Evaluating the Impact of Free-Stream Turbulence on Convective Cooling of Overhead Conductors Using Large Eddy Simulations
Bibliographic record
Abstract
The international trend of using renewable energy sources for generating electricity is increasing, partly through harvesting energy from wind turbines. Increasing electric power transmission efficiency is achievable through using real-time weather data for power line rating, known as real-time thermal rating (RTTR), instead of using the worst case scenario weather data, known as static rating. RTTR is particularly important for wind turbine connections to the grid, as wind power output and overhead conductor rating both increase with increasing wind speed, which should significantly increase real-time rated conductor from that of statically rated. Part of the real-time weather data is the effect of free-stream turbulence, which is not considered by the commonly used overhead conductor codes, Institute of Electrical and Electronics Engineers (IEEE) 738 and International Council on Large Electric Systems (CIGRÉ) 207. This study aims to assess the effect free-stream turbulence on IEEE 738 and CIGRÉ 207 forced cooling term. The study uses large eddy simulation (LES) in the ANSYS fluent software. The analysis is done for low wind speed, corresponding to Reynolds number of 3000. The primary goal is to calculate Nusselt number for cylindrical conductors with free-stream turbulence. Calculations showed an increase in convective heat transfer from the low turbulence value by ∼30% at turbulence intensity of 21% and length scale to diameter ratio of 0.4; an increase of ∼19% at turbulence intensity of 8% and length scale to diameter ratio of 0.4; and an increase of ∼15% at turbulence intensity of 6% and length scale to diameter ratio of 0.6.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".