层序地层学模拟研究进展及趋势

模拟层序地层学是层序地层学理论重要的研究手段之一,在国外该项技术较为成熟.层序地层计算机模拟软件采用的算法主要有扩散算法、流体流动算法、几何控制沉积算法、经验数值逼近算法、沉积物搬运的构造校正算法.目前,国外学者对以海平面升降、构造沉降和沉积物供应为主控因素的层序地层概念模拟的适用性提出了质疑,并对层序控制因素的多样性和不确定性进行了深入的研究.结合当前的研究进展,提出了层序控制因素的"多变量系统"观点,将层序的控制因素分为综合变量和独立变量,进一步指出如果假设综合变量为独立变量,则可以简化层序模拟,假设越多,模拟越简单,但模拟结果越粗略;要使模拟结果真实地反映实际地层特征,就要尽可能采用独立变量.     

Minimal cut-based recursive decomposition algorithm for seismic reliability evaluation of lifeline networks

In this paper, a new probabilistic analytical approach, the minimal cut-based recursive decomposition algorithm (MCRDA), is presented to evaluate the seismic reliability of large-scale lifeline systems. Based on the minimal cut searching algorithm, the approach calculates the disjoint minimal cuts one by one using the basic procedure of the recursive decomposition method. At the same time, the process obtains the disjoint minimal paths of the system. In order to improve the computation efficiency, probabilistic inequality is used to calculate a solution that satisfies the prescribed error bound. A series of case studies show that MCRDA converges rapidly when the edges of the systems have low reliabilities. Therefore, the approach can be used to evaluate large-scale lifeline systems subjected to strong seismic wave excitation.     

基于Hv方法的三轴液压振动系统频响函数估计的研究

三轴液压振动系统是实验室内模拟振动环境的主要设备,其主要功能是精确地复现给定的功率谱和时间历程。振动控制技术是实现这一目标的关键技术,它以频响函数估计为基础。频响函数估计的精度影响控制过程的快速性和稳定性。本文对几种典型的频响函数估计方法进行了分析,针对传统方法精度低且受到矩阵病态条件约束等缺点,应用特征分析与摄动理论构造了一种多入多出的Hv方法,并且应用三轴液压振动系统的实测数据对其进行了检验。结果表明该方法是适用的。     

A 3D discrete FEM iterative algorithm for solving the water pipe cooling problems of massive concrete structures

Water pipe cooling has been widely used for the temperature control and crack prevention of massive concrete structures such as high dams. Because both under‐cooling and over‐cooling may reduce the efficiency of crack prevention, or even lead to great harm to structures, we need an accurate and robust numerical tool for the prediction of cooling effect. Here, a 3D discrete FEM Iterative Algorithm is introduced, which can simulate the concrete temperature gradient near the pipes, as well as the water temperature rising along the pipes. On the basis of the heat balance between water and concrete, the whole temperature field of the problem can be computed exactly within a few iteration steps. Providing the pipe meshing tool for building the FE model, this algorithm can take account of the water pipe distribution, the variation of water flow, water temperature, and other factors, while the traditional equivalent algorithm based on semi‐theoretical solutions can only solve problems with constant water flow and water temperature. The validation and convergence are proved by comparing the simulated results and analytical solutions of two standard second‐stage cooling problems. Then, a practical concrete block with different cooling schemes is analyzed and the influences of cooling factors are investigated. In the end, detailed guidance for pipe system optimization is provided. Copyright © 2015 John Wiley & Sons, Ltd.     

Selection of sand models and identification of parameters using an enhanced genetic algorithm

Numerous constitutive models of granular soils have been developed during the last few decades. As a consequence, how to select an appropriate model with the necessary features based on conventional tests and with an easy way of identifying parameters for geotechnical applications has become a major issue. This paper aims to discuss the selection of sand models and parameters identification by using genetic algorithm. A real‐coded genetic algorithm is enhanced for the optimization with high efficiency. Models with gradually varying features (elastic‐perfectly plastic modelling, nonlinear stress–strain hardening, critical state concept and two‐surface concept) are selected from numerous sand models as examples for optimization. Conventional triaxial tests on Hostun sand are selected as the objectives in the optimization. Four key points are then discussed in turn: (i) which features are necessary to be accounted for in constitutive modelling of sand; (ii) which type of tests (drained and/or undrained) should be selected for an optimal identification of parameters; (iii) what is the minimum number of tests that should be selected for parameter identification; and (iv) what is the suitable and least strain level of objective tests to obtain reliable and reasonable parameters. Finally, a useful guide, based on all comparisons, is provided at the end of the discussion. Copyright © 2015 John Wiley & Sons, Ltd.     

