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Experimental modal analysis of star-spar buoy using eigensystem realization algorithm and stochastic subspace identification methods.

机译：基于特征系统实现算法和随机子空间识别方法的星－梁浮标实验模态分析。

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摘要

The star-spar buoy employed in this study is a small buoy system for omni-directional wave energy harvesting, and it has been designed for the system to resonate at a dominant period of 2.25 seconds in heave motion. However, this design frequency must be verified through experiments. The objective of this study is thus to obtain the frequency and damping, associated with the heave motion of the star-spar buoy through tank and sea experiments. Two specific time domain modal identification techniques to be utilized in this study are eigensystem realization algorithm (ERA) and stochastic subspace identification (SSI). ERA is a deterministic (input-output) modal identification technique and SSI is a stochastic (output-only) technique.;Traditionally, the discrete Fourier transform of a digital signal has been employed as a signal decomposition technique, as well as a modal identification technique by picking the peaks from its Fourier spectrum. However, the purposes and concepts of signal decomposition and modal identification are very different. While the performance of a signal decomposition technique would be judged based on the fitting between the reconstructed signal and the original signal, that of a modal identification technique could be judged based on whether identified modal parameters are close to the true modal parameters. When true modal parameters are unknown, the performance of a modal identification technique usually would be judged based on a stabilization diagram.;When a response signal, from either the tank test or the sea test, is modeled as the sum of many damped harmonic components, the numerical studies in this thesis demonstrates that using ERA to estimate component frequencies and damping ratios, together with a least-squares solution for getting amplitudes and phase angles, is an excellent signal decomposition technique. For modal identification, SSI is found to be better than ERA, and is a very efficient method for both the tank and the sea test data.;In their theoretical derivations, both ERA and SSI methods assume that the dynamic system is a time-invariant linear system. However, the real buoy-fluid system under investigation must be a nonlinear system, thus to apply ERA or SSI, a first approximation is to treat the dynamic system to be piecewise linear, i.e. linear within a short period. In this study, introducing a sliding window is for assuming that the system is linear within the window duration. With this sliding window, an ERA-based time-frequency analysis, in parallel to the short time Fourier transform (STFT), has been conducted. It was concluded that using ERA-based analysis could overcome the frequency resolution and leakage problems.
机译：本研究中使用的星状浮标是一个用于全方位波能收集的小型浮标系统，它的设计目的是使系统在起伏运动中占主导地位的2.25秒产生共振。但是，必须通过实验验证此设计频率。因此，本研究的目的是通过坦克和海上实验来获得与星型浮标的升沉运动相关的频率和阻尼。本研究中使用的两种特定的时域模态识别技术是本征系统实现算法（ERA）和随机子空间识别（SSI）。 ERA是确定性（输入-输出）模式识别技术，SSI是随机（仅输出）技术;传统上，数字信号的离散傅里叶变换已被用作信号分解技术和模式识别通过从傅立叶光谱中选取峰来实现该技术。但是，信号分解和模式识别的目的和概念非常不同。虽然将基于重构信号和原始信号之间的拟合来判断信号分解技术的性能，但是可以基于所识别的模态参数是否接近真实的模态参数来判断模态识别技术的性能。当真实模态参数未知时，通常将基于稳定图来判断模态识别技术的性能;当将来自坦克试验或海上试验的响应信号建模为许多阻尼谐波分量的总和时本文的数值研究表明，使用ERA估计分量频率和阻尼比，以及使用最小二乘法求解振幅和相位角，是一种出色的信号分解技术。对于模态识别，发现SSI比ERA更好，并且是对油罐和海试数据都非常有效的方法;在理论推导中，ERA和SSI方法都假设动态系统是时不变的线性系统。但是，所研究的实际浮标流体系统必须是非线性系统，因此要应用ERA或SSI，第一近似方法是将动态系统处理为分段线性，即在短时间内线性。在本研究中，引入滑动窗口是为了假设系统在窗口持续时间内是线性的。利用该滑动窗口，已经进行了与短时间傅立叶变换（STFT）并行的基于ERA的时频分析。结论是，使用基于ERA的分析可以克服频率分辨率和泄漏问题。

著录项

  • 作者

    Zhang, Pengyu.;

  • 作者单位

    University of Rhode Island.;

  • 授予单位 University of Rhode Island.;
  • 学科 Engineering Marine and Ocean.
  • 学位 M.S.
  • 年度 2012
  • 页码 104 p.
  • 总页数 104
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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