A linearization scheme for vibrations due to combined deterministic and stochastic loads

Nonlinear vibrations due to combined deterministic and stochastic loads are investigated through a novel linearization scheme. The steady-state motion is expressed as a sum of an ensemble mean (deterministic) part and a zero-mean stochastic part. Further, harmonic averaging is used to account for the mean response, while statistical linearization is used to determine the standard deviation of the random part. This solution scheme involves coupling of the two procedures since the elements of the linear system depend both on the standard deviation of the response, and on the amplitude of its periodic mean. Good agreement is observed, for several sets of parameter values, between the results derived by the proposed scheme and a suite of relevant Monte Carlo studies.     

Critical excitation for elastic–plastic structures via statistical equivalent linearization

Since earthquake ground motions and their effects on structural responses are very uncertain even with the present knowledge, it is desirable to develop a robust structural design method taking into account these uncertainties. Critical excitation approaches are promising and a new random critical excitation method for single-degree-of-freedom (SDOF) elastic–plastic structures is proposed. The power (area of power spectral density (PSD) function) and the intensity (magnitude of PSD function) are fixed and the critical excitation is found under these restrictions. In contrast to linear elastic structures, transfer functions and related simple expressions for response evaluation cannot be defined in elastic–plastic structures and difficulties arise in describing the peak responses except elastic–plastic time-history response analysis. Statistical equivalent linearization is utilized to estimate the elastic–plastic stochastic peak responses approximately. The critical excitations are obtained for two examples and compared with the corresponding recorded earthquake ground motions.     

On the determination of the power spectrum of randomly excited oscillators via stochastic averaging: An alternative perspective

An approximate formula which utilizes the concept of conditional power spectral density (PSD) has been employed by several investigators to determine the response PSD of stochastically excited nonlinear systems in numerous applications. However, its derivation has been treated to date in a rather heuristic, even “unnatural” manner, and its mathematical legitimacy has been based on loosely supported arguments. In this paper, a perspective on the veracity of the formula is provided by utilizing spectral representations both for the excitation and for the response processes of the nonlinear system; this is done in conjunction with a stochastic averaging treatment of the problem. Then, the orthogonality properties of the monochromatic functions which are involved in the representations are utilized. Further, not only stationarity but ergodicity of the system response are invoked. In this context, the nonlinear response PSD is construed as a sum of the PSDs which correspond to equivalent response amplitude dependent linear systems. Next, relying on classical excitation-response PSD relationships for these linear systems leads, readily, to the derivation of the formula for the determination of the PSD of the nonlinear system. Related numerical results are also included.     

New method for efficient Monte Carlo–Neumann solution of linear stochastic systems

The Neumann series is a well-known technique to aid the solution of uncertainty propagation problems. However, convergence of the Neumann series can be very slow, often turning its use highly inefficient. In this article, a λ convergence parameter is introduced, which yields accurate and efficient Monte Carlo–Neumann solutions of linear stochastic systems using first order Neumann expansions. The λ convergence parameter is found as solution to a distance minimization problem, for an approximation of the inverse of the system matrix using the Neumann series. The method presented herein is called Monte Carlo–Neumann with λ convergence, or simply MC–N λ method. The accuracy and efficiency of the MC–N λ method is demonstrated in application to stochastic beam bending problems.     

Stochastic sensitivity analysis of energy-dissipating structures with nonlinear viscous dampers by efficient equivalent linearization technique based on explicit time-domain method

Nonlinear fluid viscous dampers have been widely used in energy-dissipating structures due to their stable and high dissipation capacity and low maintenance cost. However, the literature on stochastic optimization of nonlinear viscous dampers under nonstationary excitations is limited. This paper is devoted to the stochastic response and sensitivity analysis of large-scale energy-dissipating structures equipped with nonlinear viscous dampers subjected to nonstationary seismic excitations. The analysis procedure is developed in the frame of the equivalent linearization method (ELM) in conjunction with the explicit time-domain method (ETDM). The equivalent linear system and the corresponding statistical moments of responses at a specific time instant are first obtained through a series of stochastic response analyses of the linearized systems. Then the sensitivities of the statistical moments of responses are determined via a series of stochastic sensitivity analyses of the equivalent linear system at the corresponding time instant. The above two iterative procedures are facilitated at high efficiency using ETDM with explicit formulations of the statistical moments of responses and the sensitivities of the statistical moments. This process is repeated for different time instants, and the time histories of the statistical moments and their sensitivities can be obtained. The stochastic response and sensitivity results are further utilized to conduct the stochastic optimal parametric design of the nonlinear viscous dampers. A one-storey building model equipped with a nonlinear viscous damper is analyzed to demonstrate the accuracy of the proposed method, and a suspension bridge with a main span of 1200 m equipped with 4 nonlinear viscous dampers is further investigated to illustrate the feasibility of the proposed method for stochastic optimal design of large-scale structures.     

