钢筋混凝土受弯构件的断裂有限元分析

将混凝土线弹性断裂力学用于钢筋混凝土结构，对五种不同含钢率的钢筋混凝土梁的裂缝扩展问题，分别进行了以应力为判据的钢筋混凝土非线性有限元计算和以应力强度因子为判据的断裂有限元计算，同时对两组不同配筋率的钢筋混凝土梁进行了试验研究．其结果表明，配筋率对裂缝的扩展有很大影响；用有限元法分析具有较低配筋率的钢筋混凝土构件时，按断裂力学的开裂准则要比按应力σｍａｘ≤σｌ的开裂准则更客观     

基于刚性梁实现的恒应变率试验系统

介绍一种基于刚性梁、在软件性试验机上测定材料，在控制应变率条件下的应变软件系统的试验系统，得到了刚性梁试验系统中试件应变速率与系统施力速率之间的函数关系，给出了一组实测结果，讨论了测试与控制精度 。     

水泵模型泵段及水泵模型装置试验台

介绍了江苏理工大学流体机械研究所的水泵模型泵段及水泵模型装置试验台，分析了试验台的特点和基本功能，其效率测量的综合误差为±0.761％，测量精度超过GB3216-89BtafoISO／DIS5198A级（草案）标准，可满足多种用途的试验需要．     

期望体理论的实验研究及端部放矿崩矿步距优化

放出体形态研究是研究崩落矿岩流动规律和确定最优采场结构参数的基础.基于标志颗粒法,通过底部与端部放矿物理实验,研究无限边界条件和半无限边界条件下放出体形态及其变化规律,验证期望体理论的可靠性和适用性.在此基础上,开展了18 m×20 m结构参数下端部放矿崩矿步距的优化实验研究.研究结果表明：实际放出体形态并不是标准的椭球体,而是与期望体更为相近;在无限边界条件和半无限边界条件下,放矿量与放出体高度满足幂函数关系,与放出体半径满足指数函数关系;建议在18 m×20 m结构参数下,优先选用无贫化放矿方式和4.6 m的崩矿步距.     

基于激光遥测方法的某在役风电塔现场实测分析

风力发电塔作为一类特殊的高柔结构,顶部支撑的风机在工作状态下对塔身产生动态激励,目前的风电塔振动实测通常采用接触式传感器.为发展风电塔现场非接触测试技术,本文对激光遥测方法进行阐述,利用加速度计和激光遥测设备对某1.5 MW风电塔进行环境脉动激励下的动力实测,对两种仪器的现场使用及测试结果进行对比,分析遥测方法的优势.利用得到的多组实测信号,分别采用峰值拾取法和随机子空间识别法进行分析处理,获得该风电塔的自振频率.     

1Cr9Mo钢高速气-固两相流冲蚀磨损

采用自制的激波驱动气-固两相流冲蚀磨损试验装置,选取SiO2、Al2O3和SiC颗粒,对煤化工常用材料1Cr9Mo钢进行高速气-固两相流冲蚀磨损试验研究.结合试件表面冲蚀磨损形貌,分析冲击速度、冲击角度、颗粒硬度、颗粒粒径、试件温度等因素对材料的冲蚀磨损率的影响.结果表明:在20 ℃和400℃下,1Cr9Mo钢的最大冲蚀磨损率均出现在15°～25°的冲蚀角之间,体现出典型塑性材料的冲蚀磨损特征;低角度冲蚀时磨损机理以颗粒的切削作用为主,高角度冲蚀时颗粒垂直撞击材料表面产生凹坑并致使凹坑周围的片状物碎屑从材料表面剥离;试件冲击速度指数在2.3 ～3.2范围内,磨损率受颗粒硬度影响较大;在相同冲蚀条件下,硬度较高的Al2O3和SiC颗粒对试件的磨损率比SiO2颗粒高一个数量级;磨损率随颗粒粒径的增大呈现先递增后下降的趋势;在400℃时SiO2颗粒对试件的冲蚀磨损率明显提高,磨损率最大值约为20℃时的3倍.     

硬塑高黏度地层盾构施工土体改良试验研究

以上海6号土中盾构施工为研究背景,进行硬塑高黏度土体改良的室内试验.选择3种添加剂研究其对渣土性能的影响,并综合分析其最优注入率范围.研究表明:单独使用泡沫需在增加土样含水率到30.0%条件下,控制泡沫注入率(质量比,下同)为30.0%时较好;高分子材料和减黏剂则需在同样含水率下使其注入率分别为6.0%、5.5%时最佳;高分子材料和泡沫组合在高分子材料注入率、泡沫注入率分别为3%、20%时,减黏剂和泡沫组合在减黏剂注入率、泡沫注入率分别为3%、15%时效果更为合适.利用该配比方案进行现场掘进试验,所得结果可为类似地层盾构施工土体改良提供参考.     

Adaptive detector design of MIMO radar with unknown covariance matrix

The problem of detecting signal with multiple input multiple output (MIMO) radar in correlated Gaussian clutter dominated scenario with unknown covariance matrix is dealt with. The general MIMO model, with widely separated sub-arrays and co-located antennas at each sub-array, is adopted. Firstly, the generalized likelihood ratio test (GLRT) with known covariance matrix is obtained, and then the Rao and Wald detectors are devised, which have proved that the Rao and Wald test coincide with GLRT detector.To make the detectors fully adaptive, the secondary data with signal-free will be collected to estimate the covariance. The performance of the proposed detector is analyzed, however, it is just ancillary. A thorough performance assessment by several numerical examples is also given, which has considered the sense with co-located antennas configure of transmitters and receivers array.The results show that the performance the proposed adaptive detector is better than LJ-GLRT, and the loss can be acceptable in comparison to their non-adaptive counterparts.     

On a robust test for SETAR‐type nonlinearity in time series analysis

There has been growing interest in exploiting potential forecast gains from the nonlinear structure of self‐exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR‐type nonlinearities in observed time series. However, previous studies show that classical nonlinearity tests are not robust to additive outliers. In practice, time series outliers are not uncommonly encountered. It is important to develop a more robust test for SETAR‐type nonlinearity in time series analysis and forecasting. In this paper we propose a new robust nonlinearity test and the asymptotic null distribution of the test statistic is derived. A Monte Carlo experiment is carried out to compare the power of the proposed test with other existing tests under the influence of time series outliers. The effects of additive outliers on nonlinearity tests with misspecification of the autoregressive order are also studied. The results indicate that the proposed method is preferable to the classical tests when the observations are contaminated with outliers. Finally, we provide illustrative examples by applying the statistical tests to three real datasets. Copyright © 2009 John Wiley & Sons, Ltd.