Thermal Analysis of New Generation of Distribution Transformers: Experimental Validation
Bibliographic record
Abstract
Because the insulation condition is closely linked to the hot-spot temperature (HST), a detailed thermal evaluation of distribution transformers (DTs)—among the grid's most critical and costly assets—can reduce the risk of failure. Accordingly, this study investigates DT thermal behavior to predict HST with high fidelity. During temperature-rise testing (TRT), optical fiber sensors (OFSs) are employed to assess the proposed non-uniform three-dimensional (3D) computational fluid dynamics (CFD) models. Relative to OFS measurements, the new 3D CFD-based thermal analysis exhibits a deviation of only 0.11% (0.1 °C), demonstrating excellent accuracy and computational efficiency. In addition, the non-uniform 3D CFD temperature fields are validated via thermographic imaging for both the top-oil temperature (TOT) and bottom-oil temperature (BOT). The resulting discrepancies remain below 0.65%, indicating strong agreement between thermography and CFD predictions at both locations and, consequently, adequate accuracy of the proposed model. Finally, the incorporation of nanofluids lowers key thermal indices, thereby extending transformer service life.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".