500kN 足尺磁流变液阻尼器设计的关键技术

对我们制作的、目前出力居世界前列的500kN足尺MR阻尼器设计的关键技术、阻尼器的性能试验及力学模型的参数识别进行了研究。在对磁流变液材料的最大屈服剪应力、抗沉降稳定性以及磁滞响应时间等关键性能试验分析的基础上,确认该磁流变液的抗沉降稳定性和其他两项性能指标满足MR阻尼器的设计与工程应用要求。同时,对此500kN足尺MR阻尼器设计的关键技术,包括内置式碟型弹簧蓄能器、磁场防漏设计以及引线保护等进行了介绍;并通过对此MR阻尼器性能的试验,依据阻尼最小二乘法,对MR阻尼器采用的修正的B ingham模型的参数进行了识别,得到了与试验结果吻合的MR阻尼器的力学模型。通过研究可知,高性能磁流变液材料的制备以及MR阻尼器设计的关键技术是大吨位足尺MR阻尼器成功的关键。     

Morlet 小波用于环境激励下的模态参数识别研究

本文分别从卷积和Parseval定理的角度推导了非正交小波变换系数的实用计算方法。在环境激励下以互相关函数代替系统的自由响应数据,给出了基于Morlet小波变换的频率、阻尼比的参数识别方法,重点介绍了基于最小二乘法的振型识别技术。采用2层楼仿真算例和潮白河桥应用实例验证本算法,识别结果表明基于Morlet小波变换的模态参数识别技术能够有效地识别出环境激励下系统的模态参数。     

地震作用下结构时变物理参数识别

利用小波分析函数多尺度逼近方法,将剪切型结构在地震作用下时变的阻尼和刚度用尺度函数的线性组合表示,将时变参数的辨识问题转化为由已知的正交尺度函数和系统的输入输出来估计线性组合中的时不变系数问题,用最小二乘法对剪切型框架结构在地震作用下时变的刚度和阻尼进行了有效的辨识。此方法无需事先假定系统参数的时变规律,在有噪声情况下可以用Tikhonov正则化方法减小识别方程的不适定性对识别结果的影响。数值算例表明了该方法的有效性。     

基于“葵花8号”气象卫星的陇东南地区强对流识别跟踪技术研究

利用常规观测资料、区域自动站资料和“葵花8号”气象卫星资料,对2016年4—9月甘肃省陇东南地区出现的43次强对流天气过程进行分析,确立了强对流云团识别指标、追踪方法及预报指标,并对2018年部分个例进行效果检验。结果表明：（1）利用卫星B13通道（10.4μm）亮温值TBB≤238 K或B08通道（6.2μm）与B13通道亮温差△TBB<0 K双阈值作为强对流云团识别指标,可以准确识别出陇东南地区的强对流天气云团;（2）利用“逆向搜索法”、“面积重叠法”及对云团重心的计算,可以对强对流云团进行准确定位、追踪及移动路径外推预报;（3）建立的强对流天气落区判别指标对该地区短时强降水及冰雹落区具有一定的预报能力。     

Vision-based tracking with projective mapping for parameter identification of remotely operated vehicles

Parameter identification of a remotely operated vehicle (ROV) is often based on the dynamic responses collected by its onboard sensors. However, for commercial ROVs, the required data for identification may not be available due to the absence of suitable sensors or limitations in accessing onboard sensor data. Therefore, this study proposes a vision-based tracking system to measure the dynamic response of an ROV. The tracking system is independent of the ROV, and is able to localize an ROV to a high degree of precision by means of projective mapping. The validity of the proposed tracking system is demonstrated through identification of a commercial ROV. A simplified nonlinear ROV dynamic model with six degrees of freedom (DOF) is used for identification. Uncoupled motions, including surge, sway, and yaw, are obtained from the ROV dynamic model, and the corresponding experiments are carried out for each DOF. Hydrodynamic parameters are then estimated with numerical optimizations by comparing the measured ROV responses with the output of the mathematical model. The experimental results show that the vision-based tracking system can accurately measure the transient and steady-state responses of an ROV. Additionally, the simulations of the ROV dynamic model, with the optimal parameter estimates, give results in agreement with the measured data.     

System identification for nonlinear maneuvering of large tankers using artificial neural network

This paper deals with the application of nonparametric system identification to a nonlinear maneuvering model for large tankers using artificial neural network method. The three coupled maneuvering equations in this model for large tankers contain linear and nonlinear terms and instead of attempting to determine the parameters (i.e. hydrodynamic derivatives) associated with nonlinear terms, all nonlinear terms are clubbed together to form one unknown time function per equation which are sought to be represented by the neural network coefficients. The time series used in training the network are obtained from simulated data of zigzag maneuvers and the proposed method has been applied to these data. The neural network scheme adopted in this work has one middle or hidden layer of neurons and it employs the Levenberg–Marquardt algorithm. Using the best choices for the number of hidden layer neurons, length of training data, convergence tolerance etc., the performance of the proposed neural network model has been investigated and conclusions drawn.     

一些海洋动植物区系的真菌分离和抑菌活性初步研究

从南海海域的鱼类、贝类、海藻以及海泥样品中分离出199株海洋真菌,分属于青霉属、曲霉属和酵母属等9个属,其中以青霉属和曲霉属真菌居多,分别占28.6%和26.8%。以大肠杆菌和金黄色葡萄球菌作为指示菌,筛选出抑菌活性菌株7株,其中2株既抗大肠杆菌又抗金黄色葡萄球菌,4株抗金黄色葡萄球菌,1株抗大肠杆菌。     

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Mapping of moraine dammed glacial lakes and assessment of their areal changes in the central and eastern Himalayas using satellite data

The relatively rapid recession of glaciers in the Himalayas and formation of moraine dammed glacial lakes(MDGLs) in the recent past have increased the risk of glacier lake outburst floods(GLOF) in the countries of Nepal and Bhutan and in the mountainous territory of Sikkim in India. As a product of climate change and global warming, such a risk has not only raised the level of threats to the habitation and infrastructure of the region, but has also contributed to the worsening of the balance of the unique ecosystem that exists in this domain that sustains several of the highest mountain peaks of the world. This study attempts to present an up to date mapping of the MDGLs in the central and eastern Himalayan regions using remote sensing data, with an objective to analyse their surface area variations with time from 1990 through 2015, disaggregated over six episodes. The study also includes the evaluation for susceptibility of MDGLs to GLOF with the least criteria decision analysis(LCDA). Forty two major MDGLs, each having a lake surface area greater than 0.2 km2, that were identified in the Himalayan ranges of Nepal, Bhutan, and Sikkim, have been categorized according to their surface area expansion rates in space and time. The lakes have been identified as located within the elevation range of 3800 m and6800 m above mean sea level(a msl). With a total surface area of 37.9 km2, these MDGLs as a whole were observed to have expanded by an astonishing 43.6% in area over the 25 year period of this study. A factor is introduced to numerically sort the lakes in terms of their relative yearly expansion rates, based on their interpretation of their surface area extents from satellite imageries. Verification of predicted GLOF events in the past using this factor with the limited field data as reported in literature indicates that the present analysis may be considered a sufficiently reliable and rapid technique for assessing the potential bursting susceptibility of the MDGLs. The analysis also indicates that, as of now, there are eight MDGLs in the region which appear to be in highly vulnerable states and have high chances in causing potential GLOF events anytime in the recent future.