苏通大桥桥址区实测强风非平稳风特性分析

以2012年"达维"和"海葵"台风期间苏通大桥SHMS实测风数据为研究对象,采用游程检验法对桥址区实测风速进行了平稳性检验,对比分析了基于平稳和非平稳风速模型计算得到的平均风速风向(时变)、紊流强度、紊流功率谱密度等风特性,并采用基于小波变换(WT)的演变功率谱密度(EPSD)估计方法,进行了实测强风非平稳演变功率谱分析。结果表明,实测风速表现出明显的非平稳特性;由于时变趋势项的引入,非平稳风速模型比传统平稳风速模型能更好地表征实测强风特性;所得演变功率谱密度直观地展示了实测脉动风速能量的时频分布,EPSD均值与傅里叶变换谱吻合良好,验证了EPSD估计的可靠性。研究结果可为今后非平稳风场实测分析及数值模拟提供参考。     

分数阶导数系统响应功率谱密度的小波-Galerkin方法

利用小波的时间-频率联合分辨率,提出了一种在完全非平稳随机激励下,计算分数阶阻尼线性系统响应功率谱密度的方法。方法的思路在于选用广义谐和小波,并利用小波-Galerkin近似,将具有分数阶导数的运动微分方程转化为一组以响应小波变换为未知量的代数方程,解之并求得响应小波变换后,结合随机过程功率谱密度的小波变换表达得到激励与响应功率谱密度之间的关系。为此,在频域中推导了小波-Galerkin方法必需的小波整数阶与分数阶联系系数。数值算例表明:对具有不同分数阶导数的系统,所建议的方法具有较好的适用性。     

多自由度结构动力可靠度分析的小波方法

该文发展了基于小波分析的局部平稳法在多自由度结构动力可靠度中的应用。首先,基于广义谐和小波和随机过程的局部平稳小波模型,发展了线性多自由度结构系统在各时间-频率子域上激励功率谱与响应功率谱之间的关系,并计算得到了在一般随机动力激励下结构随机动力响应功率谱密度和各阶谱矩。随后,根据随机动力激励和响应的高斯假定及超越过程的Markov假定,得到了线性多自由度结构在均匀/非均匀调制随机激励下层间位移的动力可靠度指标。结构动力可靠度的Monte Carlo模拟显示了所提方法的可靠性与计算高效性。     

基于小波变换的非平稳脉动风时变功率谱估计方法研究

为揭示非平稳随机脉动风的时频特性,基于小波变换原理推导了时变功率谱的时间、频率和幅值与小波变换系数的关系,建立了非平稳随机脉动风时变功率谱估计的小波函数加权和法,并采用模拟非平稳脉动风和实测台风过程对理论推导结果进行了验证。研究结果表明:非平稳随机过程在某一时刻的不同尺度小波变换系数是一个以此非平稳随机过程的调制函数与小波函数的乘积为调制函数的非平稳随机过程的傅里叶变换,非平稳随机过程的时变功率谱等于不同尺度和不同时移的小波函数模平方的加权和,小波函数加权和法计算的非平稳随机脉动风的时变功率谱与理论结果具有良好的一致性。小波函数加权和法可有效地估计非平稳随机脉动风的时变功率谱,估计的时变功率谱可为进一步理解强(台)风的随机脉动特性奠定基础。     

非线性系统响应功率谱密度的小波-Galerkin方法

发展了广义谐和小波在确定非线性系统随机动力响应中的应用。首先,利用周期广义谐和小波展开非线性动力微分方程,并考虑小波的联系系数后,可将动力微分方程转化为一组非线性代数方程。其次,利用Newton迭代法数值解答了非线性代数方程,得到了非线性动力响应的小波变换。最后,根据响应时变功率谱与各阶小波变换之间的关系,计算求得了非线性动力响应的功率谱密度。数值模拟显示了本文建议方法与Monte Carlo模拟之间的吻合程度。     

地震地面运动局部谱密度的小波变换估计

已有的不少研究表明,地震地面运动的时频非平稳特性对结构响应有着很大的影响,对这种影响考虑的准确与否直接涉及结构分析的安全性。因此采用能反映时频非平稳特性的时变谱密度(即局部谱密度)来描述地震地面运动是非常必要的。首先从理论上比较了几种局部谱密度的估计方法,指出利用小波变换来估计局部谱密度在精度和速度方面有着相当的优势,有利于局部谱密度在地震动模型化、结构随机响应分析以及动力可靠度评价等方面的应用。然后通过具体算例,对理论分析进行了验证。     

