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Impact of modeling and excitation uncertainties on operational and structural reliability of tension leg platforms

机译:建模和激励不确定性对张紧腿平台运行和结构可靠性的影响

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摘要

During their operation life-cycle, tension leg platforms (TLPs) may experience, under wave and wind loading, response amplitudes that affect their operational and structural reliability. Uncertainties related to the excitation characteristics (for example significant wave height or zero up-crossing period) or to the TLP-model properties (for example modulus of elasticity for tendons or location of center of mass) significantly impact the predicted dynamic response of the platform and ultimately the calculated reliability, or more generally the TLP-risk. A simulation-based, probabilistic framework is discussed here for detailed estimation of this risk and for identification of the importance of the different uncertain model parameters (i.e. risk factors). The TLP-risk is quantified as the expected value, over the established probability distributions for these uncertain parameters, of some chosen risk consequence measure. It is calculated using stochastic (Monte Carlo) simulation, which imposes no constraints on the complexity of the models considered and can facilitate an accurate estimation exploiting recent development in computer and computational science. The identification of the importance of the risk factors is established using an efficient, sampling-based global sensitivity analysis. An illustrative example is discussed in which risk is quantified in terms of the reliability for the structural integrity and operational serviceability for a rectangular TLP. The impact of uncertainties related to the excitation and TLP models is separately addressed, whereas the influence on the estimated risk of model prediction errors is also examined.
机译:在其运行生命周期中,张力腿平台(TLP)可能会在波浪和风荷载作用下经历影响其运行和结构可靠性的响应幅度。与激发特性(例如,显着的波高或零穿越时间)或TLP模型特性(例如,肌腱的弹性模量或质心的位置)相关的不确定性会显着影响平台的动态响应预测最终计算出的可靠性,或更普遍地说是TLP风险。本文讨论了基于模拟的概率框架,用于对此风险的详细估计以及确定不同不确定模型参数(即风险因素)的重要性。在某些不确定的风险结果度量的这些不确定参数的确定概率分布上,将TLP风险量化为期望值。它是使用随机(Monte Carlo)仿真计算的,它对所考虑的模型的复杂性没有任何限制,并且可以利用计算机和计算科学的最新发展促进准确的估计。使用有效的基于采样的全局敏感性分析来确定风险因素的重要性。讨论了一个示例性示例,其中根据矩形TLP的结构完整性可靠性和可操作性对风险进行了量化。与激励模型和TLP模型相关的不确定性的影响将分别解决,而对模型预测误差的估计风险的影响也将进行检查。

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