数字化地图印刷一种随机挂网计算模型

基于彩色图像像素3刺激值与相应网点面积率之间的α幂修正的Neugebauer方程,建立由常规的调幅网为基础的调频挂网计算模型,以此实现分色胶片随机挂网     

钢屈服—摩擦复合耗能器的性能研究

本文提出了“综合利用不同耗能原理和机制来设计新型耗能器”的思想,研究开发了钢屈服－摩擦复合耗能器,并对其进行了性能试验和对比试验,考察了耗能器的工作性能和耗能性能。给出了耗能器的恢复力模型,研究结果表明,钢屈服－摩擦复合耗能器利用钢屈服滞回和摩擦两种机制耗能,具有稳定的工作性能,耗能能力强且可调节,构造简单,形状紧凑,体积小,造价低,制作与安装方便,是一种有广阔应用前景的新型耗能减震装置。     

Geophysical flows under location uncertainty,Part I Random transport and general models

A stochastic flow representation is considered with the Eulerian velocity decomposed between a smooth large scale component and a rough small-scale turbulent component. The latter is specified as a random field uncorrelated in time. Subsequently, the material derivative is modified and leads to a stochastic version of the material derivative to include a drift correction, an inhomogeneous and anisotropic diffusion, and a multiplicative noise. As derived, this stochastic transport exhibits a remarkable energy conservation property for any realizations. As demonstrated, this pivotal operator further provides elegant means to derive stochastic formulations of classical representations of geophysical flow dynamics.     

Geophysical flows under location uncertainty,Part II Quasi-geostrophy and efficient ensemble spreading

Models under location uncertainty are derived assuming that a component of the velocity is uncorrelated in time. The material derivative is accordingly modified to include an advection correction, inhomogeneous and anisotropic diffusion terms and a multiplicative noise contribution. In this paper, simplified geophysical dynamics are derived from a Boussinesq model under location uncertainty. Invoking usual scaling approximations and a moderate influence of the subgrid terms, stochastic formulations are obtained for the stratified Quasi-Geostrophy and the Surface Quasi-Geostrophy models. Based on numerical simulations, benefits of the proposed stochastic formalism are demonstrated. A single realization of models under location uncertainty can restore small-scale structures. An ensemble of realizations further helps to assess model error prediction and outperforms perturbed deterministic models by one order of magnitude. Such a high uncertainty quantification skill is of primary interests for assimilation ensemble methods. MATLAB^® code examples are available online.     

1D elastic full‐waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm–Gibbs sampler approach

Stochastic optimization methods, such as genetic algorithms, search for the global minimum of the misfit function within a given parameter range and do not require any calculation of the gradients of the misfit surfaces. More importantly, these methods collect a series of models and associated likelihoods that can be used to estimate the posterior probability distribution. However, because genetic algorithms are not a Markov chain Monte Carlo method, the direct use of the genetic‐algorithm‐sampled models and their associated likelihoods produce a biased estimation of the posterior probability distribution. In contrast, Markov chain Monte Carlo methods, such as the Metropolis–Hastings and Gibbs sampler, provide accurate posterior probability distributions but at considerable computational cost. In this paper, we use a hybrid method that combines the speed of a genetic algorithm to find an optimal solution and the accuracy of a Gibbs sampler to obtain a reliable estimation of the posterior probability distributions. First, we test this method on an analytical function and show that the genetic algorithm method cannot recover the true probability distributions and that it tends to underestimate the true uncertainties. Conversely, combining the genetic algorithm optimization with a Gibbs sampler step enables us to recover the true posterior probability distributions. Then, we demonstrate the applicability of this hybrid method by performing one‐dimensional elastic full‐waveform inversions on synthetic and field data. We also discuss how an appropriate genetic algorithm implementation is essential to attenuate the “genetic drift” effect and to maximize the exploration of the model space. In fact, a wide and efficient exploration of the model space is important not only to avoid entrapment in local minima during the genetic algorithm optimization but also to ensure a reliable estimation of the posterior probability distributions in the subsequent Gibbs sampler step.     

