Thermo-economic performance analysis and multi-objective optimization of viscosity ratio and thermal conductivity ratio of copper oxide–palm oil nanolubricants
Bibliographic record
Abstract
Through experimental research, this work explores the thermophysical properties, cooling efficiency, and economic viability of copper oxide–palm oil nanolubricants in tribology applications. The viscosity and thermal conductivity of the nanolubricants were tested at three different volume concentrations (0.1, 0.3, and 0.5 vol. %) throughout a temperature range of 30 °C to 80 °C at intervals of 10 °C. Researchers looked attentively at how the viscosity and thermal conductivity ratios of the nanolubricants were affected by temperature and volume concentration. A significant increase in thermal conductivity was noted with increasing concentration and temperature. On the other hand, as temperature increased, viscosity reduced and was dependent on volume concentration. The property enhancement ratio was used to evaluate the nanolubricants' cooling capacity before an economic analysis of their cooling efficacy was conducted. Based on experimental data, the study led to the creation of novel correlations between the viscosity ratio and thermal conductivity ratio. These models showed a high degree of agreement (R2 values of 99.47% for the thermal conductivity ratio and 97.78% for the viscosity ratio) between the expected and actual outcomes. The ideal values of the viscosity and thermal conductivity ratios were 1.10 and 1.62, respectively. These values corresponded to a critical temperature of 37.32 °C and a volume concentration of 0.16 vol. % for nanoadditives. The findings offer valuable insights into optimizing nanolubricants for enhanced cooling performance in tribological systems, with potential applications in improving energy efficiency and reducing operational costs in industrial processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".