Combined simulation–experimental approach to power cable thermal loading assessment
Bibliographic record
Abstract
The performance of an underground transmission and distribution system is critically influenced by the thermal properties of the surrounding medium, as well as the thermal properties of the cable itself. The thermal behaviour of the cable is strongly dependent on the loading conditions and thermal parameters of the cable materials as well as the thermal characteristics of the surrounding soil, ambient environment and boundary conditions. A combined experimental–computational investigation is performed to examine the thermal parameters which may influence the performance of the underground cable. First, the thermal specification of the soil was tested by simulating a high temperature gradient along the body of the tested sample enclosed by a heat source–heat sink pair facing each other. In the second part, the 15 kV XLPE underground power cable is energised as a heat source as in the actual case. The thermal field at different spots and loadings was investigated using a developed full-size experimental setup to monitor the thermal behaviour of the underground cables, surrounding soil and boundaries phenomena (heat coefficient losses at the convective boundaries and the heat losses at the isolated boundaries). The proposed combined finite-element-gradient optimisation method is used to estimate the cable thermal parameters. This is based on matching the computational simulation of the experimental model based on finite element to that obtained from the experimental measurements.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".