基于多元经验模态分解的S变换高精度数据驱动非平稳风速模拟

doi:10.12066/j.issn.1007-2861.2529

摘要/Abstract

摘要： 为了提高S变换(S transform,ST)模拟风速的精度,提出了基于多元经验模态分解(multivariate empirical mode decomposition,MEMD)的ST高精度数据驱动非平稳风速模拟.选取一组实测多变量非平稳风速,采用MEMD得到各阶固有模态函数(intrinsic mode function,IMF)并进行ST,通过本征正交分解(proper orthogonal decomposition,POD)考虑多变量间的相关性后,再采用逆ST (inverse ST,IST),加入随机初始相位,得到模拟风速.对该方法进行了时频分析研究,结果表明:该方法的模拟结果可以保留时域内非平稳风速的能量特征;得到的ST系数幅值在时频域内的能量分布接近实测风速;对平均功率谱进行量化对比,证明了该方法的模拟精度要优于基于时频功率谱密度(time-frequency power spectral density,TFPSD)的ST模拟方法.

关键词: 非平稳风速, 多元经验模态分解, S变换, 逆S变换, 数值模拟

Abstract: To enhance the accuracy of wind speed simulations using S transform (ST), this paper proposed a high-precision data-driven approach based on multivariate empirical mode decomposition (MEMD) for nonstationary wind speed simulations in the field of structural wind engineering. A set of measured nonstationary wind speeds with multiple variables was selected. Intrinsic mode functions (IMFs) were obtained via MEMD and subjected to ST. The correlation between multiple variables was determined by employing a proper orthogonal decomposition (POD). Inverse ST (IST) was applied by incorporating random initial phases to generate simulated wind speeds. A time-frequency analysis was conducted to evaluate the proposed method. The results demonstrated that the proposed method effectively retained the energy characteristics of nonstationary wind speeds in the time domain. The amplitude distributions of the resulting ST coefficients closely resembled that of the measured wind speeds in the time-frequency domain. Quantitative comparisons of the average power spectrum confirmed the superior simulation accuracy of the proposed method compared with the ST simulation method based on the time-frequency power spectral density (TFPSD).

Key words: nonstationary wind speed, multivariate empirical mode decomposition (MEMD), S transform (ST), inverse S transform (IST), numerical simulation

中图分类号:

TU311

刘枫枫, 李春祥, 曹黎媛. 基于多元经验模态分解的S变换高精度数据驱动非平稳风速模拟[J]. 上海大学学报(自然科学版), 2026, 32(4): 700-713.

LIU Fengfeng, LI Chunxiang, CAO Liyuan. High-precision data-driven simulation of nonstationary wind speeds by S transform based on multivariate empirical mode decomposition[J]. Journal of Shanghai University（Natural Science Edition）, 2026, 32(4): 700-713.

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基于多元经验模态分解的S变换高精度数据驱动非平稳风速模拟

High-precision data-driven simulation of nonstationary wind speeds by S transform based on multivariate empirical mode decomposition

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