与反应谱相容的多点完全非平稳地震动随机过程的快速模拟

为了精确评估结构地震响应的概率特性,地震动随机过程的模拟需要考虑时间变异性(频率和强度非平稳)、空间变异性以及与反应谱的相容性。在经典的多点完全非平稳随机过程的模拟方法中,由于频率与时间变量不可分离,演化功率谱矩阵分解效率较低。为了加快谱矩阵的分解,提出了新Cholesky分解方法。该方法的核心是将演化谱矩阵分离为相位和模矩阵,而模矩阵进一步被转化为与时间不相关的延迟相干矩阵。通过与时间相关的演化谱矩阵相比,延迟相干矩阵仅与频率相关,这样就显著提高了矩阵分解的效率;此外,延迟相干矩阵更适合采用插值技术。最后,将新Cholesky分解方法和插值技术应用到生成与反应谱相容的随机方法中。结果表明:新Cholesky分解与插值能够高效地模拟多点完全非平稳并且与反应谱相容的地震动样本;线性插值与三次样条插值均可达到良好的分辨率,少量的插值点即可满足精度的要求。     

An improved approximation for the spectral representation method in the simulation of spatially varying ground motions

The spectral representation method (SRM), based on the Cholesky decomposition of either cross spectral density matrix or lagged coherency matrix, is widely used in the simulation of spatially varying ground motions. In this study, the SRM, based on the decomposition of lagged coherency matrix, is modified to apply to the common case which the auto spectral densities of simulation points are not the same. When using interpolation approximation approach to improve the efficiency, the SRM based on the decomposition of lagged coherency matrix exhibits much higher accuracy than the SRM based on the decomposition of cross spectral density matrix, because the elements of lower triangular matrix obtained by the Cholesky decomposition of lagged coherency matrix vary slowly with the frequency. Therefore, the SRM, based on the decomposition of lagged coherency matrix, is generally suitable for the combination with the interpolation approximation approach.     

Dimension reduction model for two-spatial dimensional stochastic wind field: Hybrid approach of spectral decomposition and wavenumber spectral representation

A renewed methodology for simulating two-spatial dimensional stochastic wind field is addressed in the present study. First, the concept of cross wavenumber spectral density (WSD) function is defined on the basis of power spectral density (PSD) function and spatial coherence function to characterize the spatial variability of the stochastic wind field in the two-spatial dimensions. Then, the hybrid approach of spectral representation and wavenumber spectral representation and that of proper orthogonal decomposition and wavenumber spectral representation are respectively derived from the Cholesky decomposition and eigen decomposition of the constructed WSD matrices. Immediately following that, the uniform hybrid expression of spectral decomposition and wavenumber spectral representation is obtained, which integrates the advantages of both the discrete and continuous methods of one-spatial dimensional stochastic field, allowing for reflecting the spatial characteristics of large-scale structures. Moreover, the dimension reduction model for two-spatial dimensional stochastic wind field is established via adopting random functions correlating the high-dimensional orthogonal random variables with merely 3 elementary random variables, such that this explicitly describes the probability information of stochastic wind field in probability density level. Finally, the numerical investigations of the two-spatial dimensional stochastic wind fields respectively acting on a long-span suspension bridge and a super high-rise building are implemented embedded in the FFT algorithm. The validity and engineering applicability of the proposed method are thus fully verified, providing a potentially effective approach for refined wind-resistance dynamic reliability analysis of large-scale complex engineering structures.     

考虑相位角的脉动风场模拟

为克服考虑相位角后风谱密度矩阵可能不正定而无法进行Cholesky分解的困难,提出了一种基于插值技术的改进风场模拟方法。首先依据风谱密度矩阵的正定性或不正定性将模拟风频范围划分为正定区间和不正定区间。在正定区间内,可直接将风谱密度矩阵进行Cholesky分解。而在不正定区间内,可采用插值技术获得风谱密度矩阵的分解式。然后运用谐波合成法模拟出全部风频范围内的脉动风速。数值算例表明:该方法具有较高的精度,且模拟结果能较好地符合自然风的基本特性。     

