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针对导管架平台实测数据少且修正效果差的问题，本文提出了一种基于反向Kullback-Leiler(KL)散度的两阶段型随机模型修正方法。该方法的核心在于利用Kriging模型替代有限元模型进行计算，并分为两个修正阶段：第一阶段为频率距离修正；第二阶段为频率分布差异修正。首先，假设结构修正参数和模态参数服从高斯分布，从而将不确定模型修正问题转化为均值和标准差的修正问题；其次，以某导管架平台为研究对象，研究了所提方法在修正参数均值及标准差均未知的情况下的修正效果，探究了不同数量实测数据对修正结果的影响；最后，将该方法应用于渤海湾某导管架平台上。实验结果表明，修正后，导管架有限元模型频率与实测频率的均值误差小于0.3%,标准差误差小于7%。这一显著的修正效果充分证明了所提方法的有效性和实用性，为其在实际工程中的广泛应用奠定了坚实的基础。
Abstract：Aiming at the problems of limited measured data and poor updating effect of jacket platform, a two-stage stochastic model updating method based on inverse Kullaback-Leibler(KL) divergence is proposed in this paper: The constructing Kriging models as substitutes for finite element models in computations, the first stage is frequency distance updating; The second stage is frequency distribution difference updating. Firstly, assuming that the structural parameters to be updated and the modal parameters obey the Gaussian distribution, an updating problem of a model with uncertainty is transformed into the mean and standard deviation. Secondly, taking the jacket platform as an example, the correction effect of the proposed method when the mean and standard deviation of the modified parameters were unknown was studied, and the influence of different amounts of frequency data on the correction results was explored. Finally, the proposed method was applied to a jacket platform in Bohai Bay, The results show that the mean frequency correction error of the platform was less than 0.3%, and the standard deviation correction error was less than 7%, the proposed method can be applied to actual engineering.
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基本信息:
DOI：10.16441/j.cnki.hdxb.20240002
中图分类号:P75
引用信息:
[1]李韵慧,蒋玉峰,马春可,等.基于Kriging模型及反向Kullback-Leiler散度的老龄化平台随机模型修正方法[J].中国海洋大学学报(自然科学版),2025,55(09):125-136.DOI:10.16441/j.cnki.hdxb.20240002.
基金信息:
国家自然科学基金项目(52301349,52088102)资助~~
2024-01-03
2024
2024-04-16
2024
2024-03-31
1
2025-08-26
2025-08-26