分布式光纤传感测试系统(BOTDA)用于H型钢梁变形监测的实验研究

介绍了分布式光纤传感测试系统(BOTDA)的应变测量原理,并应用BOTDA技术对H型钢梁加载后的应变分布进行了测量。试验表明,BOTDA的应变测量结果与电阻应变片的测量结果及理论值具有良好的一致性。光纤测量的数据是连续的,因而更加适用于那些需要测试连续变形的结构。由BOTDA的实测应变分布推算出的H型钢梁的弯矩和挠度,与理论分析结果高度吻合。作为先进的监测技术手段,分布式光纤传感测试系统应用于SMW工法桩施工监测是完全可行的。     

用于结构健康监测的智能夹层研究进展

利用改进的柔性印刷电路技术,把一个分布式传感器/驱动器网络预先复合到一片绝缘载体膜上形成薄层,再结合相应的信号控制和处理软件,这就是当前先进的智能夹层(SMARTLayer)技术。结构健康监测(SHM)系统中智能夹层的提出及发展,将有可能把目前广泛采用的离线、静态、被动的材料及结构的损伤检测,转变为在线、动态、实时的健康监测,从而导致工程结构及材料安全监测与性能改善思路产生质的飞跃,引起结构及材料设计思想的变革。在本文中,主要阐述了智能夹层的发展、特点、关键技术、部件、相关的数据处理和应用情况,以及对今后的发展提出了一些需要注意解决的问题。在研究中,我们发现嵌入到玻璃纤维复合材料中的智能夹层对工程材料基体的力学性能没有产生明显的影响,并且自身的电学性能也非常稳定。     

基于机电阻抗技术的管道法兰结构健康监测实验研究

基于机电阻抗技术,采用一种新型压电陶瓷纤维复合材料MFC(Macro-fiber composite),对管道连接处法兰的螺栓工况进行监测实验研究。首先,研究了螺栓扭矩大小对管道法兰结构的阻抗的影响程度。在对螺栓开始施加扭矩时阻抗值变化明显,当扭矩增大到一定程度后,阻抗变化减缓。同时进一步研究了松动螺栓数量与管道法兰结构的阻抗变化之间的关系。结果显示,结构的损伤值随松动螺栓个数的增加呈线性增长。采用均方根偏差统计算法,对在不同工况条件下管道法兰结构处测得的阻抗值进行了有效的分析和处理。结果表明,采用MFC作为压电敏感元件的机电阻抗法,用于管道法兰结构健康监测具有较好的稳定性和应用潜力。     

以随机响应为目标的方舱结构动力修改

针对振动系统在随机激励下的响应进行结构动力修改是具有实用价值的工程问题。本文以系统在道路随机激励下的加速度响应为目标，借助于系统模态及响应的灵敏度分析和矩阵摄动法，对大型机动电子方舱系统进行了快速结构动力修改。     

车-桥系统耦合振动响应的简便计算

依据振动理论推导出了二自由度模型车辆与桥梁系统竖向耦合振动微分方程，采用模态分析的离散化方法，将复杂的偏微分方程问题转化为变系数常微分方程问题，并将微分方程数值积分的Runge-Kutta方法引入到该时变系统的振动响应计算中，使复杂的耦合响应问题得到简便的解决。通过算例验证了该方法的有效性和简便性。该方法只需要直接数值积分，具有公式简单，编程方便，计算速度快等优点，特别适合于工程实际问题的计算，并且不仅适用于匀速运动车辆，也适用于变速运动车辆。     

激光陀螺电路系统的研制

本文分析了系统各部分的原理，给出了测试结果，介绍了系统特点，实验表明达到了引进系统的精度。     

柴油机磨损状态监测及故障诊断专家系统知识库建立的研究

实践表明，为了确保机器的安全运行，避免突发性故障，对其实施磨损状态监测和故障诊断是十分必要的。尽管铁谱技术和光谱技术等在这方面的应用都比较成功，然而它们也各有其局限性。为了把机器的磨损状态监测和故障诊断技术推向更高的科学水平，在摩擦学系统的系统工程思想指导下，针对柴油机的“拉缸”和“拉瓦”故障，并且根据故障发生之前润滑油结构变化的各种信息，利用专家系统开发工具ＥＸＳＹＳ，建立了基于铁谱技术和光谱技术的柴油机磨损状态监测及故障诊断专家系统ＦＤＥＸＳＹＳ（Ｖ１．０）知识库；对该专家系统的测试和实际运行结果表明，所建立的知识库可以比较全面而可靠地描述柴油机的磨损状态及故障前兆，因而能够提醒用户适时采取有效措施以防止故障的发生。不仅如此，所述专家系统知识的获取方法、知识的分类表示及匹配方法等研究，对建立其它摩擦学系统的故障诊断专家系统都具有参考意义。     

Efficient computation of operator‐type response sensitivities for uncertainty quantification and predictive modeling: illustrative application to a spent nuclear fuel dissolver model

This work honors the 75th birthday of Professor Ionel Michael Navon by presenting original results highlighting the computational efficiency of the adjoint sensitivity analysis methodology for function‐valued operator responses by means of an illustrative paradigm dissolver model. The dissolver model analyzed in this work has been selected because of its applicability to material separations and its potential role in diversion activities associated with proliferation and international safeguards. This dissolver model comprises eight active compartments in which the 16 time‐dependent nonlinear differential equations modeling the physical and chemical processes comprise 619 scalar and time‐dependent model parameters, related to the model's equation of state and inflow conditions. The most important response for the dissolver model is the time‐dependent nitric acid in the compartment furthest away from the inlet, where measurements are available at 307 time instances over the transient's duration of 10.5 h. The sensitivities to all model parameters of the acid concentrations at each of these instances in time are computed efficiently by applying the adjoint sensitivity analysis methodology for operator‐valued responses. The uncertainties in the model parameters are propagated using the above‐mentioned sensitivities to compute the uncertainties in the computed responses. A predictive modeling formalism is subsequently used to combine the computational results with the experimental information measured in the compartment furthest from the inlet and then predict optimal values and uncertainties throughout the dissolver. This predictive modeling methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution for the a priori known mean values and uncertainties characterizing the model parameters and the computed and experimentally measured model responses. This approximate a priori distribution is subsequently combined using Bayes' theorem with the “likelihood” provided by the multi‐physics computational models. Finally, the posterior distribution is evaluated using the saddle‐point method to obtain analytical expressions for the optimally predicted values for the parameters and responses of both multi‐physics models, along with corresponding reduced uncertainties. This work shows that even though the experimental data pertains solely to the compartment furthest from the inlet (where the data were measured), the predictive modeling procedure used herein actually improves the predictions and reduces the predicted uncertainties for the entire dissolver, including the compartment furthest from the measurements, because this predictive modeling methodology combines and transmits information simultaneously over the entire phase‐space, comprising all time steps and spatial locations. Copyright © 2016 John Wiley & Sons, Ltd.