特大型桥梁桩基完全非平稳随机地震反应分析

考虑地震动的随机性及频率和强度非平稳性,利用作者所提出的基于强度和能量控制的随机地震动模型,建立了非平稳随机地震反应分析方法。以某特大型桥梁群桩基础为研究对象,将群桩-土-桥墩结构体系作为一个整体进行了特大型桥梁桩基非平稳随机地震反应分析。首先,基于目标加速度时程的强度和能量信息确定了作为输入的加速度时-频演变功率谱密度;其次,分析了非平稳随机过程激励下自由场和桥梁桩基相互作用体系的地震反应,探讨了非平稳随机过程激励下的群桩-土-桥墩结构动力相互作用;最后,通过比较强度非平稳随机过程和完全非平稳随机过程激励下桥梁桩基相互作用体系的地震反应,探讨了频率非平稳性对相互作用体系地震反应的影响。     

周期广义谐和小波变换及重构

谐和小波和广义谐和小波皆为在频域上紧支且时域为无穷的正交小波,其频域分辨率很好但时域分辨率较差。虽然谐和小波在无穷时域上具有正交性,但其正交性在有限时域上却无法体现。针对这个缺点,在广义谐和小波的基础上,将广义谐和小波周期化后,进而提出了一种周期广义谐和小波（Periodic Generalized Harmonic Wavelet, PGHW）。PGHW的母小波在时域中可以表达为经平移后的若干谐和项之和,在频域中表现若干 函数之和,为一种以待分析信号持时为基本周期且在其上正交的离散广义谐和小波。基于PGHW在频域内的简单性,利用快速Fourier变换（FFT）技术实现了PGHW的快速小波变换及逆变换。文章最后的算例给出了某人工合成地震波的周期广义谐和小波变换及其重构,说明了所提算法的高效性与PGHW的完全重构性。     

基于局部平稳法的粘滞阻尼被动控制结构抗震可靠度分析

针对基于确定性激励的被动控制装置参数设计不具有普遍性的问题,提出了粘滞阻尼被动控制结构在一般非平稳随机地震动作用下抗震可靠度分析的局部平稳法。首先基于非平稳随机过程的局部平稳小波模型,提出了适用于临界阻尼比较大的粘滞阻尼被动控制结构的非平稳地震动输入-多自由度（受控）结构位移响应输出的功率谱关系。其次,根据超越过程的Markov过程假定及各阶响应谱矩,得到了受控结构层间位移的动力可靠度。数值分析结果表明：粘滞阻尼器在不同层间的配置,对受控结构的层间动力可靠度有显著影响。最后,以一个6层剪切型多自由度结构为例,对比了Monte Carlo模拟估计与本文所提方法计算的结构动力可靠度,验证了该方法的可靠性与高效性。     

基于规范反应谱的全非平稳地震动过程模拟

在经典的两类演变功率谱模型基础上,建议了另两类全非平稳地震动过程的演变功率谱模型。为生成与规范反应谱一致的全非平稳地震动时程,将地震动加速度过程分解为两个独立的随机过程,其一为已知演变功率谱的全非平稳地震动过程,其二为修正的非平稳地震动过程。对于第一个随机过程,应用非平稳过程模拟的谱表示-随机函数方法,即可生成代表性时程集合及其平均反应谱。对于第二个随机过程,其演变功率谱是由调制函数和功率谱密度函数组成,而功率谱密度函数则由第一个随机过程的平均反应谱与规范反应谱的拟合误差计算得到。通过对第二个随机过程演变功率谱的修正,可使合成后的全非平稳地震动过程的平均反应谱与规范反应谱拟合一致。最后,算例验证了此方法的有效性和实用性。     

Design search and optimization in aerospace engineering

In this paper, we take a design-led perspective on the use of computational tools in the aerospace sector. We briefly review the current state-of-the-art in design search and optimization (DSO) as applied to problems from aerospace engineering, focusing on those problems that make heavy use of computational fluid dynamics (CFD). This ranges over issues of representation, optimization problem formulation and computational modelling. We then follow this with a multi-objective, multi-disciplinary example of DSO applied to civil aircraft wing design, an area where this kind of approach is becoming essential for companies to maintain their competitive edge. Our example considers the structure and weight of a transonic civil transport wing, its aerodynamic performance at cruise speed and its manufacturing costs. The goals are low drag and cost while holding weight and structural performance at acceptable levels. The constraints and performance metrics are modelled by a linked series of analysis codes, the most expensive of which is a CFD analysis of the aerodynamics using an Euler code with coupled boundary layer model. Structural strength and weight are assessed using semi-empirical schemes based on typical airframe company practice. Costing is carried out using a newly developed generative approach based on a hierarchical decomposition of the key structural elements of a typical machined and bolted wing-box assembly. To carry out the DSO process in the face of multiple competing goals, a recently developed multi-objective probability of improvement formulation is invoked along with stochastic process response surface models (Krigs). This approach both mitigates the significant run times involved in CFD computation and also provides an elegant way of balancing competing goals while still allowing the deployment of the whole range of single objective optimizers commonly available to design teams.     

