文章基本信息

标题：Propagation of Uncertainty in a Transmission Line Guyed Tower with Stochastic Guy Pretension and Subjected to Wind Load
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作者：Bruno J. Rango ; Marta B. Rosales ; Jorge S. Ballaben 等
期刊名称：Mecánica Computacional
印刷版ISSN：2591-3522
出版年度：2018
卷号：36
期号：17
页码：761-770
语种：English
出版社：CIMEC-INTEC-CONICET-UNL
其他摘要：Guyed transmission lines are extensively used in overhead power transmission around the world. This kind of structures presents a series of favorable characteristics like simple installation procedure, low weight and low cost. However, on the other side, they are highly flexible, and exhibit a very nonlinear behavior. Moreover, the most demanding load is represented by wind, which is of random nature. In this sense, the present study addresses the dynamic analysis of a three-dimensional model of a transmission line segment composed by a guyed tower with four guy wires and two spans of conductor cables, subjected to stochastic wind load. The model accounts for the coupling effect between the different physics that take place. In this scheme, the supporting tower is modeled as a linear equivalent beam-column, assuming the hypothesis of the Euler-Bernoulli beam theory and with properties equivalent to a lattice tower. The second order effect due to axial loads on the tower is considered. The motion of the conductors and the guys, on the other side, is governed by a set of nonlinear equations which considers the cables extensibility. The system is discretized by means of the Finite Element Method. The wind velocity comprises a mean and a turbulent component. The Spectral Representation Method is used to derive the latter, which starts from a Power Spectral Density of the wind velocity leading to a function that accounts for both the temporal and spatial correlations. In order to assess the sensitivity of the structure to the variations of the stiffness, the initial tension in each of the four guy cables is assumed an independent random variable. Given the available information about the pretension variables, the Principle of Maximum Entropy is applied to derive the corresponding probability distributions. The stochastic response of the structure is evaluated.