基于非线性波浪建模的近海结构长期响应预测

N. A. Mukhlas, N. I. Mohd Zaki, M. K. Abu Husain, S. S. Syed Ahmad, Chiew Teng Ng, Mohamad Shazwan Ahmad Shah, S. Umar, Norhazilan Md Noor
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引用次数: 0

摘要

风产生的随机波浪载荷是保持固定海上结构可靠性所需的主要载荷。基于概率技术,波浪荷载的固有随机性可用于预测极端近海结构响应,其性质为高斯。然而,研究人员发现,水动力成分和结构动力学会对频谱产生重大影响,从而导致非高斯随机近海结构响应。与蒙特卡罗时间模拟这种传统方法相比,有限记忆非线性系统(FMNSNL)已被证明是评估非高斯随机海上结构响应的有效方法。然而,该分析仅基于短期分布进行。最令人满意的分析是基于长期分布的分析。因此,本文将进一步研究评估极端海上结构响应的长期概率分布。结果,与蒙特卡罗时间模拟相比,100 年极端海上结构响应预测的准确率达到了 80% 至 96%。在整个研究过程中,概率分布一直使用 Gumbel 分布函数进行评估。然而,模拟响应值与拟合线之间的分布尾端仍存在些许偏差。建议在今后的工作中采用不同的分布函数,如广义极值分布(GEV)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Long-Term Offshore Structural Responses Based on Nonlinear Wave Modeling
Wind-generated random wave loads are the dominant loads to consider for maintaining the reliability of fixed offshore structures. Based on probabilistic techniques, the inherent randomness of the wave loading can be used to predict extreme offshore structural response, which is Gaussian in nature. However, researchers have found that the hydrodynamic component and structural dynamics substantially impact the frequency spectrum, leading to a non-Gaussian stochastic offshore structural response. A finite-memory non-linear system (FMNSNL) has been proven to be an efficient approach to evaluate the non-Gaussian stochastic offshore structural response compared to the conventional method, Monte Carlo time simulation. However, the analysis has been conducted based on short-term distribution only. The most satisfactory analysis is based on long-term distribution. Hence, further investigation in this paper will evaluate the long-term probability distribution of extreme offshore structural responses. As a result, a 100-year extreme offshore structural response prediction achieves 80% to 96% accuracy compared to the Monte Carlo time simulation. The probability distribution has been evaluated using the Gumbel distribution function throughout this investigation. Still, there is a little deviation at the tail end of the distribution between the simulated response values and the fitted line. A different distribution function, such as the Generalised Extreme Value (GEV) distribution, is advised for future work.
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