裂隙岩体变形模量尺寸效应研究Ⅰ:有限元法

通过对裂隙交叉和非贯通裂隙进行的网格剖分研究,建立了随机裂隙岩体网格生成方法,并编制了裂隙岩体有限元网格自动生成程序。根据锦屏电站辅助洞白山组大理岩裂隙分布调查结果,用蒙特卡洛法生成随机裂隙网络,进行了岩石-裂隙二元岩体网格自动剖分,将试验室获得的岩石和裂隙的力学参数分别赋予岩体模型中的岩石和裂隙,通过有限元方法研究了裂隙岩体等效变形模量的尺寸效应和各向异性。尺寸效应研究结果表明,所研究的裂隙岩体的等效变形模量表征单元体REV为8 m;各向异性研究结果表明,在REV尺寸下,所研究的裂隙岩体各个方向变形模量可以通过柔度张量进行拟合,且张量拟合误差不超过8%。     

石英结晶学优选与应用

石英集合体的结晶学优选可由位错滑移、双晶滑移、定向成核与生长等形成,其中位错滑移是塑性变形岩石中石英结晶学优选产生的最重要的机制。影响变形石英结晶学优选的因素有温度、应变速率、应变、差应力、水、复矿物岩石中各种矿物间的相互作用、初始结晶学方向等。系统总结了石英晶体变形与滑移系,结晶学优选的测量与表达,多种条件下石英的结晶学优选,以及在判断剪切方向、计算运动学涡度、判定变形温度、分析变形历史等方面的应用,并认为应用石英组构作运动学和动力学解析时需与其它微观、宏观现象相结合。     

基于单位四元数的小像幅面阵CCD相机检校

介绍了面阵CCD相机的径向畸变、偏心畸变模型,建立了用于非量测相机标定的扩展共线条件方程.引入了四元数姿态描述方法,利用单位四元数替代外方位角元素(ω,ψ,к)构建旋转矩阵,建立了基于单位四元数的相机标定数学模型.采用室内三维控制场对小像幅面阵CCD相机进行了标定实验,取得了优于0.2个像素的定位精度.     

基于数位的相机径向畸变参数计算

相机畸变是影响基于图像的几何量测、三维重建精度的重要因素之一。空间中一条直线在透视投影作用下理应成为一条直线,但因畸变其构象发生弯曲,通过场景信息中共线点的透视投影不变性可以求解出相机径向畸变系数。考虑畸变的渐变特性,该文设计了基于数位的相机径向畸变参数解算方法:将相机一阶径向畸变系数k1用科学计数法X×10-m表示,依次确定其指数m及尾数X,直至满足精度的设定,最终求解出k1的值。最后通过模拟和实例数据对该方法进行了分析验证。     

Assessment of Jovian synchrotron radiation with a bi-modal electron population

An explicit formula is developed to explore the mechanism of the synchrotron radiation by using a bi-modal loss-cone distribution function. The variation of the distribution function along the field line is modeled in detail and the evaluation of the total power in the synchrotron radiation is presented. The variance of synchrotron radiation with latitude depends on the electron anisotropy; for low anisotropy, synchrotron radiation increases with latitude and reaches a maximum at the particle mirror points; for high anisotropy, it decreases with latitude and maximizes at the equator. A bi-modal population is therefore suggested to explain the radiation intensity which peaks both at the equator and at high latitude.     

Interplanetary turbulences causing unusual anisotropic wave trains in cosmic rays

The various observed harmonics of the cosmic ray variation may be understood on a unified basis if the free space cosmic ray anisotropy is non-sinusoidal in form. The major objective of this paper is to study the first three harmonics of high amplitude wave trains of cosmic ray intensity over the period 1991–1994 for Deep River Neutron Monitoring Station. The main characteristic of these events is that the high amplitude wave trains shows a maximum intensity of diurnal component in a direction earlier than 1800 Hr/co-rotational direction. It is noticed that these events are not caused either by the high-speed solar wind streams or by the sources on the Sun responsible for producing these streams such as polar coronal holes. The direction of semi-diurnal anisotropy shows negative correlation with Bz. The occurrence of high amplitude events is dominant for the positive polarity of Bz component of IMF. The diurnal amplitude of these events shows a negative and the time of maximum shows a weak correlation with disturbance storm time index Dst. The direction of tri-diurnal anisotropy of these events is found to significantly correlate with geomagnetic activity index Ap.     