Modelling combined open channel flow by artificial neural networks

Combined open channel flow is encountered in many hydraulic engineering structures and processes, such as irrigation ditches and wastewater treatment facilities. Extensive experimental studies have conducted to investigate combined flow characteristics. Nevertheless, there is no simple relationship that can fully describe the velocity profiles in a turbulent flow. The artificial neural network (ANN) has great computational capability for solving various complex problems, such as function approximation. The main objective of this study is to evaluate the applicability of the ANN for simulating velocity profiles, velocity contours and estimating the discharges accordingly. The velocity profiles measured by an acoustic doppler velocimeter in the open channel of the Chihtan purification plant, Taipei, with different discharges at fixed measuring section and different depths are presented. The total number of data sets is 640 and the data sets are split into two subsets, i.e. training and validation sets. The backpropagation algorithm is used to construct the neural network. The results demonstrate that the velocity profiles can be modelled by the ANN, and the ANN constructed can nicely fit the velocity profiles and can precisely predict the discharges for the conditions investigated. Copyright © 2005 John Wiley & Sons, Ltd.     

位场DFT算法研究

本文推广了经典的抽样定理,并据此导出了函数有限离散傅里叶变换误差方程（简称DFT误差方程,下同）。该方程把有限离散傅里叶变换中固有的离散效应和有限效应表示为确切的数学形式。离散效应被表示为一个含整参变量（参变量取0,1,…,N-1）的复无穷级数;有限效应被表示为一个含整参变量（参变量取0,1,…,N-1）的复无穷级数的DFT。 基于DFT误差方程和位函数特点,作者提出了两种位场数值傅里叶变换新算法--移样法和等效源续尾叠样法。移样法可近百倍地提高位场数值傅里叶反变换的精度,等效源续尾叠样法可数十倍地提高正变换精度。两种算法都不需要增加资料长度和取样密度,因而基本不需要增加计算机时间和内存。文中给出了算例。     

快速序贯算法

本文讨论了序贯算法的基本原理，与ｋａｌｍａｎ滤波的关系，和序贯算法的渐近性态，得到无先验信息的序贯算法不受初值的影响。实例分析表明，此序贯算法可适于方程系数阵的求逆、求解和病态方程的快速解算。     

神经网络方法在地震预报研究中的初步应用

本文把神经网络方法引进地震预报研究当中。使用地震频次，最大震级，平均震级，等价地震次数等多项地震活动性指标作为神经网络的输入，未来时段内的最大地震震级作为其输出，可以对某一固定地区的最大地震震级作出中近期预报。选用的神经网络模型为含两个中间层的前向模型，并采用ＢＰ算法。所得结果表明，用神经网络方法可以在一定精度范围内使震级预报的内检符合率达到１００％，在本文的例子中，外推预报准确率达到６０％以上。     

遗传算法及其在GOMS模型反演中的应用效果分析

几何光学交互遮蔽模型（GOMS）是一种重要的遥感前向模型,它较好解释了“热点”现象,具有较强的前向模拟能力。但由于其固有的非线性性,给反演带来困难。本文尝试采用近年来兴起的并行随机全局寻优算法－遗传算法对GOMS进行反演,并针对传统遗传算法的不足进行了改进。在使用相同先验知识的条件下,将该算法与目前最有效的约束非线性最优化确定性搜索算法－逐步二次规划法对GOMS模型的反演效果进行了比较,结果表明,逐步二次规划法搜索效率较高,但结果受初值的影响很大,初值选择不当,易收敛于局部最优解,而遗传算法具有全局最优的收敛效果,但局部搜索效率较差。在某些对精度要求不高,而对搜索效率要求较高的场合,可以采用遗传算法与确定性搜索算法相结合的混合遗传算法,以提高算法的搜索效率,获得较为满意的效果。