地埋管地源热泵中长期数值模拟的土壤内热源法

在分析常规地埋管地源热泵中长期数值模拟方法——管群换热模拟法的基础上,提出了新的模拟方法——土壤内热源法.模拟结果表明,在内热源法网格数仅为管群换热模拟法网格数1/8的情况下,两者在中长期运行工况下的土壤温度场变化及埋管区域年温升变化基本相同,采用内热源法可以大大简化计算过程,缩短计算时间.     

有限元数值模拟法基本原理及其在地质构造变形研究中的应用综述

在构造变形研究中,传统的构造解析法存在一定局限性,而数值模拟方法可模拟时空变化,成为地球科学研究的重要手段。有限元法是一种模拟线性、变形问题较为高效的数值模拟法。本文详细阐述了有限元法的基本原理,并从地质模型、力学模型、计算模型三个方面解释了数值模型的建立过程,以及模型建立所需要的边界条件的确定方法,重点总结了近些年来有限元法在国内外构造变形研究中的应用和发展状况,涉及盆地、造山带、断层、俯冲带以及褶皱数值模拟等几方面内容。同时,分析了有限元数值模拟法在我国大陆构造变形中的应用进展。最后,讨论了有限元方法尚存在的问题及其发展趋势。     

高层建筑地震作用计算的时域显式随机模拟法

目前我国GB 50011—2010《建筑抗震设计规范》中所推荐的振型分解反应谱法和时程分析法尚无法完全反映地震动随机性对建筑结构的影响,采用真正意义的随机振动方法计算复杂高层建筑地震响应的需求越来越迫切。结合新修订的广东省标准DBJ 15-92—2013《高层建筑混凝土结构技术规程》,全面介绍了结构地震作用计算时域显式随机模拟法的计算原理和实现方法,内容包括:1)通过迭代计算直接获得与现行规范反应谱完全等效的地震动加速度功率谱;2)给出基于精细积分和Newmark-β积分格式的结构地震响应显式表达式的构建方法;3)结合地震动随机过程的数值模拟,统计分析得到结构平均峰值响应。以广州西塔等4栋超高层和高层建筑为工程应用实例,显示了时域显式随机模拟法在高层结构抗震分析中的高效性和实用性。计算结果表明,时域显式随机模拟法和传统振型分解反应谱法的计算结果存在一定差异,最大差异可超过25%,反应谱法的内力计算结果普遍偏小。     

蒙特卡罗法在混凝土数值模拟中的应用

概述了混凝土结构层次观点,介绍了混凝土数值模拟方法及蒙特卡罗方法,重点讨论了蒙特卡罗法在混凝土细观层次上数值模拟的应用,分析了随机数产生的原理,详述了蒙特卡罗法建立随机骨料模型的过程,总结了蒙特卡罗法应用于模拟随机骨料结构的有效性。     

土与软岩中开挖隧道的衬砌受力分析

大部分的城市隧道修建于软土或软弱岩层中。本文对比分析了基于有限元的数值模拟方法和两种解析法得出的隧道衬砌所产生的弯矩和轴力,解析法包括Curtis和Muir-Wood法。结果表明数值模拟法和解析法的计算结果相吻合,同时评估了不同参数对结果的影响。     

基于FLAC数值模拟分析某公路边坡稳定性

FLAC数值模拟法是一种基于有限差分法的针对岩土边坡稳定性分析的一种数值模拟方法。为了检验该法在土质边坡稳定性分析上的效果,本文结合同三公路(哈尔滨至佳木斯段)路堑边坡,采用FLAC数值模拟法对边坡稳定性进行分析,并将分析结果与简化毕肖普条分法分析结果进行比较验证;同时针对边坡土体的不同含水量进行分析,说明含水量的变化对边坡稳定性的影响。     

成都地铁驷马桥站深基坑变形特征研究

结合成都地铁3号线驷马桥站工程实例,采用数值模拟法研究了基坑开挖过程对地表沉降及周边土体水平位移的影响,并将现场实测数据与数值模拟数据作了对比,指出两者相互验证,为基坑施工提供了依据。     

