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首页> 外文期刊>Ocean Engineering >Stochastic identification of the structural damage condition of a ship bow section under model uncertainty
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Stochastic identification of the structural damage condition of a ship bow section under model uncertainty

机译：模型不确定性下船首部结构损伤状态的随机辨识

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A accurate, quantifiable means of assessing structural damage condition are paramount for maintaining the structural integrity of ship hull forms. Toward this end, precise knowledge of the location and magnitude of any imperfections (i.e. geometric imperfections in the form of denting and corrosion. patches) must be determined, along with concomitant uncertainties accompanying such predictions. The current paper describes a non-contact approach to identifying and characterizing such imperfections within the submerged bow section of a representative ship hull. By monitoring the pressure field local to the acoustically excited hull section, it is shown how the resulting data can be used to identify the parameters describing the structural damage field. In order to perform the identification, a fluidstructure model that predicts the spatio-temporal pressure field is required. A Bayesian, reversible jump Markov chain Monte Carlo approach is then used to generate the imperfection parameter estimates and quantify the uncertainty in those estimates. This approach is particularly appealing as it does not allow for the damage model to be explicitly known a priori. Convergence of the Markov chains is assessed, and estimates of the Monte Carlo standard error (MCSE) are provided. (C) 2015 Elsevier Ltd. All rights reserved.
机译：评估结构损坏状况的准确，可量化的手段对于维持船体形式的结构完整性至关重要。为此，必须确定对任何缺陷（即凹陷和腐蚀斑块形式的几何缺陷）的位置和大小的精确了解，以及伴随这种预测的不确定性。当前的论文描述了一种非接触式方法，用于识别和表征代表性船体的水下船首部分内的此类缺陷。通过监视声激发的船体截面局部的压力场，显示了如何将所得数据用于识别描述结构损伤场的参数。为了执行识别，需要一个预测时空压力场的流体结构模型。然后，使用贝叶斯可逆跳跃马尔可夫链蒙特卡洛方法来生成缺陷参数估计，并对这些估计中的不确定性进行量化。这种方法特别吸引人，因为它不允许先验地明确知道损坏模型。评估了马尔可夫链的收敛性，并提供了蒙特卡洛标准误差（MCSE）的估计值。 （C）2015 Elsevier Ltd.保留所有权利。

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