Uncertainty analysis for rolling contact fatigue failure probability of silicon nitride ball bearings

Uncertainty analysis and parametric studies are presented for estimating the fatigue failure probability of surface cracks in silicon nitride ball bearings subjected to rolling contact fatigue. Uncertainty quantification of input parameters are presented first based on experimental data, inspection capability, and geometric reasoning. Surrogate models for equivalent stress intensity factors are then used for uncertainty propagation, which are built upon high fidelity finite element modeling with half-penny-shaped surface cracks. Instead of black-box type surrogate modeling, physical observations are employed to decompose the high dimensional surrogate model into multiple one-dimensional models. The cross-validation technique is used to find the best surrogate that has the smallest prediction variance. The probability of failure is estimated using Monte Carlo simulation and surrogate models. The parametric studies show that reducing the maximum crack size (by limiting inspection threshold) and increasing the fatigue threshold (by improving fracture toughness of a material) are the most effective ways of reducing the probability of failure. For example, decreasing the maximum crack size by 4.4% and increasing the lowest fracture threshold by 2.8% results in the reduction of probability of failure by 40%. Ball survivability increases with decreasing ball diameter, for a given peak Hertzian stress. In order to apply the current study to hybrid ball bearing design, the survivability results are generalized through non-dimensionalization.     

Stochastic response of uncertain systems

Summary A method for the analysis of structural systems with random properties subjected to dynamic loading is presented. In this context, the weighted integral technique which provides an accurate and consistent transition from continuous type random fields to discrete type stochastic finite elements is extended to the area of stochastic structural dynamics. Both, mass and stiffness matrices are expanded in terms of weighted integrals. Monte Carlo simulation and the response surface method are applied to determine threshold exceedance probabilities of the response under randomly fluctuating dynamic loading. The capabilities of the concept with respect to the assessment of failure probabilities are investigated.

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Stochastische Antwort von Systemen mit streuenden Eigenschaften

Übersicht Es wird eine Methode zur Berechnung von Strukturen mit streuenden Eigenschaften unter dynamicscher Belastung vorgestellt. Dazu wird die Methode der gewichteten Integrale, die eine exakte, konsistente Dartellung kontinuierlicher Zufallsfelder in diskreten stochastischen finiten Elementen ermöglicht, so erweitert, daß sie auf das Gebiet der stochastischen Strukturdynamik anwendbar wird. Sowohl Massen- als auch Steifigkeitsmatrix werden durch gewichtete Integrale beschrieben. Die Bestimmung der Überschreitenswahrscheinlichkeit der Systemantwort erfolgt durch Anwendung von Monte Carlo Simulation und Antwortflächenverfahren. Die Möglichkeiten dieses Konzeptes zur Beurtcilung der Versagenswahrscheinlichkeit werden untersucht.

     

一种基于凸集-概率混合模型的结构可靠性分析法

本文建立一种凸集-概率混合模型下的结构可靠性分析方法。考虑椭球模型和区间模型两种不同类型的凸集模型,在标准空间内通过拉丁超立方抽样生成样本点,通过矩阵变换将其转换到凸集空间内,得到凸集变量的样本点;将凸集变量样本点代入极限状态方程,从而混合可靠性模型转变为纯概率可靠性模型;利用Laplace渐进积分法对每个极限状态方程进行失效概率求解,统计结果的最大值和最小值,得到失效概率的上、下界,研究了凸集变量的个数对结果的影响,通过失效概率的变异系数描述计算结果的稳定性;通过三个算例验证了计算结果的精度,并采用混合模型的蒙特卡洛法进行对比计算。研究表明：本文所提方法计算精度和效率均较高;凸集模型的类型会对结果产生较大影响;为使结果趋于稳定,椭球模型所需的凸集样本点个数多于区间模型;若凸集样本点数目相同,椭球模型的失效概率计算结果变异系数较小,稳定性高于区间模型。     