基于AUKF的框架结构离线模型更新混合试验方法

模型更新混合试验在传统混合试验方法的基础上更新与试验构件具有相同恢复力特性的构件,扩展了混合试验方法的应用范围。本文旨在提高模型更新混合试验的精度,降低试验的成本并简化模型更新混合试验方法的流程。自适应UKF(AUKF)算法在传统UKF的基础上加入方差自适应模块,能够减轻初始参数设定对参数识别结果的影响,本文基于AUKF提出一种模型更新混合试验方法。对以Bouc-Wen为恢复力模型的防屈曲约束支撑(BRB)进行低周反复加载虚拟试验,通过Matlab编制AUKF算法程序进行参数识别,验证了AUKF算法的高效准确性。对一榀8层4跨带BRB的钢框架进行混合试验数值仿真,结果表明离线模型更新试验结果较在线模型更新更接近真实结果,且简化了试验流程。     

钢筋-沥青隔震层非线性震动特性研究

为了研究钢筋-沥青隔震层的材料与几何非线性震动特性,对钢筋-沥青隔震结构进行了振动台试验,分析了结构在不同加速度幅值输入时的隔震性能。同时,在钢材的应力-应变关系基础上,考虑构件竖向力的二次矩效应,提出了钢筋-沥青隔震层骨架曲线的计算方法,并结合双线型恢复力模型的滞回规则,建立了相应的恢复力模型。利用建立的恢复力模型,编制程序进行了弹塑性时程分析,并与试验结果作对比。结果表明:当钢筋-沥青隔震层的受力钢筋进入弹塑性阶段时,隔震层能够吸收大部分地震能量,显著降低上部结构的地震反应;弹塑性时程分析结果与试验结果吻合较好,由此建立的恢复力模型准确并且适用,可为钢筋-沥青隔震层的工程设计与非线性地震反应分析提供参考。     

鄱阳湖退田还湖及其对洪水的影响

在详细分析鄱阳湖退田还湖现状基础上,建立了鄱阳湖退田还湖洪水位效应计算模型,估计退田还湖对典型年洪水位的效应.并对退田还湖对年最高水位频率的影响进行了预测.最后,探讨了退田还湖圩区适宜管理模式和退田还湖对湖泊环境的影响.     

Dynamic analysis on structures base-isolated by a ball system with restoring property

A computational model with its analysis method for base-isolated structures by a ball system with restoring property under seismic force is proposed in this paper, and the programs using numerical integration method and incremental harmonic balance method are developed. The analysis method is verified by shaking table test results of a three-storey masonry model. With these programs, the effect of some factors on the aseismic behaviour of base isolation system are analysed, and the comparison of seismic response between structures with and without base isolation is made. Results of both theory and test show that the above-mentioned base isolation system has apparent advantages over the traditional aseismic structures. © 1998 John Wiley & Sons, Ltd.     

基于FFT-MA谱模拟的快速随机反演方法研究

虽然基于地质统计学的随机反演方法能够有效融合测井资料中的高频信息,但计算效率低,占用内存大,限制了它在实际资料中的应用.本文在保留传统随机反演方法优点的基础上,创造性地引入傅里叶滑动平均(Fast Fourier Transform-Moving Average,FFT-MA)谱模拟进行频率域的地质统计模拟,并利用逐步变形算法(Gradual Deformation Method,GDM)确保模拟结果与实际地震数据的匹配,构建了基于FFT-MA谱模拟的新的快速随机反演方法.与常规随机反演相比,新方法不仅分辨率高,而且能够使反演解得到快速收敛,有效提高计算效率,减少内存占用.模型试算获得了与理论模型吻合度较好的高分辨率反演结果.实际资料分析也表明新方法所得到的高分辨率反演结果能够对薄互储层进行良好的展示,为薄储层的识别提供高效可靠的技术支持.