Harmonics of exceptionaly low amplitude anisotropic wave train events in cosmic ray intensity

The unusually low amplitude anisotropic wave train events (LAEs) in cosmic ray intensity using the ground based Deep River neutron monitor data has been studied during the period 1991–94. It has been observed that the phase of the diurnal anisotropy for the majority of the LAE events remains in the co-rotational direction. However, for some of the LAE events the phase of the diurnal anisotropy shifts towards earlier hours as compared to the annual average values. On the other hand, the amplitude of the semi-diurnal anisotropy remains statistically the same, whereas phase shift-towards later hours; a similar trend has also been found in case of tri-diurnal anisotropy. The high-speed solar wind streams do not play a significant role in causing the LAE events. The occurrence of LAE is independent of the nature of the Bz component of IMF polarity. Published in Astrofizika, Vol. 50, No. 2, pp. 313–324 (May 2007).     

Rainfall‐runoff modelling using artificial neural networks: comparison of network types

Growing interest in the use of artificial neural networks (ANNs) in rainfall‐runoff modelling has suggested certain issues that are still not addressed properly. One such concern is the use of network type, as theoretical studies on a multi‐layer perceptron (MLP) with a sigmoid transfer function enlightens certain limitations for its use. Alternatively, there is a strong belief in the general ANN user community that a radial basis function (RBF) network performs better than an MLP, as the former bases its nonlinearities on the training data set. This argument is not yet substantiated by applications in hydrology. This paper presents a comprehensive evaluation of the performance of MLP‐ and RBF‐type neural network models developed for rainfall‐runoff modelling of two Indian river basins. The performance of both the MLP and RBF network models were comprehensively evaluated in terms of their generalization properties, predicted hydrograph characteristics, and predictive uncertainty. The results of the study indicate that the choice of the network type certainly has an impact on the model prediction accuracy. The study suggests that both the networks have merits and limitations. For instance, the MLP requires a long trial‐and‐error procedure to fix the optimal number of hidden nodes, whereas for an RBF the structure of the network can be fixed using an appropriate training algorithm. However, a judgment on which is superior is not clearly possible from this study. Copyright © 2004 John Wiley & Sons, Ltd.     

An RBF‐based model with an information processor for forecasting hourly reservoir inflow during typhoons

Inflow forecasting is essential for decision making on reservoir operation during typhoons. In this paper, a radial basis function (RBF)‐based model with an information processor is proposed for more accurate forecasts of hourly reservoir inflow. Firstly, based on the multilayer perceptron neural (MLP) network, an information processor is developed to pre‐process the typhoon information (namely, typhoon characteristics and rainfall) and to produce forecasts of rainfall. The forecasted rainfall and the observed inflow are then used as input to the RBF‐based model, which is a nonlinear function approximator, to produce forecasts of hourly inflow. For parameter estimation of the RBF‐based model, the fully‐supervised learning algorithm is used. Actual applications of the proposed model are performed to yield 1‐ to 6‐h ahead forecasts of inflow. To assess the improvement due to the use of the typhoon information processor, models without the typhoon information processor are constructed and compared with the proposed model. The results show that the proposed model performs the best and is capable of providing improved forecasts of hourly inflow, especially for long lead‐time. In conclusion, the proposed model with a typhoon information processor can extract useful information from typhoon characteristics and rainfall, and consequently improve the forecasting performance. Copyright © 2009 John Wiley & Sons, Ltd.