Ethylene- and Propylene-Glycol Based Nanofluids: A Litterature Review on Their Thermophysical Properties and Thermal Performances
Bibliographic record
Abstract
Nanofluids are considered a promising way to improve the heat transfer capability of base fluids. Water is the most commonly-used heat transfer fluid. However, in refrigeration systems, it may be necessary to mix water with either ethylene- or propylene-glycol to lower its freezing point and prevent from ice formation. In the same way, for car radiators or industrial heat exchangers, the boiling point of water can be pushed up by mixing it with glycol-based fluids. The increasing awareness of energy saving and industrial energy efficiency improvement results in the growing interest in ethylene- or propylene-glycol-based nanofluids for applications in various thermal systems. The present paper proposes an extensive review of the most recent and relevant experimental and numerical works on the thermophysical properties and performances of ethylene- or propylene-glycol-based nanofluids. Research perspectives are also provided with the long-term objective that these nanofluids be more widely considered in real industrial applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".