Gerris数值方案及其在海洋数值模拟中的应用

基于动态自适应网格的开源软件Gerris受到越来越多海洋和水文研究者的关注.概述了Gerris开发背景、研究现状和特点,详细阐述了Gerris数值方案,包括动态自适应网格、动态负载平衡技术原理、广义正交曲线坐标系、内嵌复杂固体边界和地形数据的处理方法,并探讨了Gerris在海洋数值模拟中的初步应用.结果表明,Gerris动态自适应网格在多尺度问题模拟中的优势独特,在海洋数值模拟应用中可通过自适应网格提高地理特征的精度,通过GTS(或KDT)格式的数据来处理地形和网格,达到同时兼顾精确性和易用性的目的,使得Gerris与其他海洋模式进行有机结合成为重要发展方向.     

集值系统的辨识、控制及应用

本文总结了近年来关于集值系统的研究工作:在辨识方面,从不同的系统结构、不同的噪声情况和不同的集值情形等方面进行了深入的研究,针对性地提出了参数解耦、比例满秩输入设计、联合可辨识、经验分布函数、递推投影辨识等有效的辨识方法,得到了一系列重要结果;在适应控制方面,都实现了一类集值增益系统的适应跟踪控制;在同步控制方面,提出了双时间尺度同步算法和递推投影同步算法,实现了二值量测下的多个体同步控制;在应用方面,分别将集值辨识应用到复杂疾病建模和雷达目标识别问题中,构造了新的、更为有效的统计验证方法,在低信噪比下将识别率提高了10%.     

Adjoint-based Sensitivity Analysis of a Mesoscale Low on the Mei-yu Front and Its Implications for Adaptive Observation

An adjoint sensitivity analysis of one mesoscale low on the mei-yu Front is presented in this paper. The sensitivity gradient of simulation error dry energy with respect to initial analysis is calculated. And after verifying the ability of a tangent linear and adjoint model to describe small perturbations in the nonlinear model, the sensitivity gradient analysis is implemented in detail. The sensitivity gradient with respect to different physical fields are not uniform in intensity, simulation error is most sensitive to the vapor mixed ratio. The localization and consistency are obvious characters of horizontal distribution of the sensitivity gradient, which is useful for the practical implementation of adaptive observation. The sensitivity region tilts to the northwest with height increasing; the singular vector calculation proves that this tilting characterizes a quick-growing structure, which denotes that using the leading singular vectors to decide the adaptive observation region is proper. When connected with simulation of a mesoscale low on the mei-yu Front, the sensitivity gradient has the following physical characters： the obvious sensitive region is mesoscale, concentrated in the middle-upper troposphere, and locates around the key system; and the sensitivity gradient of different physical fields correlates dynamically.     

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the “on-off” switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization “on-off” switches in the forcing term, the impacts of “on-off” switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.     

Real‐time hybrid testing of a smart base isolation system

Real‐time hybrid testing is a very effective technique for evaluating the dynamic responses of rate‐dependent structural systems subjected to earthquake excitation. A smart base isolation system has been proposed by others using conventional low‐damping isolators and controllable damping devices such as magnetorheological (MR) dampers to achieve specified control target performance. In this paper, real‐time hybrid tests of a smart base isolation system are conducted. The simulation is for a base‐isolated two‐degrees‐of‐freedom building model where the superstructure and the low‐damping base isolator are numerically simulated, and the MR damper is physically tested. The target displacement obtained from the step‐by‐step integration of the numerical substructure is imposed on the MR damper, which is driven by three different control algorithms in real‐time. To compensate the actuator delay and improve the accuracy of the test, an adaptive phase‐lead compensator is implemented. The accuracy of each test is investigated by using the root mean square error and the tracking indicator. Experimental results demonstrate that the hybrid testing procedure using the proposed actuator compensation techniques is effective for investigating the control performance of the MR damper in a smart base isolation system. Copyright © 2013 John Wiley & Sons, Ltd.     

