散斑检测技术及其应用

综述了散斑计量中的主要检测技术。主题包括散斑照相技术、散斑干涉技术、电子散斑干涉技术、数字散斑相关技术和时间序列散斑检测技术。介绍了散斑计量在科学和工程研究中的一些重要应用。     

振动监测在强夯试验中的应用

以工程实例为基础,较深入地探讨了强夯试验中振动监测在确定最大夯击数、划分强夯振动影响范围以及计算地基能量吸收系数等方面的应用。     

土石坝动力反应分析

土石坝动力反应分析是土石坝力学计算中较为复杂和困难的课题。原因之一在于计算中选用的土石料和混凝土的动力本构模型与实际状态模拟有误 ;之二在于计算中动力平衡方程求解的复杂。本文试就动力反应的分析进行尝试 ,文中的计算方法在实际工程中得到验证 ,对于浙江溪口抽水蓄能电站上库面板堆石坝和四川清平水库面板堆石坝的计算分析得出合理有效的结果。1　动力本构模型1 .1　土石料动力本构模型土石料的动力性质较为复杂 ,它随振动频率而变化 ;剪切模量和阻尼比随动剪应变而变化。目前描述土石的动应力应变关系的模型有双线形模型、仑贝…     

圆运动单轴惯性振动筛整机参数多目标模糊优化设计

通过对圆运动单轴惯性振动筛进行运动学和动力学分析,建立了基于结构工艺参数、运动参数、动力参数和具有模糊约束的整机模糊优化模型,论述设计计算中模糊信息的处理方法、表格数据在程序中的处理方法以及优化的求解算法,最后通过实例计算比较,得到了客观而较佳的整机参数。     

Design and Commissioning of a Friction Welding Machine

An experimental lightweight linear friction welding machine was designed and commissioned. The machine was designed to explore higher frequencies of oscillation (up to 1 kHz) than those commercially available, for a number of different amplitudes of oscillation. During the commissioning stage problems related to rig stiffening were recognized and the operating envelope of the machine was identified. Important friction welding process parameters like stress and temperature are monitored to evaluate process development and weld integrity.     

振动监测与分析技术在核电站中的应用

从保障核全安、提高核电经济运行水平、解决重大技术问题和预测性维修等４个方面介绍了振动监测与分析技术在核电站中的应用,并通过实例说明了振动监测及分析技术在大亚湾核电站安全,稳定,经济运行中所起的重要作用。     

流体阻尼器粘性发热对整星隔振的影响

在航天器发射过程中,流体粘度随温度变化而改变,流体阻尼器具有不同的阻尼比和共振频率。结合流体阻尼器在整星隔振中的应用,建立发射阶段的热动力学模型。通过数值模拟比较不同粘温系数、不同阻尼器参数下的整星隔振秒特性,并对粘性发热的作用进行了试验研究。理论分析和试验表明,随着时间的推移,阻尼器温度升高,隔振系统的共振频率发生偏移,共振峰值发生变化。     

仪器安装板振动环境虚拟试验和验证技术研究

基于商用有限元软件MSC.Patran和Nastran,研究了导弹仪器安装板结构的振动环境预示技术。建立了仪器安装板的三维实体模型,单机仪器使用质点模拟,并通过MPC法与安装板连接;设置了多种阻尼特性,通过模态叠加法预示了该仪器安装板在振动条件下的结构动力学环境,通过振动试验验证了预示结果。研究发现,使用大阻尼能较好地预示该仪器安装板结构的动力学环境。     

Neural Network–Based Intelligent Compaction Analyzer for Estimating Compaction Quality of Hot Asphalt Mixes

Continuous real-time estimating of compaction quality during the construction of a hot mix asphalt (HMA) pavement is addressed in this paper. The densification of asphalt pavements during construction usually is accomplished by using vibratory compactors. During compaction, the compactor and the asphalt mat form a coupled system whose dynamics are influenced by the changing stiffness of the mat. The measured vibrations of the compactor along with process parameters such as lift thickness, mix type, mix temperature, and compaction pressure can be used to predict the asphalt mat density. Contrary to existing techniques in the literature in which a model is developed to fit experimental data and to predict mat density, a neural network-based approach is adopted that is model-free and uses pattern-recognition techniques to estimate density. The neural network is designed to read the entire frequency spectrum of roller vibrations and to classify these vibrations into different levels. The intelligent asphalt compaction analyzer (IACA) is then trained to convert these vibration levels into a “number” indicative of the asphalt mat density at a given location. This two-step process eliminates the need for regression analysis and produces more accurate density measurements than those reported elsewhere in the literature. Compaction studies of HMA mixes on a stiff subgrade indicate that the changes in the vibration characteristics of the roller are attributable to an increased compaction of the HMA base. The results also show that, with the neural network working as a classifier, the IACA can estimate the density continuously, and in real time, with accuracy levels adequate for quality control in the field.     

Equation error versus output error methods

Abstract

For the identification of linear processes on the basis of ARX-models, equation error least squares (EELS) (often indicated as the one step ahead prediction error method) is frequently used rather than output error least squares (OELS). This is mainly because the minimum of the convex EE-criterion can easily be found, in contrast to the OE-criterion, which often displays multiple local minima. Both methods lead to the correct parameter values when the system is in the model set chosen. But in many practical situations, such as human behaviour, the real process under study will be of infinite order causing essentially different models to be found from either EE or OE criteria. Various aspects of these differences are analysed in this study. Much attention has been paid to the performance of a simulation based on a model estimated with an EELS. This simulation performance can be predicted and bounds can be given without executing the simulation itself. Furthermore the simulation performance is very poor for systems where the energy in the initial impulse response samples is very small compared with the energy in the remainder of the response. For these systems an equation error estimate cannot even provide a proper initial guess for an OELS minimization algorithm. Examples are presented that illustrate this effect.