New formulation of Cholesky decomposition and applications in stochastic simulation

Monte Carlo simulation plays a significant role in the mechanical and structural analysis due to its versatility and accuracy. Classical spectral representation method is based on the direct decomposition of the power spectral density (PSD) or evolutionary power spectral density (EPSD) matrix through Cholesky decomposition. This direct decomposition of complex matrix usually results in large computational time and storage memory.In this study, a new formulation of the Cholesky decomposition for the EPSD/PSD matrix and corresponding simulation scheme are presented. The key idea to this approach is to separate the phase from the complex EPSD/PSD matrix. The derived real modulus matrix evidently expedites decomposition compared to the direct Cholesky decomposition of the complex EPSD/PSD matrix. In the proposed simulation scheme, the separated phase can be easily assembled. The modulus of EPSD/PSD matrix could be further decomposed into the modulus of coherence matrix (or lagged coherence matrix), which describes the basic coherence structure of stochastic process. The lagged coherence matrix is independence of time and thus remarkably improves the Cholesky decomposition efficiency.The application of the proposed schemes to Gaussian stochastic simulations is presented. Firstly, the previous closed-form wind speed simulation algorithm for equally-spaced locations is extended to a more general situation. Secondly, the proposed approach facilitates the application of interpolation technique in stochastic simulation. The application of interpolation techniques in the wind field simulation is studied as an example.     

Stochastic two-sided U-type assembly line balancing: a genetic algorithm approach

In this paper, a novel stochastic two-sided U-type assembly line balancing (STUALB) procedure, an algorithm based on the genetic algorithm and a heuristic priority rule-based procedure to solve STUALB problem are proposed. With this new proposed assembly line design, all advantages of both two-sided assembly lines and U-type assembly lines are combined. Due to the variability of the real-life conditions, stochastic task times are also considered in the study. The proposed approach aims to minimise the number of positions (i.e. the U-type assembly line length) as the primary objective and to minimise the number of stations (i.e. the number of operators) as a secondary objective for a given cycle time. An example problem is solved to illustrate the proposed approach. In order to evaluate the efficiency of the proposed algorithm, test problems taken from the literature are used. The experimental results show that the proposed approach performs well.     

Fast numerical methods for robust optimal design

A fast numerical approach for robust design optimization is presented. The core of the method is based on the state-of-the-art fast numerical methods for stochastic computations with parametric uncertainty. These methods employ generalized polynomial chaos (gPC) as a high-order representation for random quantities and a stochastic Galerkin (SG) or stochastic collocation (SC) approach to transform the original stochastic governing equations to a set of deterministic equations. The gPC-based SG and SC algorithms are able to produce highly accurate stochastic solutions with (much) reduced computational cost. It is demonstrated that they can serve as efficient forward problem solvers in robust design problems. Possible alternative definitions for robustness are also discussed. Traditional robust optimization seeks to minimize the variance (or standard deviation) of the response function while optimizing its mean. It can be shown that although variance can be used as a measure of uncertainty, it is a weak measure and may not fully reflect the output variability. Subsequently a strong measure in terms of the sensitivity derivatives of the response function is proposed as an alternative robust optimization definition. Numerical examples are provided to demonstrate the efficiency of the gPC-based algorithms, in both the traditional weak measure and the newly proposed strong measure.     

Evaluation model and algorithm of product disassembly process with stochastic feature

Disassembly planning is considered as the optimization of disassembly sequences with the target of the shortest disassembly time, the lowest disassembly cost, and the minimum disassembly energy consumption. However, obsolete products suffer from the influence of a variety of uncertainties, the disassembly process of products has the strong uncertain feature. Traditionally, to account for this uncertainty, each removal operation or removal task is assumed to be an activity or event with certain probability, and the determination of the optimal path of a disassembly process is merely a probabilistic planning problem based on this assumption. In this article, based on the established stochastic disassembly network graph, combined with different disassembly decision-making criterion, typical stochastic models for disassembly time analysis are developed. In addition, a two-phase approach is proposed to solve the typical stochastic models. Initially, according to different removal probability density functions, disassembly probability density functions of feasible disassembly paths are determined by a time-domain method or frequency-domain method, and additionally, after the disassembly probability density functions have been obtained, the quantitative evaluation of a product disassembly process and stochastic optimization of feasible disassembly paths are realized by a numerical solution method. Finally, a numerical example is illustrated to test the proposed concepts and the effectiveness of the proposed approach.     

