Assessing the Accuracy of Convective Heat Transfer from Overhead Conductor at Low Wind Speed Using Large Eddy Simulations (LES)
Bibliographic record
Abstract
This project uses Computational Fluid Dynamics (CFD) to assess the accuracy of the forced cooling term for Real Time Thermal Rating (RTTR) of power lines in overhead conductor codes, IEEE 738 and CIGRÉ 207. The analysis is done for low wind speed, corresponding to Reynolds Number of 1,000, and 3,000. The project uses Large Eddy Simulation (LES) in the ANSYS Fluent software. The primary goal is to calculate the convective heat transfer for cylindrical and stranded conductors in non-turbulent flow and for cylindrical conductors with free-stream turbulence. The results showed that the heat transfer correlations used in the codes are accurate for low turbulent flows and that the stranded conductor causes an increase in heat transfer of ~9 % over a cylindrical conductor at low wind speed. The constant heat flux boundary condition experiences ~15 % higher Nusselt Number than uniform temperature boundary condition. The calculated increase in heat transfer due to turbulence was significant; increased heat transfer due to turbulence ~24 % at Reynolds Number of 3,000 at a turbulence intensity of 8% and length scale to diameter ratio of 0.4.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".