Pre- and post-earthquake regional loss assessment using deep learning

As urban systems become more highly sophisticated and interdependent, their vulnerability to earthquake events exhibits a significant level of uncertainties. Thus, community-level seismic risk assessments are indispensable to facilitate decision making for effective hazard mitigation and disaster responses. To this end, new frameworks for pre- and post-earthquake regional loss assessments are proposed using deep learning methods. First, to improve the accuracy of the response prediction of individual structures during the pre-earthquake loss assessment, a widely used nonlinear static procedure is replaced by the recently developed probabilistic deep neural network model. The variabilities of the nonlinear responses of a structural system given the seismic intensity can be quantified during the loss assessment process. Second, to facilitate near-real-time post-earthquake loss assessments, an adaptive algorithm, which identifies the optimal number and locations of sensors in a given urban area, is proposed. Using a deep neural network that estimates area-wide structural damage given the spatial distribution of the seismic intensity levels as a surrogate model, the algorithm adaptively places additional sensors at property lots at which errors from surrogate estimations of the structural damage are the greatest. Note that the surrogate model is constructed before earthquake events using simulated datasets. To test and demonstrate the proposed frameworks, the paper introduces thorough numerical investigations of two hypothetical urban communities. The proposed frameworks using the deep learning methods are expected to make critical advances in pre- and post-earthquake regional loss assessments.     

Diffraction imaging using an adaptive phase filter

As an ideal carrier of high-resolution information, seismic diffraction can be used to clarify and locate small-scale discontinuities or inhomogeneities in the subsurface. However, a diffraction is weak and thus be suppressed by the specular reflection. Furthermore, a diffraction would be destroyed by the conventional imaging method due to the polarity reversal of diffraction. In this paper, we analyse the behaviour of diffraction and reflection. For the image point on a horizontal or oblique reflector, the zone on both sides of the stationary point has the same energy after using a cosine weight function. Based on the behaviour, we propose the adaptive phase filter to adjust the polarity of the energy on both sides, and calculate it through the illumination angle and the reflector dip angle. This method avoids the calculation of the Fresnel zones and can further suppress residual reflection that disturb the diffraction images. Synthetic and field data applications show that the desired imaging results can be obtained by the proposed method. The test results demonstrate that the method is efficient in detecting small-scale discontinuities or inhomogeneities in the subsurface and can provide high-resolution information for seismic interpretation.     

Spherical sliding isolation bearings with adaptive behavior: Theory

The principles of operation and force–displacement relationships of three novel spherical sliding isolation bearings are developed in this paper. These bearings are completely passive devices, yet exhibit adaptive stiffness and adaptive damping. That is, the stiffness and damping change to predictable values at calculable and controllable displacement amplitudes. The primary benefit of adaptive behavior is that a given isolation system can be separately optimized for multiple performance objectives and/or multiple levels of ground shaking. With the devices presented here, this is accomplished using technology that is inherently no more complex than what is currently used by the civil engineering profession. The internal construction consists of various concave surfaces and behavior is dictated by the different combinations of surfaces upon which sliding can occur over the course of motion. As the surfaces upon which sliding occurs change, the stiffness and effective friction change accordingly. A methodology is presented for determining which surfaces are active at any given time based on the effective radius of curvature, coefficient of friction and displacement capacity of each sliding surface. The force–displacement relationships and relevant parameters of interest are subsequently derived based on the first principles. Copyright © 2007 John Wiley & Sons, Ltd.     

中国东北深源地震定位方法解析

黑龙江省有时会发生震源深度300-600 km的深源地震,且地震释放能量及有感范围均较大。该地区发生M > 6.0深源地震会产生较大社会影响,对于深源地震,快速准确地确定震源参数具有重要意义。利用黑龙江省测震台网自"十五"项目投入运行以来记录的31条深源地震数据,采用LocSAT和HypoSAT定位方法进行重新定位,将定位结果与中国地震台网中心统一编目给出的目录数据进行对比,寻求更加适用该区深震的定位方法。分析表明,对于黑龙江省测震台网记录的东北地区深源地震,使用HypoSAT方法进行定位结果优于LocSAT定位方法。     

扰动重力梯度张量单分量和组合分量最小二乘配置法模型的建立

建立了利用扰动重力梯度张量Tzz分量和Txx+Tyy、Tzz-Txx-Tyy组合分量确定地球重力场的调和分析法模型,进一步推导了扰动重力梯度张量对角线三分量的自协方差和互协方差函数的级数展开式,推导了单分量、组合分量与重力位系数之间协方差函数的实用计算公式,给出了利用单分量和组合分量解算地球重力场模型的最小二乘配置法基本原理公式.结果表明,最小二乘配置法具有一定的抗差能力,随着观测数据误差的不断增大,其恢复的重力场模型有效阶次不断降低,精度也不断下降;Tzz-Txx-Tyy组合分量解算重力场模型的精度最高,其次为Tzz分量,Txx+Tyy组合分量最差.