Equivalent linearization for systems driven by Lévy white noise

The equivalent linearization method is extended to the case of nonlinear systems driven by Lévy white noise. The classical objective function used for the determination of the equivalent linear system cannot be applied because the Lévy process generating the Lévy white noise has no variance and higher order moments. An alternative objective function based on the characteristic function of the state of the linear system is used for solution. Two simple examples are presented to illustrate the proposed extension of the equivalent linearization method. The examples show that the proposed equivalent linearization method provides approximations of quality similar to the classical equivalent linearization method.     

A two-step method for analysis of linear systems with uncertain parameters driven by Gaussian noise

A two-step method is proposed to find state properties for linear dynamic systems driven by Gaussian noise with uncertain parameters modeled as a random vector with known probability distribution. First, equations of linear random vibration are used to find the probability law of the state of a system with uncertain parameters conditional on this vector. Second, stochastic reduced order models (SROMs) are employed to calculate properties of the unconditional system state. Bayesian methods are applied to extend the proposed approach to the case when the probability law of the random vector is not available. Various examples are provided to demonstrate the usefulness of the method, including the random vibration response of a spacecraft with uncertain damping model.     

基于统计线性化的非线性随机振动虚拟激励法研究及应用

传统的虚拟激励法成功地解决了结构线性随机地震响应计算效率低的问题,却无法将非线性因素考虑在内。以统计线性化理论为基础,提出了一种可以考虑结构非线性的改进的绝对位移直接求解的虚拟激励法,采用不同的抗震分析方法,对一座单塔双索面预应力混凝土斜拉桥进行位移、速度、加速度功率谱密度以及位移、轴力响应值的计算,并进行对比分析,结果表明所提出改进的虚拟激励法不仅可以考虑结构的非线性,而且精确、高效,可以广泛地应用于桥梁工程的抗震分析中。     

An efficient approach for statistical moments estimation of structural response based on a novel adaptive hybrid dimension-reduction method

Statistical moments estimation is one of the main topics for the analysis of a stochastic system, but the balance among the accuracy, efficiency, and versatility for different methods of statistical moments estimation still remains a challenge. In this paper, a novel point estimate method (PEM) based on a new adaptive hybrid dimension-reduction method (AH-DRM) is proposed. Firstly, the adaptive cut-high-dimensional model representation (cut-HDMR) is briefly reviewed, and a novel AH-DRM is developed, where the high-order component functions of the adaptive cut-HDMR are further approximated by multiplicative forms of the low-order component functions. Secondly, a new point estimation method (PEM) based on the AH-DRM is proposed for statistical moments estimation. Finally, several examples are investigated to demonstrate the performance of the proposed PEM. The results show the proposed PEM has fairly high accuracy and good versatility for statistical moments estimation.     

A stochastic model for localized disturbances and its applications

A stochastic model for local disturbances, particularly for a temporal harmonic with random modulations in amplitude and/or phase, is proposed in this paper. Results for the second moment responses of a linear single-degree-of-freedom system to this type of stochastic loading demonstrate a significant change in response characteristics due to a small uncertainty. A local phenomenon may last much longer and resonance may be smeared to a broad range. Integrated with wavelet transform, the proposed approach may be used to model a random process with non-stationary frequency content. Especially, it can be effectively used for Monte Carlo simulation to generate large size of samples that have similar characteristics in time and frequency domains as a pre-selected mother sample has. The technique has a great potential for the case where uncertainty study is warranted but the available samples are limited.     

Recursive approach for random response analysis using non-orthogonal polynomial expansion

Using non-orthogonal polynomial expansions, a recursive approach is proposed for the random response analysis of structures under static loads involving random properties of materials, external loads, and structural geometries. In the present formulation, non-orthogonal polynomial expansions are utilized to express the unknown responses of random structural systems. Combining the high-order perturbation techniques and finite element method, a series of deterministic recursive equations is set up. The solutions of the recursive equations can be explicitly expressed through the adoption of special mathematical operators. Furthermore, the Galerkin method is utilized to modify the obtained coefficients for enhancing the convergence rate of computational outputs. In the post-processing of results, the first- and second-order statistical moments can be quickly obtained using the relationship matrix between the orthogonal and the non-orthogonal polynomials. Two linear static problems and a geometrical nonlinear problem are investigated as numerical examples in order to illustrate the performance of the proposed method. Computational results show that the proposed method speeds up the convergence rate and has the same accuracy as the spectral finite element method at a much lower computational cost, also, a comparison with the stochastic reduced basis method shows that the new method is effective for dealing with complex random problems.     

