ANSYS在高层建筑抗震中的应用

文章利用ANSYS中动力分析的谱分析功能,对某高层框架结构进行了基于振型分解反应谱法的抗震计算,直接得到了准确、丰富的结构动力反应.     

底部两层框架—砼抗震墙,上部五层砖房模型的振动台试验

通过底部两层框架－砼抗震墙、上部五层砖房１／６比例模型的模拟地震振台试验，考察了该结构的水平地震作用下的力学性能，并分析了不同试验阶段模型的动力特性变化规律、地震反应分布规律、薄弱层部位及抗震能力。最后初步提出了这种结构的抗震设计建议。     

河床式水电站厂房上部结构三维有限元抗震分析

利用ansys软件进行河床式水电站上部结构的抗震分析,建立三维有限元动力计算模型。模态分析计算得到其自振特性,并通过反应谱法计算结构的抗震动力响应。将动力计算结果与静力计算结果叠加,结果显示其满足各项要求。     

基于MIDAS GTS尾矿坝动静力效应分析

利用Midas GTS有限元软件建立尾矿坝三维模型,并且基于M_C准则及有限元强度折减法对尾矿坝进行静力稳定性评价分析,采用整体位移和安全系数两个指标评价尾矿坝的稳定性。有限元强度折减法避免了极限平衡理论评价尾矿坝稳定性时将土体视为刚体的缺陷,使结果更为精确可靠。采用反应谱和时程分析法对对尾矿坝进行动力稳定分析。反应谱法考虑了土体的弹塑性结构和坝体结构的动力特性,避免了拟静力法无法考虑结构动力特性反应所带来的不精确性;时程分析法既能考虑地震持续时间对尾矿坝地震效应的影响,同时也能计算能量的损耗和考虑坝体的非线性结构,本文通过对两种方法的计算结果作对比分析得出反应谱理论的局限性。从多角度多方法进行尾矿坝的稳定分析评价,才能确保人民生命财产安全,更有效率地发展国民经济。     

某电力厂房的地震反应分析

对某钢筋混凝土横向框排架、纵向纯框架的电力厂房进行地震反应分析,研究该厂房的动力特性、受力影响、扭转效应等,分析结果可供设计时参考。     

大跨度连续刚构桥桥墩的地震响应分析

为分析桥墩形式对大跨度连续刚构桥的地震影响,以某连续刚构桥为工程背景,运用大型专业有限元分析软件MIDAS CIVIL建立动力分析模型,分别对不同墩截面形式进行模态分析,采用动力反应谱分析法计算出结构的动力响应,并对不同结果对比分析,所得结论为大跨度连续刚构桥的抗震分析和设计提供相关依据。     

上市公司财务重述公告的市场反应研究

本文利用事件研究法对发布财务重述公告的上市公司在公告日前后的市场反应进行了检验，并对比分析了不同类型重述公告引发的市场反应的差异。研究发现，上市公司的财务重述具有显著的负面市场反应，且不同类型重述公告的市场反应各异。由收入确认问题引发的财务重述，其负面市场反应大于由其他问题导致的财务重述的市场反应；当重述公告涉及范围广、重述发起人为外部监管机构、重述涉及核心会计指标以及重述导致盈余调减时，其负面市场反应更为显著。     

高层双塔结构的静力弹塑性Pushover研究分析

高层双塔结构是一种复杂高层结构,文章基于静力弹塑性Pushover方法分析了高层大底盘双塔结构的地震反应特性,研究了自振周期、振型特点、地震作用和结构位移等特点,对常用的高层双塔结构的动力特性有了进一步认识,并为设计提出了一些有益的建议。     

振荡与计时测定

简要介绍了振荡实验，以及反应的原理与波动图形。     

唠加长

我想，绝大多数人一提到国产后的凯迪拉克ATS-L,第一反应都是轴距加长，然后……就没有第二反应了，其实改变远不止于此，本期我们就来唠一唠加长和加长之外的ATS-L。和去年推出的进口版标准轴距ATS相比，国产版在轴距上加长了85毫米。     

光谱失配误差对光伏组件测试的影响研究

光伏组件测试中,太阳模拟器的光谱辐照度分布、光伏组件的光谱响应度、PN结温度等是影响测试结果的主要因素。本文从太阳模拟器光谱失配误差产生的原因出发,研究了光谱失配误差的理论处理方法,并使用脉冲模拟器对单晶硅、非晶硅光伏组件进行了实验分析,给出了减小或修正光谱失配误差对光伏组件测试结果影响的措施建议。     

基于谱分析的连续加工过程检验批量的确定

在连续加工过程中 ,合理地确定检验批量 ,不仅可以反映加工过程的稳定性 ,而且也是确定抽样检验方案及检验频次的基础。本文主要利用时间序列的谱分析方法对连续加工过程的质量检测数据进行周期划分 ,从而确定抽样检验的批量 ,并根据周期分析结果 ,指出了导致加工质量产生波动的一些原因。     

A bootstrap-assisted spectral test of white noise under unknown dependence

To test for the white noise null hypothesis, we study the Cramér-von Mises test statistic that is based on the sample spectral distribution function. Since the critical values of the test statistic are difficult to obtain, we propose a blockwise wild bootstrap procedure to approximate its asymptotic null distribution. Using a Hilbert space approach, we establish the weak convergence of the difference between the sample spectral distribution function and the true spectral distribution function, as well as the consistency of bootstrap approximation under mild assumptions. Finite sample results from a simulation study and an empirical data analysis are also reported.     