一种基于凸集-概率混合模型的结构可靠性分析法

本文建立一种凸集-概率混合模型下的结构可靠性分析方法。考虑椭球模型和区间模型两种不同类型的凸集模型,在标准空间内通过拉丁超立方抽样生成样本点,通过矩阵变换将其转换到凸集空间内,得到凸集变量的样本点;将凸集变量样本点代入极限状态方程,从而混合可靠性模型转变为纯概率可靠性模型;利用Laplace渐进积分法对每个极限状态方程进行失效概率求解,统计结果的最大值和最小值,得到失效概率的上、下界,研究了凸集变量的个数对结果的影响,通过失效概率的变异系数描述计算结果的稳定性;通过三个算例验证了计算结果的精度,并采用混合模型的蒙特卡洛法进行对比计算。研究表明：本文所提方法计算精度和效率均较高;凸集模型的类型会对结果产生较大影响;为使结果趋于稳定,椭球模型所需的凸集样本点个数多于区间模型;若凸集样本点数目相同,椭球模型的失效概率计算结果变异系数较小,稳定性高于区间模型。     

基于主动学习神经网络的转向架构架疲劳可靠性分析

为了提高转向架构架疲劳可靠性分析的精度与效率,提出一种主动学习BR-BP神经网络模型与Monte Carlo法相结合的可靠性分析方法。该方法针对BP神经网络的缺陷,使用贝叶斯正则BR(Bayesian regularization)算法作为训练算法,以提高神经网络的拟合精度与收敛速度,并考虑可靠性分析的固有特点,构造了一种适用于BP神经网络的主动学习函数,用于指导最佳样本点的选择。提出的学习函数不仅保证了样本点分布在极限状态函数附近,还考虑了样本点的预测误差以及样本点分布对失效概率计算的影响。转向架构架可靠性分析结果表明,本文方法在提高拟合精度的同时兼顾了计算效率,验证了所提方法的优越性与可行性。     

一种分析区间参数结构可靠性的减基快速计算方法

将减基法与蒙特卡洛模拟结合,提出了一种快速计算区间不确定结构可靠性的方法。该方法分为离线和在线计算两个阶段,离线阶段利用减基法建立减基空间,进而形成减缩模型;而在线阶段则将减缩模型融入蒙特卡洛方法,进而快速求解区间失效概率及相应的区间参数失效域。该方法在减基空间而非有限元空间中分析区间结构的可靠性,减少了求解时间,提高了计算效率,通过算例验证了该方法的可行性和有效性。     

A methodology to achieve the set of operation modes of reconfigurable parallel manipulators

The demand for increasingly more versatile machinery has boosted the development of the so-called reconfigurable mechanisms. In this paper, the authors present a general methodology to assess the multioperational capacity of a 6-DOF parallel manipulator basing on the possible motion patterns having Lie group structure that the manipulator owns. This ability of having different operation modes enables the manipulator to adapt to diverse tasks. To show the potential of the methodology, this approach has been applied to the 6-DOF 3-CPCR which is capable of generating multiple motion patterns. In addition to carrying out the complete theoretical study in which all the different operation modes are obtained, and validating the procedure with GIM software designed for kinematic analysis and design of mechanisms, a demonstration prototype of the 3-CPCR parallel manipulator has been also built.

     

