Data from: Prediction model for aerodynamic coefficients of iced quad bundle conductors based on machine learning method

The lift, drag and torsional moment coefficients, versus wind attack angle of iced quad bundle conductors in the cases of different conductor structure, ice and wind parameters are numerically simulated and investigated. With the Latin hypercube sampling (LHS) and numerical simulation, sampling points are designed and datasets are created. Set the number of sub-conductors, wind attack angle, bundle spacing, ice accretion angle, ice thickness, wind velocity and diameter of conductor as the input variables, a prediction model for the lift, drag and moment coefficients of iced quad bundle conductors is created, trained and tested based on the dataset and extra-trees algorithm. The final integrated prediction model is further validated by applying the aerodynamic coefficients from the prediction model and numerical simulation respectively to analyze the galloping features. The developed efficient prediction model for the aerodynamic coefficients of iced quad bundle conductors plays an important role in the quick investigation, prediction and early warning of galloping.

针对不同导线结构、覆冰与风参数工况下的覆冰四分裂导线，本文对其升力系数、阻力系数与扭转力矩系数随风攻角的变化特性开展了数值模拟与研究。结合拉丁超立方抽样（Latin Hypercube Sampling, LHS）与数值模拟方法，完成了抽样点设计与数据集构建。以子导线根数、风攻角、分裂间距、覆冰角度、覆冰厚度、风速及导线直径作为输入变量，基于所构建的数据集与极端随机树（Extra-Trees）算法，完成了覆冰四分裂导线升力、阻力及力矩系数预测模型的搭建、训练与测试。通过分别采用预测模型与数值模拟得到的气动力系数分析导线舞动特性，对最终集成的预测模型进行了进一步验证。本文所构建的覆冰四分裂导线气动力系数高效预测模型，可在导线舞动的快速研究、预测与预警工作中发挥重要作用。

搜集汇总

背景与挑战

背景概述

该数据集通过拉丁超立方采样和数值模拟生成，包含覆冰四分裂导线在不同结构、覆冰和风参数下的气动系数数据，用于基于机器学习的气动系数预测模型训练和舞动分析验证。数据集以ZIP格式提供，大小为4.6 MB，发布于2021年，采用开放许可。