Performance Evaluation of Nanocoolants for Automotive Cooling Application
Bibliographic record
Abstract
As compared to base fluid, nanofluids exhibit a significant higher thermal property. It has been seen in the recent work that the heat exchange between the nanofluid and the heat source is greater than the traditional fluids and there is also a significant enhancement of the thermal conductivity. The use of nanofluid in the automobile radiator improves the heat transfer rate and eventually results in overall reduction in the size of the radiator. In this study, heat transfer coefficient of the nanofluid is inspected experimentally as an automobile radiator coolant. The nanoparticle with the size <100 nm is selected. The experiments were conducted on titanium dioxide (TiO2) with deionised water as a base fluid. The thermos-physical properties such as specific heat capacity, thermal conductivity and viscosity of the nanofluid were measured experimentally. The samples of the nanofluid were prepared with weight concentration of TiO2 varied from 0.1%–0.25%wt using two-step method. The good stability is obtained by the Magnetic stirring and Sonication process with the sodium dodecyl sulphate (SDS) surfactant. Transient hot wire method is used for the measurement of the thermal conductivity of the nanofluid. The Zeta Potential test is used to determine the stability of the nanofluid. The experiments were conducted on various temperature ranges (50–90°C) to know the effect of coolant. The results were found that thermal conductivity of the samples was increased by 10% to 15 % for the different concentration (0.1%–0.25%wt) as compared to the conventional fluid like deionised water. The rate of flow of nanofluid was 2–3 litres/min. The nanoparticle and the surfactant were used in the equal ratio (1:1).
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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".