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作 者:梁俊豪 蔡萌琦 周林抒 柳军[4] 闵光云 丁顺利 田博文 胡茂明 黄汉杰[6] LIANG Junhao;CAI Mengqi;ZHOU Linshu;LIU Jun;MIN Guangyun;DING Shunli;TIAN Bowen;HU Maoming;HUANG Hanjie(School of Mechanical Engineering,Chengdu University,Chengdu 610106,China;School of Architecture and Civil Engineering,Chengdu University,Chengdu 610106,China;State Grid Sichuan Integrated Energy Service Co.,Ltd.,Chengdu 610072,China;School of Mechanical and Electrical Engineering,Southwest Petroleum University,Chengdu 610500,China;Sino-French Institute of Nuclear Engineering and Technology,Sun Yat-sen University,Zhuhai 519082,China;China Aerodynamics Research and Development Center,Mianyang 621000,China)
机构地区:[1]成都大学机械工程学院,四川成都610106 [2]成都大学建筑与土木工程学院,四川成都610106 [3]国网四川综合能源服务有限公司,四川成都610072 [4]西南石油大学机电工程学院,四川成都610500 [5]中山大学中法核工程与技术学院,广东珠海519082 [6]中国空气动力研究与发展中心,四川绵阳621000
出 处:《成都大学学报(自然科学版)》2023年第4期416-422,共7页Journal of Chengdu University(Natural Science Edition)
基 金:国家自然科学基金(51507106);成都市国际科技合作资助项目(2020-GH02-00059-HZ);模式识别与智能信息处理四川省高校重点实验室开放基金(MSSB-2020-05)。
摘 要:为解决风洞试验成本高与耗时长的问题,提出了一种基于机器学习预测气动力系数的研究方法.首先利用风洞试验获得不同参数下的覆冰导线气动力系数,然后通过机器学习构建模型预测了新月形覆冰导线在不同冰厚与风速下的气动力参数,所得各覆冰导线气动力系数随风攻角变化曲线与由风洞试验所得结果规律一致.基于机器学习和风洞试验所得气动系数确定的Den Hartog与Nigol系数随风攻角的变化结果相吻合,表明了机器学习预测方法的可行性.In order to solve the problem of high cost and time-consuming of wind tunnel test,an experimental method based on machine learning to predict aerodynamic coefficients is proposed.In this paper,the aerodynamic coefficient of the iced conductor under different parameters was obtained by wind tunnel test,then the aerodynamic parameters of the crescent type icing conductor under different ice thicknesses and wind speeds were predicted by machine learning,and the aerodynamic coefficient of each iced conductor changed with the wind attack angle with the wind tunnel test.The change of Den Hartog coefficients,determined based on the aerodynamic coefficient obtained by machine learning and wind tunnel experiments,coincided with the change of Nigols coefficient with wind attack angle.The results show the feasibility of the machine learning prediction methods.
分 类 号:TM752[电气工程—电力系统及自动化]
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