Analysis of Aged Oil on the Cooling of Power Transformers from Computational Fluid Dynamics and Experimental Measurements
Bibliographic record
Abstract
In this paper, experimental and numerical investigations were conducted to study the aging impact on the cooling capacity of mineral oils in power transformers. The experimental investigations had three objectives. Firstly the study of the impact of oil aging on its physicochemical properties using two diagnostic techniques which are: DDP (Dissolved Decay Products) according to ASTM (American Society for Testing and Materials)-D6802 standard and Interfacial Tension (IFT) according to ASTM-D971 standard. Secondly,the study of the impact of oil aging on its viscosity according to ASTM D445. Finally the development of empirical equations depicting oil viscosity changes, to be implemented in the numerical model. To achieve this, accelerated thermal aging tests of mineral oil samples were conducted in laboratory conditions according to ASTM D-1934standard. Data from experimental investigations were used to conduct the numerical investigations. A 2D axisymmetric numerical model was developed with COMSOL Multiphysics 4.3a to study the cooling capability of oil aged at different levels. The results indicate that the oil physicochemical properties are affected by aging. Furthermore the results show that the oil viscosity increase with aging and give a good correlation between viscosity and aging indicators. Finally it was found that sludge from oil oxidation byproduct contributes to the formation of hotspots and this leads to a significant increase of hotspot temperature in the power 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".