Simulation of stationary non-Gaussian multivariate wind pressures based on moment-based piecewise Johnson transformation model

被引:9
|
作者
Wu, Fengbo [1 ]
Liu, Min [2 ]
Huang, Guoqing [2 ]
Peng, Liuliu [2 ]
Guo, Zengwei [1 ]
Jiang, Yan [3 ]
机构
[1] Chongqing Jiaotong Univ, State Key Lab Mt Bridge & Tunnel Engn, Chongqing 400074, Peoples R China
[2] Chongqing Univ, Sch Civil Engn, Chongqing 400044, Peoples R China
[3] Southwest Univ, CET Coll Engn & Technol, Chongqing 400700, Peoples R China
基金
中国国家自然科学基金;
关键词
Simulation; Non-Gaussian wind pressures; Statistical moments; Johnson transformation model; Piecewise Johnson transformation model; EXPANSION; LOAD;
D O I
10.1016/j.probengmech.2022.103225
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Fast and accurate simulation of non-Gaussian wind pressures are desired sometimes in the wind-resistant design of buildings under stationary winds. Recently, attracted by its wide application range, the moment-based Johnson transformation model (JTM) has been successfully applied by some authors of this paper. However, the simulation accuracy and efficiency of the moment-based JTM still need to be improved. Inspired by the successful application of the proposed newly defined statistical moments to the moment-based Hermite polynomial model (HPM), this paper applies these moments to the JTM named moment-based piecewise JTM (PJTM) and proposes a novel PJTM-based simulation method. In this method, a set of close-form formulas to estimate the parameters of PJTM are firstly proposed. Secondly, the analytical formula to determine the correlation distortion relationship by PJTM is further developed. Finally, the performance of the proposed method is verified by the very long non-Gaussian wind pressure data from a wind tunnel test. Results shown the proposed method by PJTM can not only present higher simulation efficiency but also better simulation accuracy compared with JTM and can present higher simulation efficiency compared with moment-based piecewise HPM.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Wind speed prediction using non-gaussian model based on Kumaraswamy distribution
    Shad, Mohammad
    Sharma, Y. D.
    Narula, Pankaj
    ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2024, 46 (01) : 719 - 735
  • [42] Statistical-based monitoring of multivariate non-Gaussian systems
    Liu, Xueqin
    Xie, Lei
    Kruger, Uwe
    Littler, Tim
    Wang, Shuqing
    AICHE JOURNAL, 2008, 54 (09) : 2379 - 2391
  • [43] Stationary response analysis for a linear system under non-Gaussian random excitation by the equivalent non-Gaussian excitation method and the Hermite moment model
    Tsuchida, Takahiro
    Kimura, Koji
    PROBABILISTIC ENGINEERING MECHANICS, 2023, 74
  • [44] Hybrid C- and L-Moment-Based Hermite Transformation Models for Non-Gaussian Processes
    Gao, S.
    Zheng, X. Y.
    Huang, Y.
    JOURNAL OF ENGINEERING MECHANICS, 2018, 144 (02)
  • [45] Adaptive Gaussian Filter Based on ICEEMDAN Applying in Non-Gaussian Non-stationary Noise
    Zhang, Yusen
    Xu, Zixin
    Yang, Ling
    CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2024, 43 (7) : 4272 - 4297
  • [46] Linear prediction and z-transform based CDF-mapping simulation algorithm of multivariate non-Gaussian fluctuating wind pressure
    Jiang, Lei
    Li, Chunxiang
    Li, Jinhua
    WIND AND STRUCTURES, 2020, 31 (06) : 549 - 560
  • [47] NUFFT-enhanced higher-order spectral representation for simulating multivariate non-Gaussian wind pressures
    Li, Xin
    Li, Shaopeng
    Jiang, Yan
    Yang, Qingshan
    Hui, Yi
    Wang, Yuhang
    Zeng, Jiadong
    JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2024, 250
  • [48] Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer
    Liu, Chao
    Fu, Li
    Yang, Dan
    Miller, David R.
    Wang, Junming
    ADVANCES IN ATMOSPHERIC SCIENCES, 2020, 37 (01) : 90 - 104
  • [49] Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer
    Chao LIU
    Li FU
    Dan YANG
    David R.MILLER
    Junming WANG
    AdvancesinAtmosphericSciences, 2020, 37 (01) : 90 - 104
  • [50] Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer
    Chao Liu
    Li Fu
    Dan Yang
    David R. Miller
    Junming Wang
    Advances in Atmospheric Sciences, 2020, 37 : 90 - 104