A response spectrum method for peak floor acceleration demands in earthquake excited structures

This paper addresses the prediction of the median peak floor acceleration (PFA) demand of elastic structures subjected to seismic excitation by means of an adapted response spectrum method. Modal combination is based on a complete quadratic combination (CQC) rule. In contrast to previous studies, in the present contribution closed form solutions for the correlation coefficients and peak factors entering the CQC rule are derived using concepts of normal stationary random vibration theory. A ground motion set, which matches the design response spectrum for a specific site and a target dispersion, is used to define the stochastic base excitation. The response spectrum method is tested for various planar and spatial generic high-rise structures subjected to this particular ground motion set. A comparison of the outcomes with the results of computationally more expensive response history analyses shows the applicability and accuracy of the proposed simplified method.     

Methodology for assessment of statistical planning effects on the S‐N curve determination using Monte Carlo simulations

One of the most important input data in fatigue analysis is the material fatigue properties. This research aims to present a methodology for assessment of statistical planning of fatigue experiments through a MatLab algorithm developed based on Monte Carlo simulations, which enables to simulate statistically the effects of main parameters used for defining the fatigue test setting and to verify their impact on the relative percentage difference (RPD) in fatigue properties estimation comparing to a reference material. The aspects treated here have not been clearly discussed in the standards. Therefore, the proposed recommendations combined with standards procedures is a tool for test engineers, permitting a fatigue test planning with more background and precision, which can help in decisions about which is the better setup, including the sample size, number of stress levels, stress value in each level, and replication. The methodology and good practices presented in this paper were demonstrated by means of actual data from the literature.     

Using computer techniques for vibration damage estimation under stochastic loading using the Monte Carlo Method for aerospace applications

The main focus of this article is a review of legacy methods for vibration damage estimation under stochastic loading and extending research made by Dirlik and Bendat using two combined methods: FEM and Monte Carlo simulation, for which we used Python programming for aerospace applications. For some aircraft, regulated by the RTCA international aviation standard DO-160G (Environmental Conditions and Test Procedures for Airborne Equipment), stochastic loading is defined as one of the requirements. This article will focus on the stochastic loading impact on the fatigue life assessment made on a dummy sample, and frequency and time domain damage estimation shall be considered in parallel to compare both results. Additionally, dummy PSD responses shall be defined in the frequency domain for signal statistical parameters research. The article introduces Rainflow Cycle Counting methods in the frequency domain for procedures used commercially in aerospace applications. The first method introduced and developed further is the Dirlik method of Rainflow Cycle Counting in the frequency domain, which is the most popular method in commercial use. The second technique introduced and developed further was established by Bendat — the Narrow Band Method. The new empirical equation presented in this paper is the modification of the Narrow Band Method fitted for general use (narrow band, wide band, and white noise signals). A new approach for the integration of spectral moments is introduced in this paper, allowing for an accurate evaluation of the signal statistic parameters in the frequency domain for use in the modified Dirlik and Narrow Band methods. Research results also revealed new phenomena not researched by Dirlik, such as high vibration damage variation from stochastic loading, which depends on the frequency resolution (the block size used in Inverse Fourier Transformation). This discovery will be the subject of further study. Research results presented in this paper will also be utilised to combine stochastic and deterministic loading scenarios for military helicopters, as well as fighter aircraft, and will be the subject of further research.     

Analysis of multi-degree of freedom strongly non-linear mechanical systems with random input: Part II: equivalent linear system with random matrices and power spectral density matrix

We present a method for estimating the (power spectral density) PSD matrix of the stationary response of lightly damped randomly excited multi-degree of fredom mechanical systems with strong non-linear asymmetrical restoring forces. The PSD matrix is defined as the mean value of the PSD matrix response of an equivalent linear system (ELS) whose damping and stiffness matrices depend on non-linear vibration modes of the associated conservative system, the frequencies and modes shapes being amplitude dependent. Based on a generalized van der Pol transformation and using a stochastic averaging principle, as developed in a companion paper, a stationary probability density function for the amplitude process is derived to characterize the ELS fully. Some possible simplifications of the method, such as modal reduction and/or local linearization, are also discussed. The results obtained are in good agreement with those of direct numerical simulations taking two typical examples.