基于Hermite插值的简化风场模拟

针对传统Deodatis谐波合成法的模拟效率受Cholesky分解次数制约的问题,通过对互谱密度矩阵分解引入Hermite插值,推导了基于Hermite插值的简化风场模拟方法,将传统谐波合成法中的Cholesky分解次数由n×N次缩减为n×2k次（2k < N）,从而大幅度提升了传统谐波合成法的计算效率。以某大跨度三塔悬索桥主梁风场模拟为例,分别基于传统Deodatis法、三次Lagrange插值法、Hermite插值法模拟了时长为4096 s的脉动风速时程,三者在模拟耗时与模拟精度方面的对比表明:Hermite插值法与Lagrange插值法均能显著提高传统谐波合成法的模拟效率;Hermite插值法的模拟效率略低于三次Lagrange插值法,但其对H矩阵的模拟精度明显高出一个层次,因而Hermite插值法在风场模拟中表现更优。采用基于Hermite插值的简化方法,模拟脉动风速的功率谱与相关函数均能与目标值吻合较好,表明所模拟的脉动风速仍具有较高的保真度。在此基础上,通过插值间距的优化分析给出了插值间距的建议取值区间。     

超高层建筑脉动风速场模拟的改进谐波合成法

针对传统的谐波合成法模拟超高层建筑脉动风速场存在计算量大的问题,提出了考虑互谱密度矩阵为复数矩阵的一般情况,使用插值精度较高的样条插值技术来减少Cholesky谱矩阵分解的次数,再使用FFT技术进一步加快模拟计算速度的改进谐波合成法.为了阐明改进谐波合成法具有较好的模拟精度和较快的计算速度, 运用该方法模拟了一栋225 m高的超高层建筑脉动风速场,并与仅使用FFT技术的谐波合成法即传统谐波合成法的模拟结果进行了对比分析.模拟结果的对比分析表明:尽管改进后的谐波合成法对谱分解矩阵采用了插值近似措施,但模拟的随机风速样本仍具有很好的精度,计算效率比传统的谐波合成法有了较大的提高.     

具有桥塔风效应的桥梁风场数值模拟

将特征正交分解型谱表示法运用于模拟具有桥塔风效应的桥梁风场中。首先介绍桥塔风效应和桥梁风场的概率描述,然后结合模态截断技术,介绍特征正交分解（Proper Orthogonal Decomposition,POD）型谱表示法,该方法是对常用的原型谱表示法的继承和提高,且物理概念更加清晰。通过引入对风速谱矩阵的显式预分解,推导模拟具有桥塔风效应的桥梁风场的简化计算公式,将对付目标功率谱矩阵的特征值分解运算简化为对实矩阵的运算。该方法可用FFT加速,相对于原有的模拟方法具有较高的计算效率。最后,以模拟龙潭河特大桥施工最大双悬臂阶段的脉动风速场为算例,解释了脉动风速过程特征正交分解模态的物理意义,说明该方法的可靠性。在算例中,观察到复杂相关结构下,特征正交分解发生振型交换的现象,并分析其原因。     

基于波数-频率联合演变功率谱的一维空间非均匀脉动风场模拟

在高层建筑、大跨桥梁和大型风力发电系统等大型工程中,风荷载对结构的安全性至关重要、甚至是起控制作用的要素。因此,脉动风速场的模拟具有重要意义。谱表达方法得到了广泛的应用。但经典的谱表达方法需要对互功率谱矩阵的逐个频率点进行Cholesky分解或本征正交分解（POD）,当模拟的空间点数较多时,分解效率非常低下、甚至可能出现矩阵奇异而难以实现。基于波数-频率联合功率谱或联合演变谱,不需要Cholesky分解或POD,可以方便地实现均匀或非均匀脉动风场的模拟。当模拟点为等间距点时,该方法能够使用FFT技术提高模拟效率,而当模拟点为非等间距点、不能使用FFT技术时,模拟效率依然有待提高。鉴于此,该文引入“结构化”非均匀离散方法和“舍选法”思想,建议了二维波数-频率域的非均匀离散策略,显著地提高了模拟效率。以某桥塔的一维空间非均匀脉动风场的数值模拟为例,验证了该方法的有效性。     

An efficient space–time based simulation approach of wind velocity field with embedded conditional interpolation for unevenly spaced locations