深埋隧洞涌水量预测数值模型研究进展

深埋隧洞具有高地温、高地应力和高外水压力等特点,随着隧洞施工的进行,围岩中原有的渗流条件被破坏,会产生涌、突水危害。涌水量的预测方法主要有水均衡法、解析法、数值模拟法等。常用的数值模拟方法主要包括:有限分析法、有限单元法、有限体积法、有限差分法和边界元法等。数值模拟法能更好地刻画复杂构造和边界条件,但存在选取模型和建模刻画不合理导致失真的缺点。为了系统地总结和分析各模型在工程应用中的优缺点,本文总结了目前常用的隧洞涌水量预测数值模型,对比分析了各模型的结构特征和使用环境,提出合理的建议,为今后深埋隧洞涌水量预测模型的建立和发展提供参考。     

水中直流电阻率法对河道淤积层探测的可行性分析

基于有限差分法,建立了水中直流电阻率法对河道淤积层探测的正演模型,并模拟了两种典型的测量方式,分析了测量方式对视电阻率值与淤积层探测结果的影响,通过数值模拟法与室内水槽试验法视电阻率反演结果的对比,研究了水中直流电阻率法对河道淤积层探测的可行性。     

随机振动荷载作用下结构动力可靠性设计方法研究

以轨道结构为例建立了车辆-轨道垂向耦合动力计算模型,并使用Wilson-q数值积分方法得到了轨道结构在列车荷载作用下的随机振动响应。基于随机过程跨越理论,得到了不同跨越假设以及极值分布条件下钢轨动力响应可靠度,并与数值模拟法结果进行比较,表明对于低跨越界限,极值I型分布法的计算结果最接近精确值;对于高界限则高斯分布法效果最好。     

An efficient stochastic dynamic analysis of soil media using radial basis function artificial neural network

Since a lot of engineering problems are along with uncertain parameters, stochastic methods are of great importance for incorporating random nature of a system property or random nature of a system input. In this study, the stochastic dynamic analysis of soil mass is performed by finite element method in the frequency domain. Two methods are used for stochastic analysis of soil media which are spectral decomposition and Monte Carlo methods. Shear modulus of soil is considered as a random field and the seismic excitation is also imposed as a random process. In this research, artificial neural network is proposed and added to Monte Carlo method for sake of reducing computational effort of the random analysis. Then, the effects of the proposed artificial neural network are illustrated on decreasing computational time of Monte Carlo simulations in comparison with standard Monte Carlo and spectral decomposition methods. Numerical verifications are provided to indicate capabilities, accuracy and efficiency of the proposed strategy compared to the other techniques.     

Application of spherical subset simulation method and auxiliary domain method on a benchmark reliability study

This paper addresses a benchmark study designed to evaluate the performance of various methods in calculating the reliability of large systems. In particular, this paper focuses on evaluating two reliability methods recently proposed by the authors, referred to as spherical subset simulation (S³) and auxiliary domain method (ADM). S³ is based on dividing the failure domain into a number of appropriately selected subregions and calculating the failure probability as a sum of the probabilities associated with each of these subregions. The probability of each subregion is calculated as a product of factors. These factors can be estimated accurately by a relatively small number of samples generated according to the conditional distribution corresponding to the particular subregion. The generation of such samples is achieved through Markov Chain Monte Carlo (MCMC) simulations using a MCMC algorithm proposed by the authors. The proposed method is very robust and is suitable for treating general high-dimensional problems such as the given benchmark problems. ADM is applicable to reliability problems involving deterministic dynamic systems subjected to stochastic excitation. The first step in ADM involves the determination of an auxiliary failure domain (AFD). The choice of the AFD is based on preliminary MCMC simulations in the target failure domain. It must be noted that although the AFD is chosen to be specified as a union of linear failure domains, the method does not assume any restriction with respect to the target failure domain, which is assumed to be generally non-linear. Once the AFD is determined, the ADM proceeds with a modified subset simulation procedure where the first step involves the direct simulation of points in the AFD. This is in contrast to standard subset simulation (SSM) where the first step involves standard Monte Carlo Simulations. The number of steps and the computational effort required by ADM, assuming an appropriate AFD is chosen, can be smaller than that required by SSM. Results for the benchmark problems show that both S³ and ADM are efficient for treating high dimensional reliability problems.     