Mathematical formulation and numerical treatment based on transition frequency densities and quadrature methods for non-homogeneous semi-Markov processes

Non-homogeneous semi-Markov processes (NHSMP) are important stochastic tools for modeling reliability metrics over time for systems where the future behavior depends on the current and next states as well as on sojourn and process times. The classical method to solve the interval transition probabilities of NHSMPs consists of directly applying any general quadrature method to some non-convolution integral equations. However, this approach has a considerable computational effort. Namely, N²-coupled integral equations with two variables must be solved, where N is the number of states. Therefore, this article proposes a more efficient mathematical formulation and numerical treatment, which are based on transition frequency densities and general quadrature methods respectively, for NHSMPs. The approach consists of only solving N-coupled integral equations with one variable and N straightforward integrations. Two examples in the context of reliability are also presented. The first one addresses a case where a semi-analytical solution is available. Then an example of application concerning pressure-temperature optical monitoring systems for oil wells is discussed. In both cases, the proposed approach is validated via the comparison against the results obtained from the semi-analytical solution (for the first example) as well as from both the classic and the Monte Carlo methods.     

Global sensitivity analysis for stochastic processes with independent increments

This paper is a first attempt to develop a numerical technique to analyze the sensitivity and the propagation of uncertainty through a system with stochastic processes having independent increments as input. Similar to Sobol’ indices for random variables, a meta-model based on Chaos expansions is used and it is shown to be well suited to address such problems. New global sensitivity indices are also introduced to tackle the specificity of stochastic processes. The accuracy and the efficiency of the proposed method is demonstrated on an analytical example with three different input stochastic processes: a Wiener process; an Ornstein–Uhlenbeck process and a Brownian bridge process. The considered output, which is function of these three processes, is a non-Gaussian process. Then, we apply the same ideas on an example without known analytical solution.     

Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD

Quality function deployment (QFD) is a planning and problem-solving tool that is renowned for translating customer requirements into the technical attributes of a product. To deal with the imprecise elements in the development process, fuzzy set theory is incorporated into QFD methodology. A novel fuzzy expected value operator approach is proposed in this paper to model the QFD process in a fuzzy environment, and two fuzzy expected value models are established to determine the target values of engineering characteristics in handling different practical design scenarios. Analogous to stochastic programming, the underlying philosophy in the proposed approach is based on selecting the decision with maximum expected returns. Furthermore, the proposed approach considers not only the inherent fuzziness in the relationships between customer requirements and engineering characteristics, but also the correlation among engineering characteristics. These two kinds of fuzzy relationships are aggregated to give the fuzzy importance of individual engineering characteristics. Finally, an example of a quality improvement problem of a motor car design is given to demonstrate the application and performance of the proposed modelling approach.     

地震激励邻接高层建筑的随机最优控制

采用控制设备联接相邻的高层建筑以降低其地震响应是一个切实有效的方法。基于随机动态规划原理与随机平均法,提出耦合相邻高层建筑的随机最优控制方法。先建立任意层数并在任意层高处控制联接的耦合结构的缩聚模型,再运用随机平均法导出关于模态能量的oIt随机微分方程,应用随机动态规划原理建立动态规划方程,由此可确定最优控制律。将结构的响应控制化为模态能量控制,缩减控制系统的维数。用高斯随机过程模拟地震激励,可计及其功率谱特性。数值结果表明该耦合结构随机最优控制方法的有效性。     

Iterative scenario based reduction technique for stochastic optimization using conditional value-at-risk

In the last decades, several tools for managing risks in competitive markets, such as the conditional value-at-risk, have been developed. These techniques are applied in stochastic programming models primarily based on scenarios and/or finite sampling, which in case of large-scale models increase considerably their size according to the number of scenarios, sometimes resulting in intractable problems. This shortcoming is solved in the literature using (i) scenario reduction methods, and/or (ii) speeding up optimization techniques. However, when reducing the number of scenarios, part of the stochastic information is lost. In this paper, an iterative scheme is proposed to get the solution of a stochastic problem representing the stochastic processes via a set of scenarios and/or finite sampling, and modeling risk via conditional value-at-risk. This iterative approach relies on the fact that the solution of a stochastic programming problem optimizing the conditional value-at risk only depends on the scenarios on the upper tail of the loss distribution. Thus, the solution of the stochastic problem is obtained by solving, within an iterative scheme, problems with a reduced number of scenarios (subproblems). This strategy results in an important reduction in the computational burden for large-scale problems, while keeping all the stochastic information embedded in the original set of scenarios. In addition, each subproblem can be solved using speeding-up optimization techniques. The proposed method is very easy to implement and, as numerical results show, the reduction in computing time can be dramatic, and more pronounced as the number of initial scenarios or samples increases.