振动疲劳下非高斯随机载荷中心矩的快速计算

IF 8.9 1区 工程技术 Q1 ENGINEERING, MECHANICAL Mechanical Systems and Signal Processing Pub Date : 2025-04-01 Epub Date: 2025-02-15 DOI:10.1016/j.ymssp.2025.112434
M. Palmieri , J. Slavič , F. Cianetti
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引用次数: 0

摘要

在振动疲劳分析中,采用谱法评价结构在随机振动下的疲劳损伤。谱方法在非高斯和非平稳载荷条件下失效,并提出了各种解决方案。校正系数是有希望的,并且取决于系统响应的峰度和偏度,这需要广泛的时域分析。进行时域分析会降低谱法的计算效率。本文提出了一种基于模态分解的方法,以数值有效地计算获得峰度和偏度所需的中心矩。在非高斯随机荷载作用下,对该方法进行了数值验证。该方法的结果与标准方法相同,计算时间减少了大约两个数量级。这扩展了谱方法与校正系数在频域的疲劳损伤数值估计的适用性,即使在非高斯载荷的情况下也是如此。
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Fast evaluation of central moments for non-Gaussian random loads in vibration fatigue
In vibration fatigue analysis, spectral methods are used to evaluate the fatigue damage of structures experiencing random vibrations. Spectral methods fail under non-Gaussian and non-stationary loading conditions and various solutions have been proposed. Correction coefficients are promising and depend on the kurtosis and skewness of the system’s response, which requires extensive time-domain analyses. Performing time-domain analysis undermines the computational efficiency of spectral methods. The present manuscript proposes a modal decomposition-based approach to numerically efficiently compute the central moments required to obtain the kurtosis and skewness. The proposed method is numerically validated on a structure subjected to non-Gaussian random loads. The proposed method demonstrates results identical to the standard approach, showing a reduction in computation time of around two orders of magnitude. This extends the applicability of spectral methods in conjunction with correction coefficients for numerical estimation of fatigue damage in the frequency domain even in the case of non-Gaussian loadings.
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来源期刊
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing 工程技术-工程:机械
CiteScore
14.80
自引率
13.10%
发文量
1183
审稿时长
5.4 months
期刊介绍: Journal Name: Mechanical Systems and Signal Processing (MSSP) Interdisciplinary Focus: Mechanical, Aerospace, and Civil Engineering Purpose:Reporting scientific advancements of the highest quality Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems
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