Stochastic Dynamic Response of a Cross Rope Transmission Line
Bibliographic record
Abstract
Cross Rope (CR) structures are increasingly used in High Voltage (HV) and Ultra High Voltage (UHV) transmission lines (TLs). The configuration of this kind of structures consists of two steel truss masts, each of which is grounded by two guy-cables connected at their upper end. The masts present no rigid connection between them: they are only linked by the CR cable which, likewise, supports the insulator chains and therefore the conductors. The implementation of this structural typology in transmission lines is relatively recent and its popularity is rising due to some favorable features when compared to self-supporting towers and other configurations of guyed structures (their low weight and associated low cost stand out). However, despite the recent application of CR structures in power lines around the world – Argentina, Brasil, South Africa, Australia, Canada – many aspects of their response to time-varying excitations have not been studied and documented in detail yet. In this work, a segment of a CR transmission line under stochastic wind load is addressed. The mathematical model for the dynamics of the different main structural elements (tower, insulators and cables) is stated, and the governing differential equations are discretized through the Finite Element Method. For the generation of the spatially and temporally correlated wind load field, the Spectral Representation Method (SRM) is applied. Attention is focused on the effect of the aerodynamic damping on the structural response.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".