Identifying environmentally important supply chain clusters in the automobile industry

In this paper, we develop a new approach that combines the spectral clustering method and input–output analysis to detect environmentally important supply chain clusters. The newly developed method was applied to automobile manufacturing in Japan, and major clusters with high energy intensities in the automobile supply chain were identified. This paper proposes that the car manufacturers will be able to regularly publish their life-cycle assessment reports with a focus on the indirect energy consumptions within the critical supply chains and request key auto-part manufacturers in the cluster to reduce the indirect consumptions through the relevant supply chain engagement.     

New international evidence on the cyclical behaviour of output: Kuznets swings reconsidered

Spectral analysis is a particularly valuable method for seeking dependences expressed as lags between different magnitudes. Its use in this article was first determined by the search for maximum objectivity in the observation of time series. The possibility of applying it to a large number of series was then examined. This twin requirement resulted from a desire to avoid the criticism generally levelled at statistical studies concerning cyclical movements of the economy. Spectral analysis is based on the theory of stochastic processes. It starts with the core hypothesis that a given time series consists of a large number of sinusoidal components with different frequencies (univariate spectral analysis). It makes it possible to divide a particular category of records into a set of oscillations of different frequencies and then to show the links between the components with the same frequency in the various series examined (cross-spectral or bivariate spectral analysis). It has had limited applications in cliometrics to date. It is used here to determine the frequency of GDP series of several OECD countries. A reminder of the method Sect. 2 is followed by successive examination of the various series chosen, the treatment of these series and the results of spectral analysis Sect. 3. It is then possible as a conclusion to show the prospects of this type of approach and to synthesise a completely new major result for understanding economic dynamics in nineteenth and twentieth centuries, that is to say the existence of a single intermediate cycle with 15–20-year frequency that calls into question or even partially contradicts previous work on economic cycles.      

A Frequency-domain Based Test for Non-correlation between Stationary Time Series

A one-sided asymptotically normal test for non-correlation between two stationary time series is proposed based on the spectral coherence function. The test statistic is a properly standardized version of the integrated spectral coherency and has similar asymptotic properties as a previously introduced time domain based test for non-correlation. Unlike its time domain counterpart, the proposed test does not require prewhitening of the time series and, thus, is a truly nonparametric test for non-correlation. In a simulation study, we evaluate the small sample performance of the proposed test in comparison with the time domain test and address the problem of bandwidth selection. Furthermore, we present a modification of the test statistic that allows to test for non-correlation over frequency bands. This version shows higher power of detecting interrelationships restricted to the frequency band of interest. This work has been carried out at the Institute of Applied Mathematics at the University of Heidelberg and partly while the author was visiting the Department of Statistics at the University of Chicago.     

On the performance of the ordinary least squares method under an error component model

Starting from the one-dimensional results by Wang et al (1994) we consider the performance of the ordinary least squares estimator in comparison to the best linear unbiased estimator under an error component model with random effects in units and time. Upper bounds are derived for the first-order approximation to the difference between both estimators and for the spectral norm of the difference between their dispersion matrices.     

Measuring and testing Granger causality over the spectrum: An application to European production expectation surveys

Decomposing Granger causality over the spectrum allows us to disentangle potentially different Granger causality relationships over different frequencies. This may yield new and complementary insights compared to traditional versions of Granger causality. In this paper, we compare two existing approaches in the frequency domain, proposed originally by Pierce [Pierce, D. A. (1979). R-squared measures for time series. Journal of the American Statistical Association, 74, 901–910] and Geweke [Geweke, J. (1982). Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association, 77, 304–324], and introduce a new testing procedure for the Pierce spectral Granger causality measure. To provide insights into the relative performance of this test, we study its power properties by means of Monte Carlo simulations. In addition, we apply the methodology in the context of the predictive value of the European production expectation surveys. This predictive content is found to vary widely with the frequency considered, illustrating the usefulness of not restricting oneself to a single overall test statistic.     

Dimensionality reduction in forecasting with temporal hierarchies

Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. It can be challenging to estimate the full cross-covariance matrix for a temporal hierarchy, which can easily be of very large dimension, yet it is difficult to know a priori which part of the error structure is most important. To address these issues, we propose to use eigendecomposition for dimensionality reduction when reconciling forecasts to extract as much information as possible from the error structure given the data available. We evaluate the proposed estimator in a simulation study and demonstrate its usefulness through applications to short-term electricity load and financial volatility forecasting. We find that accuracy can be improved uniformly across all aggregation levels, as the estimator achieves state-of-the-art accuracy while being applicable to hierarchies of all sizes.     

The use of spectral analysis to validate peanning models

Statistical fit of model predictions to empirical evidence is found to be an insufficient condition for establishing the validity of a planning model where the dynamic behavior is of particular importance. The paper describes a spectral analysis statistical test that can be used to validate the structure of a planning model by comparing the time series generated by the model with the actual time series of events for the real system under study. Validation of an ambulance simulation model is reported in which the model apparently was valid based on classical goodness of fit tests of aggregate data. However, following a spectral analysis of the simulation results, an entirely new method of generating incidents was found to be necessary. The resulting model then was able to duplicate realistically the essential cyclical nature of hourly demand for emergency care observed in the real system which periodically created excessive busy and idle periods not realized in the structure of the original model.