2008年南海季风爆发前后西沙海域海气通量变化特征

基于2008年4至5月在南海西沙永兴岛进行的海气通量观测试验资料和NCEP资料,应用COARE3.0通量算法计算了海气通量,分析了季风爆发前后西沙海域天气变化特点和海气通量对南海季风爆发的响应。结果表明:2008年南海季风首先于5月第1候在南海南部爆发,受热带气旋等因素的影响,北部海区季风爆发推迟到5月18日。季风爆发和热带气旋活动对西沙海域的风速和海气通量影响较大,其中热带气旋的影响更强烈。热带气旋来临之前,潜热通量、感热通量以及动量通量均较小;在气旋活动及此后的季风爆发时期,大风使潜热通量和动量通量显著增强,感热通量则在降水期间变化明显;动量通量的最大值出现在热带气旋活动期间,其在此过程中的均值是观测初期均值的3倍以上。在整个观测过程中,潜热通量明显大于感热通量,后者是前者的16∶1。不同类型天气过程中,潜热通量的日变化相似,而感热通量的日变化有差异。湍流交换系数与风速有较好的相关关系。     

区域-时间-长度算法在青海地区的应用研究

应用检测地震活动水平相对变化的区域-时间-长度算法（RTL算法）,分析研究了青海地区有相对较完备小震资料的14次6级以上震例。结果表明,该区6级以上地震发生前在源区或近源区大约120km半径的空间尺度内,‰值能检测到明显的1年内和2年内时间尺度的中期地震活动异常,地震活动增强的“上升型”异常和地震活动平静的“下降型”异常各占一半。     

MODIS遥感数据格式及使用方法综合分析

针对当前MODIS遥感资料推广应用中数据产品使用者和开发者在实际工作中经常遇到数据格式和计算方法方面的问题,分析了MODIS遥感资料的数据格式,以及如何使用定标后的1B数据计算地球视场的光谱反射率和辐射率进而生成各种气象和环境监测应用产品,并给出了一个数据处理应用实例.     

BP人工神经网络在青藏铁路南段地壳稳定性定量评价中的应用

将BP人工神经网络方法引入区域构造活动性、区域地壳稳定性研究领域,对青藏铁路南段沿线的构造活动性进行定量分析.选用断层运动速率、地震震级、温泉温度及剪切应变4个关键影响因子作为BP人工神经网络的输入向量,构造活动强度(α)作为输出向量,以α为定量判据,将全区划分为相对稳定区(α＜0.22)、较不稳定区(α≈0.22～0.38)、不稳定区(α≈0.38～0.69)、极不稳定区或强烈构造活动区(α≥0.69).在青藏铁路南段沿线划分出格仁错、崩错、当雄-羊八井、错那湖、唐古拉山口南、聂荣东北、聂荣西北、雅鲁藏布江断裂沿线、萨迦等不稳定区,在不稳定区内部进一步划分出申扎、蓬错、尼木、桑雄、羊八井5个极不稳定区.     

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.