Examination of Yawed Wind Loading on Transmission Towers
Bibliographic record
Abstract
Guyed and self-supported transmission towers are typically comprised of two unique geometries: the vertical tower body and the horizontal conductor support cross-arm. Much research has been directed towards the evaluation of aerodynamic coefficients of the compact and symmetric geometry often used in the vertical portion of towers, however, much less information is available for cross-arm or non-symmetric sections. Wind tunnel tests carried out on a typical tower cross-arm design indicate that the drag loads vary significantly with wind direction, and exhibit a relationship which differs from the design procedure in the current (3rd Edition) of ASCE Manual of Practice No. 74: Guidelines for Electrical Transmission Line Structural Loading (ASCE-74). Two additional methods are presented, which show better agreement with the experimental data: the IEC equation for yawed wind (which was recommended in the 2nd Edition of ASCE-74); and a modified version of the yawed wind formula in ASCE-74. These approaches have been proposed for the upcoming edition of ASCE-74. The implications of the different equations are shown with reference to the wind loading on the structure only, as well as the overall tower-line system. The results are also compared with those obtained using PLS-TOWER. The paper reviews how force coefficients (i.e., drag coefficients) are developed and applied in wind load calculations, and describes the implications of the current and proposed approaches to yawed wind loads.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".