Stability analysis of wind-induced torsional motion of slender bridges

The paper presents a numerical, simulation-based approach to investigate the stability of torsional motion of slender suspension bridges under stochastic wind turbulence. The torsional bridge motion is represented by a linear, single degree of freedom oscillator. Stochastic turbulence in wind velocity is considered in the form of a periodic excitation with random phase modulation. The stability condition refers to the asymptotic sample stability, for which the necessary and sufficient condition is that the largest Lyapunov exponent be negative. Monte Carlo simulation is performed to evaluate the largest Lyapunov exponent, and stochastic differential equations of motion are integrated in polar coordinates using Euler's scheme. Unlike earlier analytical approximations, a quadratic noise term is retained in the present analysis. The turbulence intensity is shown to have a small stabilizing effect on the bridge stability in a sense that an increase in the turbulence intensity moderately increases the critical mean wind velocity beyond the deterministic flutter velocity. The stabilization effect is limited to the case of narrowband detuned excitation. In the proximity of the parametric resonance frequency, an increase in the bandwidth of the excitation process tends to stabilize the bridge motion.     

Computational stochastic structural analysis (COSSAN) – a software tool

The paper provides an overview of the current status of the COSSAN™ [Institute of Engineering Mechanics, Leopold-Franzens University, Innsbruck, Austria, EU. COSSAN. Computational stochastic structural analysis, User’s Manual, Part B, 2001; Part A, 1996] software developed at the Institute of Engineering Mechanics of the Leopold Franzens University, Innsbruck, Austria, EU. Two options provided by COSSAN™ are described: (1) A ‘Stand Alone Tool Box’ which is an event driven modular general purpose code and (2) the ‘Third Party Communication Tools’ which allow to employ deterministic Third Party (FE-) codes for stochastic analysis without the need to access and modify the third party source code. The Stand Alone Tool Box covers a fairly wide field of stochastic methods including various sampling techniques, random fields, fatigue analysis, reliability based optimization, random vibration, Monte Carlo simulation and FE-analysis. The Third Party Communication Tools are designed to extend existing deterministic FE-codes for considering uncertainties and consequently perform stochastic analyses using methods based on Monte Carlo procedures.     

Modeling of nonstationary ground motion and analysis of inelastic structural response

A new stochastic model of ground excitation is proposed in which both intensity and frequency content are functions of time, and corresponding methods for estimating the model parameters based on actual earthquake records are also given. The proposed ground motion model can be efficiently applied in simulations as well as random vibration vibration and reliability studies of inelastic structures. Responses of single-mass inelastic systems and three-story space frames, with or without deterioration under the nonstationary biaxial ground excitation are investigated via the equivalent linearization method and Monte Carlo simulations. The results show that the time-varying frequency content the dominant frequencies of ground excitation are close to the structural natural frequency. Also biaxial and torsional response may become significant in an unsymmetric structure.     

Comparison of methods of including stochastic factors into deterministic models of indoor air quality

The paper discusses problems connected with the inclusion of stochastic factors in deterministic models of indoor air quality (IAQ). Three different methods are shortly presented: quasi-dynamic multizone modelling with generation of input data time series; multizone modelling based on the theory of stochastic differential equations: and Monte Carlo simulation with independent random generation of stochastic parameters. The described methods are compared using a computer simulation of carbon dioxide concentration in a simple two-compartment office. The comparison of simulation results shows that the way in which stochastic disturbances are included in the models does not have an important influence on mean value of predicted carbon dioxide concentration. At the same time, the analysis of standard deviations indicates that the method of disturbance generation and its later incorporation into the IAQ models have a great influence on the probability distribution of estimated concentrations. Finally, there is a discussion on the main advantages and disadvantages of each of the proposed methods.     