The classical spectral representation method (SRM) has been extensively used in the simulation of multivariate stationary Gaussian random processes. Due to the application of fast Fourier transform (FFT), the simulation is usually efficient. However, for processes with a large number of simulation points, it becomes necessary to enhance the simulation efficiency. One example is the wind velocity field along a large-span bridge, where hundreds of wind velocity fluctuations are required. In the case of bridges built over a homogeneous terrain such as coastal area or flat plain, the wind velocity field can be modeled as a multivariate homogeneous random process i.e., the auto power spectral densities (PSDs) at evenly-spaced simulation points are same and the cross PSD is a function of separation distance between two simulation points. Furthermore, in some applications, additional simulation points need to be included to a set of uniformly distributed points in order to make the wind velocity field consistent to the structural dynamic analysis requirement.In this paper, a hybrid approach of space–time random-field based SRM and proper orthogonal decomposition (POD)-based interpolation is developed for simulating the above wind velocity process. In this approach, the random-field based SRM is used to simulate the multivariate homogeneous random process composed of a set of uniformly distributed simulation locations while POD-based interpolation is used to conditionally generate the wind velocities at a few unevenly distributed points using the previously simulated wind velocities. The idea of the former is based on transforming the simulation of the homogeneous random process into that of the corresponding space–time random field where the phase angle is assumed to be zero and the coherence function must be an even function in terms of separation distance. Through this procedure, customary requirement for spectral matrix decomposition is eliminated and application of two dimensional FFT can improve the simulation efficiency dramatically. The shortcomings of this method include a slight approximation regarding the simulated sample and the non-ergodicity for the correlation function. The numerical example of a homogeneous wind velocity field along a bridge deck shows that the proposed random field-based method is very efficient in terms of accuracy and efficiency when the number of simulation locations is large and the POD-based interpolation also has good performance.     

Simulation of stationary Gaussian/non-Gaussian stochastic processes based on stochastic harmonic functions

A new model is proposed to represent and simulate Gaussian/non-Gaussian stochastic processes. In the proposed model, stochastic harmonic function (SHF) is extended to represent multivariate Gaussian process firstly. Compared with the conventional spectral representation method (SRM), the SHF based model requires much fewer variables and Cholesky decompositions. Then, SHF based model is further extended to univariate/multivariate non-Gaussian stochastic process simulation. The target non-Gaussian process can be obtained from the corresponding underlying Gaussian processes by memoryless nonlinear transformation. For arbitrarily given marginal probability distribution function (PDF), the covariance function of the underlying multivariate Gaussian process can be determined easily by introducing the Mehler’s formula. And when the incompatibility between the target non-Gaussian power spectral density (PSD) or PSD matrix and marginal PDF exists, the calibration of the target non-Gaussian spectrum will be required. Hence, the proposed model can be regarded as SRM to efficiently generate Gaussian/non-Gaussian processes. Finally, several numerical examples are addressed to show the effectiveness of the proposed method.     

An efficient simulation approach for multivariate nonstationary process: Hybrid of wavelet and spectral representation method

Currently, the classical spectral representation method (SRM) for nonstationary process simulation is widely used in the engineering community. Although this scheme has the higher accuracy, the time-dependent spectra results in unavailability of fast Fourier transform (FFT) and thus the simulation efficiency is lower. On the other hand, the approach based on stochastic decomposition can apply FFT in the simulation. However, the algorithm including the fitting procedure is relatively complicated and thus limits its use in practice.In this paper, the hybrid efficient simulation method is proposed for the vector-valued nonstationary process, which contains the spectra decomposition via wavelets and SRM. This method can take advantage of FFT and is also straightforward to engineering application. Numerical examples are employed to evaluate the proposed method. Results show that the method performs fairly well for the scalar process and vector-valued process with real coherence function. In the case of complex coherence function, the majority of the phase in the coherence function cannot be remained in the simulation. In addition, the validity of proper orthogonal decomposition (POD) in nonstationary process simulation via the decomposition of the time-dependent nonstationary spectra is studied. Analysis shows that the direct use of POD in nonstationary spectra decomposition may not be useful in nonstationary process simulations.     

