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首页> 外文期刊>Applied Ocean Research >Weak-mode identification and time-series reconstruction from high-level noisy measured data of offshore structures
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Weak-mode identification and time-series reconstruction from high-level noisy measured data of offshore structures

机译:从海上结构的高噪声测量数据中进行弱模式识别和时序重建

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摘要

The identification of true weak modes buried in high-level, noisy, measured data from offshore structures is a "practical but challenging problem because weak modes are typically eliminated as noise and rarely, yield a discrete time series. This study proposes a weak-mode identification and time-series reconstruction method for offshore structures when high-level noise is present. A theoretical development proposed in this study extends the traditional modal analysis to reconstructing the discrete time series of weak modes, thereby removing its previous limitations to only frequencies, damping ratios and mode shapes. Additionally, a second development proposed in this study makes the reconstructed time series not simply a combination of harmonic components from a Fourier transform but rather complex exponentials; the damping of the test structure is thus estimated with a better accuracy. A third theoretical development avoids variations in the results from different original signals by handling multiple signals simultaneously. The proposed approach primarily includes three steps: (1) estimate the poles and corresponding residues of high-level, noisy, measured data by converting high-order difference equations to first-order difference equations; (2) isolate the poles of weak modes by assigning multiple rough-pole windows, and subsequently extract the corresponding residues based on the row number of the isolated pole vector; and (3) identify and reconstruct the time series of the weak modes of interest in the form of complex exponentials. The most primary advantage of the proposed process in engineering applications is that the pole windows can be easily obtained and assigned from the relationship between the frequencies and their poles. Three numerical examples are studied: the first presents "the detailed numerical operation of the proposed method, the second extends the proposed method from managing one signal to managing multiple signals, and the third demonstrates the advantage of the approach compared with traditional methods. The numerical results indicate that the original signals can be decomposed into multiple complex exponentials with representative poles and corresponding residues, and that the new signals representing weak modes could be reconstructed by assigning a range of frequencies in terms of their relations with the poles. To study the performance of the proposed method when applied to offshore structures such as offshore platforms and marine risers, the experimental data from the high mode VIV experiments sponsored by the Norwegian Deepwater Programme (NDP) are used firstly. The results show that two dominant frequencies corresponding to the in-line and cross-flow directions can be identified simultaneously even one mode is very weak compared with the other, and the time series of the weak mode could be reconstructed with a rough frequency window. Then sea-test data of two offshore platforms are used: one was collected from the JZ20-2MUQ offshore platform when it was excited by ice, and the other was collected from the WZ11-4D platform when it was excited by waves. The results further demonstrated that a large model order is required to estimate all poles and residues of the original noisy signals, and that the row number corresponding to a weak mode of the isolated pole matrix could be easily determined via finite element analysis or engineering experiences.
机译:识别隐藏在海上结构的高水平,嘈杂,测量数据中的真正弱模式是一个“实际但具有挑战性的问题,因为弱模式通常作为噪声被消除,并且很少会产生离散的时间序列。该研究提出了一种弱模式存在高噪声时海上结构的识别和时间序列重建方法本研究提出的理论发展将传统的模态分析扩展到了重建弱模式的离散时间序列上,从而消除了以前仅对频率,阻尼的限制此外,本研究提出的第二项发展是使重建的时间序列不仅是傅立叶变换的谐波分量的组合,而且是复杂的指数;因此,可以更好地估计测试结构的阻尼。第三理论发展避免了不同原始信号的结果出现偏差同时处理多个信号。所提出的方法主要包括三个步骤:(1)通过将高阶差分方程转换为一阶差分方程来估计高电平,嘈杂的测量数据的极点和相应的残差; (2)通过分配多个粗糙极点窗口来隔离弱模的极点,然后根据孤立极点向量的行数提取相应的残差; (3)以复指数形式识别和重构弱关注模式的时间序列。在工程应用中所提出的方法的最主要优点是极窗可以很容易地获得并根据频率与其极之间的关系进行分配。研究了三个数值示例:第一个给出“该方法的详细数值运算,第二个将该方法从管理一个信号扩展到管理多个信号,第三个证明了该方法与传统方法相比的优势。数值”结果表明,可以将原始信号分解为具有代表性极点和相应残基的多个复指数,并且可以通过根据频率与极点的关系分配一定的频率范围来重构表示弱模式的新信号。将该方法应用于海上平台(如海上平台和海上立管)时,首先使用了由挪威深水计划(NDP)赞助的高模VIV实验的实验数据,结果表明,两个主要频率对应于线和横流方向可以同时识别自然地,即使一个模式与另一模式相比也非常弱,并且可以用一个粗糙的频率窗口来重构该弱模式的时间序列。然后使用两个海上平台的海试数据:一个是在JZ20-2MUQ海上被冰激发时采集的,另一个是从WZ11-4D平台被海浪激发时采集的。结果进一步表明,需要一个大的模型阶来估计原始噪声信号的所有极点和残差,并且可以通过有限元分析或工程经验轻松确定与孤立极点矩阵的弱模式相对应的行数。

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