Stochastic power spectra models for typhoon and non-typhoon winds: A data-driven algorithm

被引:12
|
作者
Liu, Zihang [1 ]
Fang, Genshen [1 ,2 ]
Hu, Xiaonong [1 ]
Xu, Kun [3 ]
Zhao, Lin [1 ,2 ]
Ge, Yaojun [1 ,2 ]
机构
[1] Tongji Univ, State Key Lab Disaster Reduct Civil Engn, Shanghai 200092, Peoples R China
[2] Tongji Univ, Key Lab Transport Ind Wind Resistant Technol Bridg, Shanghai 200092, Peoples R China
[3] Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China
基金
中国国家自然科学基金;
关键词
Wind power spectrum; Typhoon; Non-typhoon winds; Stochastic model; Monte Carlo method; Moment-based method; Buffeting response; Structural reliability; BOUNDARY-LAYER; TROPICAL CYCLONES; GUST FACTORS; TURBULENCE; HAZARD; THUNDERSTORM; PROFILES;
D O I
10.1016/j.jweia.2022.105214
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The shape of power spectra both for typhoon and non-typhoon winds directly affect the response level of the structure. The conventional deterministic spectrum models fail to reproduce the randomness of the structure response and could underestimate the real vibration amplitudes, especially for typhoon winds featured with stronger gustiness due to internal circulation and thermodynamic effects. Based on long-term observation data captured by the structural health monitoring system installed at Xihoumen suspension bridge, the parameters of wind power spectra in along-wind, cross-wind and vertical directions for typhoon and non-typhoon winds are extracted, respectively. The statistical characteristics and correlations among these parameters are examined. The data-driven stochastic power spectra models are then proposed by utilizing the moment-based theoretical solutions and Monte Carlo technique, respectively. The mean wind speed is incorporated in these models which allows the random simulations of power spectra with respect to different mean wind speeds. The proposed stochastic model is finally applied to generate a large number of wind power spectra adapted to the mean extreme wind speed return period curves in typhoon and non-typhoon mixed climates. It is suggested that the present stochastic power spectra model can be applied to estimate the random response of structures at different return periods due to typhoon and non-typhoon winds, which can be extended to conduct the performance-based design and uniform-risk-based design of structures in wind engineering.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Damage Probability Assessment of Transmission Line-Tower System Under Typhoon Disaster, Based on Model-Driven and Data-Driven Views
    Hou, Hui
    Geng, Hao
    Huang, Yong
    Wu, Hao
    Wu, Xixiu
    Yu, Shiwen
    ENERGIES, 2019, 12 (08)
  • [32] STOCHASTIC DATA-DRIVEN HARDWARE RESILIENCE TO EFFICIENTLY TRAIN INFERENCE MODELS FOR STOCHASTIC HARDWARE IMPLEMENTATIONS
    Zhang, Bonan
    Chen, Lung-Yen
    Verma, Naveen
    2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2019, : 1388 - 1392
  • [33] Robustness to Incorrect System Models in Stochastic Control and Application to Data-Driven Learning
    Kara, Ali Devran
    Yuksel, Serdar
    2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2018, : 2753 - 2758
  • [34] Stochastic Evaluation of Indoor Wireless Network Performance with Data-Driven Propagation Models
    Bakirtzis, Stefanos
    Wassell, Ian
    Fiore, Marco
    Zhang, Jie
    2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, : 3587 - 3592
  • [35] Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool
    Hah, David
    Quilty, John M.
    Sikorska-Senoner, Anna E.
    ENVIRONMENTAL MODELLING & SOFTWARE, 2022, 157
  • [36] Data-driven Based Active Power Distribution Algorithm in Wind Farm
    Liu J.
    Zhang B.
    Zhao C.
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2019, 43 (17): : 125 - 131
  • [37] A Data-Driven Reserve Response Set Policy for Power Systems With Stochastic Resources
    Singhal, Nikita G.
    Li, Nan
    Hedman, Kory W.
    IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2019, 10 (02) : 693 - 705
  • [38] Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
    Guo, Yi
    Baker, Kyri
    Dall'Anese, Emiliano
    Hu, Zechun
    Summers, Tyler
    2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC), 2018, : 3840 - 3846
  • [39] Power Inductor Design Optimization with Data-Driven Magnetic Loss Models
    Solimene, Luigi
    Musumeci, Salvatore
    Ragusa, Carlo Stefano
    2024 27TH INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS, ELECTRICAL DRIVES, AUTOMATION AND MOTION, SPEEDAM 2024, 2024, : 755 - 761
  • [40] Data-Driven Component Cost Models for Power-Electronic Converters
    Fronczek, Carsten
    Fritz, Niklas
    De Doncker, Rik W.
    2023 25TH EUROPEAN CONFERENCE ON POWER ELECTRONICS AND APPLICATIONS, EPE'23 ECCE EUROPE, 2023,