Modeling wall effects in a micro-scale shock tube using hybrid MD–DSMC algorithm

Wall effects in a micro-scale shock tube are investigated using the Direct Simulation Monte Carlo method as well as a hybrid Molecular Dynamics–Direct Simulation Monte Carlo algorithm. In the Direct Simulation Monte Carlo simulations, the Cercignani–Lampis–Lord model of gas–surface interactions is employed to incorporate the wall effects, and it is shown that the shock attenuation is significantly affected by the choice of the values of tangential momentum accommodation coefficient. A loosely coupled Molecular Dynamics–Direct Simulation Monte Carlo approach is then employed to demonstrate incomplete accommodation in micro-scale shock tube flows. This approach uses fixed values of the accommodation coefficients in the gas–surface interaction model, with their values determined from a separate dynamically similar Molecular Dynamics simulation. Finally, a completely coupled Molecular Dynamics–Direct Simulation Monte Carlo algorithm is used, wherein the bulk of the flow is modeled using Direct Simulation Monte Carlo, while the interaction of gas molecules with the shock tube walls is modeled using Molecular Dynamics. The two regions are separate and coupled both ways using buffer zones and a bootstrap coupling algorithm that accounts for the mismatch of the number of molecules in both regions. It is shown that the hybrid method captures the effect of local properties that cannot be captured using a single value of accommodation coefficient for the entire domain.     

相关正态变量情况下基于自适应超球重要抽样的可靠性灵敏度分析的转换法

在将相关正态变量转换成独立正态变量的基础上,首先建立了基于Monte Carlo模拟的相关正态变量可靠性灵敏度分析的转换法,并对其可靠性灵敏度估计值作了方差分析.其次将Monte Carlo转换法与自适应超球重要抽样法相结合,建立了相关正态变量可靠性灵敏度分析的自适应超球重要抽样转换法.所建立方法利用抽样样本提供的信息,通过迭代逐步确定最优超球半径,极大地提高了算法的稳健性和效率.由于自适应超球重要抽样转换法融合了Monte Carlo法的普适稳健性和超球重要抽样的高效性,因此它对于高度非线性隐式极限状态方程、多个失效模式串、并及混联系统、多个最可能失效点问题均具有很强的适应性,算例结果充分证明了这些优点.     

Reliability-based design for soil tillage machines

Using classical design methods for tillage machines does not completely guarantee a safety and satisfactory performance, due in part to the randomness of tillage forces. This randomness is derived from the variability in soil engineering properties and the variations in tool design parameters and operational conditions. In this paper, a reliability-based design approach was developed, for the first time, by integrating the randomness of tillage forces into the design analysis of tillage machines, aiming at achieving reliable machines. The proposed approach was based on the uncertainty analysis of basic random variables and the failure probability of tillage machines. The failure probability was estimated according to two performance criteria related to the structural design requirement and the quality of tillage operation. Two reliability methods, namely the Monte Carlo simulation technique and the first-order reliability methods were used for this purpose. This approach was implemented for the design of a chisel plough shank.The results showed that there were many values of the shank dimensions that guaranteed the required reliability level. However, in order to achieve the best design solution from an economic point of view, minimizing the volume of the shank structure was integrated into the reliability-based design approach. This led to the reduction of the initial volume of the shank structure by 6.86%. It was concluded that integrating the economical constraint into the reliability-based design approach can lead to optimal designs of tillage tools that ensures the required reliability level at low cost.     

基于Chebyshev展开的区间穿孔板超材料分析

目前对于声学超材料的传输特性分析和优化大多是基于确定的数值和确定的模型,然而在实际工程和结构设计中存在大量材料自身特性和几何物理参数的不确定性.如果忽略这些不确定变量对声学超材料传输特性分析和优化过程的影响,得到的结果可能不正确.针对这一现状,拟将切比雪夫区间模型引入多层穿孔板超材料,提出多层穿孔板超材料声学透射率的区间切比雪夫展开-蒙特卡洛模拟法(interval Chebyshev expansionMonte Carlo simulation method,ICE-MCSM).该方法采用截断切比雪夫多项式近似拟合多层穿孔板超材料的声学透射率响应曲线,构造声学透射率响应曲线的切比雪夫代理模型;然后采用蒙特卡洛模拟法(Monte Carlo simulation method,MCSM)随机生成一定数量的不确定区间变量的样本数据点,并将生成的不确定区间变量样本数据点代入切比雪夫代理模型,预测单个不确定区间变量和多个不确定区间变量条件下的多层穿孔板超材料声学透射率区间的上界和下界.数值分析结果表明,ICE-MCSM预测的声学透射率变化区间的上界和下界与直接蒙特卡洛法(direct Monte Carlo simulation method,DMCSM)预测的声学透射率上界和下界的结果非常接近.与DMCSM相比,ICE-MCSM具有更高的计算效率.因此,ICE-MCSM可有效且高效地分析不确定区间变量条件下多层穿孔板超材料声学透射率传输特性,具有良好的工程应用前景.     