基于复数表达的随机渗流研究

以复数形式表示渗透系数,将其变异值设置为复数的虚部.利用有限单元法,通过求解复系数线性方程组计算随机渗流问题,并编制相应程序,计算结点水头和水头变异值.在应用蒙特卡罗法进行渗流计算时,仅考虑帷幕渗透系数的变异,并假设其服从均匀分布.选取水头均值与水头标准差为蒙特卡罗法渗流计算的统计特征值.从数值方面分析了基于复数表达的随机渗流计算所得的水头值及水头变异值与蒙特卡罗法计算的水头均值及标准差之间的关系,验证了所用方法在模拟随机渗流场方面的正确性和可行性.为大型复杂问题的大变异性求解提供了方便快捷的计算方法.     

Stochastic collocation-based nonlinear analysis of concrete bridges with uncertain parameters

This paper focuses on the stochastic response of concrete bridges considering uncertainty in bearing and abutment stiffness. A multi-span simply supported bridge with concrete girders is selected. A 3D-dimensional model is prepared, and nonlinear response history analyses are performed. For the numerical dynamic simulation, the non-sampling stochastic method based on generalized polynomial chaos (gPC) expansion is utilised. The uncertain parameters include the vertical and shear stiffness of bearings and the lateral stiffness of abutments are presented by the truncated gPC expansions. Furthermore, the system response such as base shear, acceleration, velocity and displacement in different columns is presented by gPC expansion with unknown deterministic coefficients. The stochastic Galerkin projection is employed to calculate a set of deterministic equations. A non-intrusive solution, as a set of collocation points, determines the unknown gPC coefficients of the system response and the results are compared with Monte Carlo simulations. The key advantage of spectral discretization is the combination of the mentioned method with the spatial discretization, e.g. finite element model. This study also emphasises the accuracy in results and time efficiency of the proposed non-sampling method for uncertainty quantification of stochastic systems comparing to sampling procedure (e.g. Monte Carlo simulation).     

Time-dependent system reliability analysis by adaptive importance sampling

A method for evaluating time-dependent reliability of a structural system subjected to stochastic loads is presented. Structural deterioration due to environmental stressors is also taken into account. An adaptive Monte Carlo simulation procedure combined with conditional expectation is proposed. The optimum common ratio of the standard deviation of an importance sampling variables to that of the corresponding original valuables is estimated iteratively as well as their mean values. Unlike systems evaluated by simple Monte Carlo simulations, the accuracy of the failure probability evaluated by adaptive importance sampling is relatively insensitive to the magnitude of the probability.     

Uncertain linear systems in dynamics: Retrospective and recent developments by stochastic approaches

This paper provides an overview along with a critical appraisal of available methods for uncertainty propagation of linear systems subjected to dynamic loading. All uncertain structural properties are treated as random quantities by employing a stochastic approach. The loading can be either of deterministic or stochastic nature, described by white noise, filtered white noise, and more generally, by a Gaussian stochastic process.The assessment of the variability of the uncertain response in terms of the mean and variance is described by reviewing the random eigenvalue problem and procedures to evaluate the first two moments of the stochastic (uncertain) response. Computational procedures which are efficiently applicable for general FE-models are the focus of this work.Most recent developments for the reliability assessment–besides a retrospective review–are summarized, with particular emphasis on numerical Monte Carlo Simulation approaches. This review comprises methods to assess the first excursion probability directly by efficient numerical methods. General “black box” procedures and approaches applicable only for linear systems are critically discussed. Specific procedures applicable to linear systems subjected to general Gaussian excitation are subsequently addressed. Methods applicable for deterministic structural systems are introduced first. Finally, a procedure to exploit the solutions for deterministic linear systems for stochastic uncertain systems in an efficient manner is described.