大跨度结构随机脉动风场的快速模拟方法

针对传统谐波合成法计算量巨大、内存耗费多的缺点,研究了空间各点的分解功率谱密度函数曲线随频率变化的特点。在此基础上,提出采用三次均匀B样条插值方法来拟合分解谱密度函数曲线,引入矩阵Cholesky分解的优化递归算法来加速矩阵分解速度,同时利用FFT技术来减少谐波项合成所需要的时间。经过上述的改进,可以大大提高双索引频率下谐波合成法的计算效率。最后利用两个算例表明,这种方法可以高效、准确地模拟出适合大跨度结构的随机脉动风场。     

基于双POD模型的空间相关三维随机风场数值模拟

POD法提供了一种高效、准确的风荷载模拟方法,通过对风场的功率谱密度矩阵进行Schur分解,得到一系列的特征值和特征向量,选取主要的几阶特征模态进行计算就可以得到比较精确的结果。该文讨论了具有空间相关性三维随机风场的数学模型,利用双POD模型和蒙特卡罗模拟法,详细描述了空间相关三维风场的数值模拟方法。通过大跨越输电塔三维风场的数值模拟研究表明,模拟的顺风向、横风向、竖直向风速的功率谱密度函数与理论值较为符合,并且具有较好的随机性。证实了该文提出的方法是一种高效、准确的结构三维风场模拟方法,并可应用于大跨空间结构、高层建筑及大跨度桥梁等结构之中。     

Reducing the peak-to-average power ratio using unitary matrix transformation

A theoretical analysis is presented to show that in orthogonal frequency division multiplexing systems, the peak-to-average power ratio (PAPR) can be reduced by performing a unitary matrix transformation on the input vector of N symbols. The authors also prove that this transformation does not degrade the bit error rate (BER) or power spectral density (PSD) performance. Based on this, the inverse discrete Fourier transform matrix is proposed as the unitary matrix to reduce the PAPR. The simulation results show that the proposed scheme can obtain significant PAPR reduction while maintaining good performance in the BER and the PSD. To further evaluate the performance of the proposed scheme, the authors compare it with some well known PAPR reduction techniques by simulations. It is demonstrated that the proposed scheme can offer better system performance and achieve a better compromise with regard to the PAPR reduction, BER, spectral efficiency and computational complexity.     

大跨越输电塔线体系随机脉动风场模拟研究

为在时域内分析大跨越输电塔-线体系风振响应,根据结构体型特征和脉动风场的功率谱特性,考虑输电塔-线分布、平均风剖面变化、功率谱能量与相干性等影响因素,提出了简化作用于输电塔线体系的多变量三维脉动风场(n-V-3D)为多变量一维脉动风场(n-V-1D)分析方法。结合输电塔线体系有限元法风振响应分析的特点,应用谐波叠加法和谱分解的适当修正,建立了脉动风速时程数值模拟方法。实例模拟表明,数据符合统计检验,模拟功率谱与目标谱吻合,从而验证了模拟方法的有效性和模拟脉动风速时程适用性。     

Semi-active control of wind excited building structures using MR/ER dampers

A semi-active control strategy for building structures subject to wind loading and controlled by MR/ER dampers is proposed. The power spectral density (PSD) matrix of the fluctuating part of wind velocity vector is diagonalized in the eigenvector space. Each element of the diagonalized PSD matrix is modeled as a set of second-order linear filter driven by white noise. A Bingham model for MR/ER dampers is used. The forces produced by MR/ER dampers are split into passive and active parts and the passive part is combined with structural damping forces. A set of partially averaged Itô equations for controlled modal energies are derived by applying the stochastic averaging method for quasi-integrable-Hamiltonian systems. The optimal control law is then determined by using the stochastic dynamical programming principle and the cost function is so selected that the optimal control law can be implemented by the MR/ER dampers. The response of semi-active controlled structures is predicted by using the reduced Fokker–Planck–Kolmogorov equation associated with fully averaged Itô equations of the controlled structures. A comparison with clipped linear quadratic Gaussian (LQG) control strategy, for an example, shows that the proposed semi-active control strategy for MR/ER dampers is superior to clipped LQG control strategy.     

