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随机结构TMD优化设计与概率密度演化研究

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摘要: 针对随机结构提出了TMD的优化设计方法,基于Gegenbauer多项式逼近法将随机结构转化为等效确定性扩阶系统,选取该扩阶系统传递函数的H∞范数为优化目标,采用遗传算法来优化TMD参数。此外,应用概率密度演化理论分析比较了随机结构TMD系统控制前后的结构随机地震响应和动力可靠度,并用蒙特卡罗法进行了验证。仿真分析结果表明:采用所提方法优化设计的TMD能有效地减小随机结构响应的峰值和方差,明显改善结构在地震激励下的动力可靠度,采用该方法优化设计的TMD具有良好的鲁棒性。关键词: 随机结构; 调谐质量阻尼器; 概率密度演化法; 动力可靠度; 遗传算法

中图分类号: TU311.3文献标志码: A文章编号: 10044523(2015)02028506

DOI:10.16385/j.cnki.issn.10044523.2015.02.015

引言

调谐质量阻尼器(TMD)作为一种有效的被动控制装置,已被广泛用于减小结构在风、地震等外部激励下的振动响应[1~5]。TMD的优化设计涉及对结构动力特性参数的真实估计。实际工程中各种不确定因素都会带来结构参数的不确定性,针对这种情况,将结构的某些参数视为随机变量可能更为恰当[6,7]。因此,优化TMD系统参数时应充分考虑结构参数随机性的影响[8]。

本文针对实际工程中所具有的随机性和不确定性,提出了随机结构TMD的优化设计方法。首先基于正交多项式理论将随机参数符合λPDF分布的随机结构化为一均方残差最小意义下的确定性等效扩阶系统[10,11],为了减小结构的随机性产生的不确定性对于TMD控制效果的影响,提出以扩阶系统响应传递函数的H∞范数为优化目标,采用遗传算法来进行TMD参数优化。最后结合数值算例应用概率密度演化理论(PDEM)[12,13]来分析比较随机结构TMD系统的响应和动力可靠度,并采用蒙特卡罗法(Monte Carlo simulation)来验证随机结构TMD优化设计方法的有效性。

6结论

本文基于正交多项式展开理论,提出了更符合工程实际的随机结构TMD的优化设计方法,并结合算例验证了所述方法的有效性。通过对全文的分析,可以得到以下主要结论:

(1)实际结构的参数不确定性对于结构响应影响较大,在合理设计TMD时需考虑结构参数的随机性。本文所提出的TMD优化设计方法其优化过程不依赖于特定的外激励,该优化设计有效降低了TMD控制效果对于结构随机参数的敏感性,提高了TMD控制效果的鲁棒性,具有重要的理论意义与工程应用前景;

(2)结构响应概率密度分布为多峰曲线,不服从正态分布、对数分布等常用概率分布,若直接用常用概率分布近似,可能会造成较大误差;

(3)采用本文所提出的方法优化设计的TMD有效降低了随机结构位移峰值均值,减小了响应的离散性,同时明显改善了随机结构在地震激励下的动力可靠度,体现了该方法的有效性。

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Abstract: This paper presents a robust optimal design procedure of TMD parameters for stochastic structures based on H∞ performance. A stochastic structure is first transformed into its deterministic equivalent system by Gegenbauer polynomial approximation method. Genetic algorithm is used in order to evaluate TMD′s optimum parameters through H∞ norm of the transfer function of the deterministic equivalent system as the objective function. The procedure of the optimal design of TMD is illustrated through a numerical example by the probability density evolution analysis. Numerical results show preliminarily that the optimized TMD can effectively suppress the undesirable vibration and improve structural dynamic reliability of the stochastic structure under the earthquake.

Key words: stochastic structure; tuned mass damper; probability density evolution method; dynamic reliability; genetic algorithm

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