A domain decomposition method for stochastic analysis of acoustic fields with hybrid and localized uncertainties

An efficient domain decomposition method (DDM) is proposed for the dynamic analysis of stochastic acoustic fields with hybrid and localized uncertainties. The hybrid and localized uncertainties refer to the parameters that are associated with local properties of the acoustic fields and meanwhile are subjected to different kinds of randomness. To take advantage of the locally distributed feature of uncertain parameters, the full acoustic domain is divided into several sub-domains, along with each localized uncertain parameter being assigned to one specific sub-domain. In each sub-domain, the deterministic Helmholtz equation is transformed to a weak integral form and the discretized governing equation is obtained by employing Chebyshev orthogonal polynomials as admissible functions. The random or interval perturbation technique is applied to the individual governing equation according to the respective uncertainty type, whereby the stochastic governing equation is established. The original acoustic field is eventually recovered by the introduction of penalty functions to impose sound pressure continuity on the interfaces of sub-domains, and the (intervals of) sound pressure, together with its expectation and variance, can be subsequently obtained. The accuracy and efficiency of the proposed method are verified in several numerical examples by comparisons with the results given by brute force Monte Carlo simulations, and the DDM-based independent way of modelling and analysis proves to be quite effective and flexible for uncertainty quantification in acoustic fields.     

Efficient Uncertainty Quantification for Unconfined Flow in Heterogeneous Media with the Sparse Polynomial Chaos Expansion

In this study, we explore an efficient stochastic approach for uncertainty quantification of unconfined groundwater flow in heterogeneous media, where a sparse polynomial chaos expansion (PCE) surrogate model is constructed with the aid of the feature selection method. The feature selection method is introduced to construct a sparse PCE surrogate model with a reduced number of basis functions, which is accomplished by the least absolute shrinkage and selection operator-modified least angle regression and cross-validation. The training samples are enriched sequentially with the quasi-optimal samples until the results are satisfactory. In this study, we test the performance of the sparse PCE method for unconfined flow with the presence of random hydraulic conductivity and recharge, as well as pumping well. Numerical experiments reveal that, even with large spatial variability and high random dimensionality, the sparse PCE approach is able to accurately estimate the flow statistics with greatly reduced computational efforts compared to Monte Carlo simulations.

     

考虑非高斯特性的结构首次失效时间计算

本文发展了一个计算具有非高斯特性的结构首次失效时间的解析方法.该方法利用Hemite矩模型将非高斯结构反应映射为标准高斯过程,由此计算反应的平均超越率、成群超越以及初始状态的影响,并最终给出结构的首次失效时问概率.二次力函数激励下线性单自由度系统的首次失效时间分析说明了该方法的使用过程,同时对该方法的计算结果与Monte Carlo模拟结果进行了对比.     

Optimisation of temporal averaging processes in PIV

A hybrid of correlation and vector averaging is introduced to capitalise on the advantages of each process. An extensive series of Monte Carlo simulations have been conducted to investigate hybrid averaging and evaluate it against both vector and correlation averaging. The simulations show that hybrid averaging improves the measurement accuracy over both correlation and vector averaging over a wide range of imaging conditions. The simulations are validated by applying hybrid averaging to experimental micro- and macro-flows. In pulsatile conditions, correlation averaging yields an averaged correlation function that is multi-modal, which can result in unpredictable measurements. A Monte Carlo simulation shows the benefits of hybrid averaging over correlation averaging in such conditions. This has been experimentally validated on the unsteady wake behind a shedding circular cylinder at Re = 98.     

