Optimization of viscosity of propylene glycol and water (50:50)/Graphene nanofluid: A response surface methodology and machine learning approach
Bibliographic record
Abstract
: This study looked at how thick a nanofluid made from propylene glycol and nanoparticles flows, testing temperatures from 40 to 120°C and using nanoparticle amounts from 0% to 0.5%, by applying response surface methodology (RSM). The quadratic model provided the best exact fit, outperforming the linear and two-factor interaction models. It had a high coefficient of determination (R² = 0.9874), low standard deviation, and a statistically significant P-value (< 0.0001). At 120°C and 0.5 wt% nanoparticle concentration, the model projected an ideal situation with a kinematic viscosity of 0.284 m³/s. Several machine learning methods were used on the experimental data to enhance the RSM analysis. These algorithms were k-Nearest Neighbor (kNN), Random Forest, XGBoost, and Decision Trees. The models with the strongest association with experimental results were XGBoost, decision trees, and kNN, which produced the best prediction accuracies (R² values ranged from 0.90 to 0.97). These models well represented the complicated nonlinear relationships between the variables. There were no noticeable indications of sedimentation or agglomeration, and the nanofluid maintained steady flow behavior even when subjected to greater temperatures and more nanoparticle loadings. The results suggest that combining RSM and machine learning methods can create a solid approach for improving and predicting the thermal properties of nanofluids used in heat management.
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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 | 0.001 |
| 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) | 0.000 | 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".