium dodecyl sulfate (SDS) was used to stabilize the suspensions. The resulting material was used as a coolant in a double pipe heat exchanger operated under co- and counter-current flow conditions. The nanofluid thermo–physical features such as the thermal conductivity, viscosity, and density were determined at various temperatures and mass fractions. Then, pertinent semi-empirical relations were developed. To verify any MWCNT and SDS interactions with the material, the Fourier-transform infrared analysis was performed. Moreover, the stability of the nanofluid suspension was understudied through the UV–vis and thermogravimetric analysis techniques. In addition, the maximum heat transfer coefficient improvement was determined to be 86.7% at a MWCNT mass fraction of 0.8 wt%. Meanwhile, average increments of the overall heat transfer coefficient and thermal conductivity of the prepared nanofluid were revealed about 37.2% and 138%, respectively in comparison with that of the base fluid. Furthermore, the optimum thermal conductivity of 0.388 W/m.K was obtained at 45 °C and 0.8 wt% of the MWCNT. Ultimately, a sensitivity analysis emphasized that, the understudied system’s behaviors were within an accuracy limit of ± 97%.
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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 | 0.000 |
| 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) | 0.001 | 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".