非平稳随机过程功率谱密度估计的小波方法

讨论了已有文献中基于一般非正交小波以及广义谐和小波的非平稳随机过程演变功率谱密度（Evolutionary Power Spectral Density, EPSD）估计的问题。在一种新的非平稳随机过程模型（局部平稳小波过程,Locally Stationary Wavelet process, LSW）的基础上,提出了一种新的估计非平稳随机过程时变功率谱密度的方法。所建议的新方法能与估计非平稳随机过程EPSD的经典方法统一起来,当以上两种方法均使用广义谐和小波时,二者退化为同一形式。为了验证所建议方法的有效性,给出了基于广义谐和小波的多变量均匀调制下非平稳随机地震动互/自功率谱估计的算例。并以汶川8.0级地震中某近场地及远场地上的地震加速度为例,计算得到了其能量在时－频域上的不同分布。     

Simulation of wind velocity time histories on long span structures modeled as non-Gaussian stochastic waves

A methodology is proposed for efficient and accurate modeling and simulation of correlated non-Gaussian wind velocity time histories along long-span structures at an arbitrarily large number of points. Currently, the most common approach is to model wind velocities as discrete components of a stochastic vector process, characterized by a Cross-Spectral Density Matrix (CSDM). To generate sample functions of the vector process, the Spectral Representation Method is one of the most commonly used, involving a Cholesky decomposition of the CSDM. However, it is a well-documented problem that as the length of the structure – and consequently the size of the vector process – increases, this Cholesky decomposition breaks down numerically. This paper extends a methodology introduced by the second and fourth authors to model wind velocities as a Gaussian stochastic wave (continuous in both space and time) by considering the stochastic wave to be non-Gaussian. The non-Gaussian wave is characterized by its frequency–wavenumber (FK) spectrum and marginal probability density function (PDF). This allows the non-Gaussian wind velocities to be modeled at a virtually infinite number of points along the length of the structure. The compatibility of the FK spectrum and marginal PDF according to translation process theory is secured using an extension of the Iterative Translation Approximation Method introduced by the second and third authors, where the underlying Gaussian FK spectrum is upgraded iteratively using the directly computed (through translation process theory) non-Gaussian FK spectrum. After a small number of computationally extremely efficient iterations, the underlying Gaussian FK spectrum is established and generation of non-Gaussian sample functions of the stochastic wave is straightforward without the need of iterations. Numerical examples are provided demonstrating that the simulated non-Gaussian wave samples exhibit the desired spectral and marginal PDF characteristics.     

A reduced polynomial chaos expansion method for the stochastic finite element analysis

The stochastic finite element analysis of elliptic type partial differential equations is considered. A reduced method of the spectral stochastic finite element method using polynomial chaos is proposed. The method is based on the spectral decomposition of the deterministic system matrix. The reduction is achieved by retaining only the dominant eigenvalues and eigenvectors. The response of the reduced system is expanded as a series of Hermite polynomials, and a Galerkin error minimization approach is applied to obtain the deterministic coefficients of the expansion. The moments and probability density function of the solution are obtained by a process similar to the classical spectral stochastic finite element method. The method is illustrated using three carefully selected numerical examples, namely, bending of a stochastic beam, flow through porous media with stochastic permeability and transverse bending of a plate with stochastic properties. The results obtained from the proposed method are compared with classical polynomial chaos and direct Monte Carlo simulation results.     

Filter approach to the stochastic analysis of MDOF wind-excited structures

In this paper, an approach useful for stochastic analysis of the Gaussian and non-Gaussian behavior of the response of multi-degree-of-freedom (MDOF) wind-excited structures is presented. This approach is based on a particular model of the multivariate stochastic wind field based upon a particular diagonalization of the power spectral density (PSD) matrix of the fluctuating part of wind velocity. This diagonalization is performed in the space of eigenvectors and eigenvalues that are called here wind-eigenvalues and wind-eigenvectors, respectively. From the examination of these quantities it can be recognized that the wind-eigenvectors change slowly with frequency while the first wind-eigenvalue dominates all the others in the low-frequency range. It is shown that the wind field can be modeled in a satisfactory way by taking the first wind-eigenvector as constant and by retaining only the first eigenvalue in the calculations. The described model is then used for stochastic analysis in the time domain of MDOF wind-excited structures. This is accomplished by modeling each element of the diagonalized wind-PSD matrix as the velocity PSD function of a set of second-order digital filters with viscous damping driven by white noise of selected intensity. This approach makes it easy to obtain in closed form the statistical moments of every order of the structural response, taking full advantage of the Itô calculus. Moreover, in the proposed approach, it is possible to reduce the computational effort by appropriately selecting the number of wind modes retained in the calculation.