Rheological Analysis of Thermally Aged Natural Ester Fluid Using Nonlinear Least Square Technique
Bibliographic record
Abstract
In the present study, rheological studies dealing with the flow behavior of thermally aged natural ester fluid under laboratory-controlled conditions are performed. Visual observation of gelling is witnessed due to thermal aging. An enhancement in viscosity of about 188% is observed with the 500-h aged fluid compared to the nonaged fluid. It is estimated that the flow behavior altered by gel follows non-Newtonian behavior with a shear-thinning effect on applied shear rate conditions. The dependency of storage and loss modulus to amplitude and frequency sweep under oscillatory shear flow was investigated. The viscous state of gel dominates over its initial elastic state, beyond the gel point. The flow models that govern the behavior of the gel were applied, adopting nonlinear least square methods to investigate the rheological parameters in terms of flow behavior index and yield stress. The results of the thermally aged natural ester fluid demonstrate a positive correlation with Bingham’s model and Mizrahi–Berk’s model based on the yield stress and flow behavior index, respectively. The phase transition of gel investigated in the present study can be used as a time-based preventive measure, by the transformer manufacturer, in the heat transfer performance of natural ester-filled transformers.
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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) | 0.000 | 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".