A univariate approximation at most probable point for higher-order reliability analysis

This paper presents a new univariate method employing the most probable point as the reference point for predicting failure probability of structural and mechanical systems subject to random loads, material properties, and geometry. The method involves novel decomposition at the most probable point that facilitates a univariate approximation of a general multivariate function, response surface generation of the univariate function, and Monte Carlo simulation. In addition to the effort of identifying the most probable point, the method requires a small number of exact or numerical evaluations of the performance function at selected input. Results of four numerical examples involving elementary mathematical functions and structural/solid-mechanics problems indicate that the proposed method provides accurate and computationally efficient estimates of probability of failure. Finally, the fatigue failure of lever arm in a wheel loader has been evaluated, demonstrating the ability of the new method in solving industrial-scale fatigue reliability problems.     

随机有限元分析的二级区域分解并行求解算法

采用蒙特卡罗模拟(MCS)和加权积分法对二维问题进行随机有限元分析。尽管MCS方法对任何有确定解的问题都具有求解精度高的优点,但由于求解所需的计算量巨大使其应用受到限制。利用并行求解技术可有效地处理这种密集型计算问题。基于有限元分裂对接法(FETI)的并行特性并利用预处理共轭梯度法(PCG)的求解高效性,结合整体子区域实现(GSI-PCG)和FETI法,提出二级求解算法,并在工作站机群上实现了数值算例。算例计算结果表明本文GSI(PCG)-FETI算法具有较高的并行加速比和并行效率,具有良好的性能,可有效地进行二维问题的随机有限元分析。     

The influence of errors in an interferogram analysis

In determining the influence of random errors in experimental measurement and data reduction, calculus may be applied to some problems where compounded error effects are encountered. However, for some processes it may be more convenient to apply a method of analysis described here which is based on computer simulation of errors, a variation of the so-called Monte Carlo technique. Interferometric moiré fringe patterns corresponding to in-plane displacement fields at 0, 45 and 90 deg, to the plane of the crack, taken from compact tension specimens, were analyzed to evaluate theJ-integral fracture parameter. Since theJ value for each load point was derived from over 100 separate measurements of fringe spacings, each with a significant probability of error, and it was desired to establish the magnitude of these errors on the final outcome, a Monte Carlo simulation of the analysis errors was performed. As a result of the simulation exercise, it could be concluded that the level of errors generated in the fringe-analysis procedure did not significantly affect the value ofJ obtained.     

结构系统可靠性优化设计的神经网络方法

针对具有非正态随机参数的可靠性(优化)设计,提出了随机摄动-Edgeworth级数方法,采用该方法将可靠性概率约束转化为等价的确定型约束,可以迅速准确地获得优化设计信息。针对具有多失效模式的结构系统可靠性优化设计,提出了随机模拟一神经网络方法(MCS—NN),将随机模拟方法与神经网络技术有机结合,为结构系统可靠性优化设计提供了一种新方法。     

岩石弹塑性破裂过程的数值模拟研究

分析了对于非均质、非连续且材料参数分布具有分散性和随机性的岩体介质,采用传统有限元法解决其工程问题的局限性．阐述了用岩石力学性质的分布特性来描述岩石材料不均匀性,并用蒙特卡洛模拟方法,将符合Weibull分布规律的材料参数赋予岩体结构中的各个单元体,进而用计算机进行数值模拟的研究方法．应用了在Windows95环境下利用FORTRAN Power Station4．0平台开发的具有可视性的“岩石弹塑性破裂过程分析”软件——REPFPA软件,对典型的岩石试样和巷道模型的弹塑性破裂过程进行数值模拟研究,得到了有价值的实验结果．证明把材料参数随机赋值方法引入到岩土类材料的弹塑性破裂过程分析中去,并用计算机进行数值模拟是研究岩石弹塑性破裂过程的一种有效方法。