须条与长丝间距对Sirofil成纱结构性能的影响

分析了Sirofil纺纱过程中“V”形区的动态变化及粗纱须条与长丝间距大小对“V”形区形状和单纱受力的影响,认为间距增大会使“V”形区夹角增大、单纱段长度变长,并以此为依据,保持其他工艺参数不变,改变粗纱与长丝间距大小,试纺Sirofil纱,通过理论分析和实验比较讨论间距大小对Sirofil成纱结构性能的影响。结果表明：随着间距增大,成纱直径系数趋小、毛羽减少、强伸性与条干均呈非单调性变化;综合考虑成纱结构和性能,在其他参数相同时,Sirofil最佳成纱间距为12～16mm。     

旋转球的动力学分析

角球在飞进中产生了两种压差阻力,在其合力的作用下,球体被迫改变了正常的轨道,从而形成了“香蕉形”的轨迹。     

利用改进的精细积分法求解转子系统的瞬态响应

针对用精细积分法求解转子系统的瞬态响应时会出现奇异矩阵的情况,对精细积分法进行改进。利用改进的精细积分法对Jeffcott转子模型系统的瞬态响应进行分析。算例与仿真实验论证了改进方法求解转子瞬态响应的可行性。     

钢板厚度对多层波纹转子阻尼器刚度特性的影响

多层波纹钢板转子阻尼器是利用干摩擦作用实现阻尼减振的转子支撑阻尼器,钢板厚度是影响阻尼器特性的主要因素.在Hertz接触分析模型基础上,对多层波纹钢板转子阻尼器的刚度特性进行了研究.给出波纹钢板阻尼器刚度的计算方法和计算实例,并通过静态实验对理论研究进行验证.研究结果表明,波纹钢板阻尼器的刚度在不同圆周方向上变化较小,钢板厚度对阻尼器刚度影响成非线性关系.图12,参8.     

抽水蓄能电站在发电工况下旋转备用效益的测算

以抽水蓄能电站各项辅助服务中最为重要一项———旋转备用辅助服务为对象,利用蒙特卡罗随机生产模拟技术,研究抽水蓄能电站最优抽水策略下发电工况下旋转备用容量的定价理论,给出了以边际成本确定旋转备用容量电价的模型方法,以及抽水蓄能电站在发电工况下旋转备用的效益模型。并结合算例说明了模型的正确性和合理性。目的在于使各厂家在发电工况下机组提供备用时能够在提供电能和提供备用之间作出合理的选择,充分体现出其容量的市场价值,使旋转备用的效益达到最优。     

变频调速技术在细纱机恒张力控制中的应用

根据纱线长度，用变频器控制细纱机主轴转速，设计了纺纱过程中纱线恒张力控制．程序按照状态变化进行设计开发．实验结果表明，该设计达到设计要求．其产品已投入批量生产．     

多支承转子-轴承系统试验台动力学设计

为实现多支承轴承-转子系统振动监测和机械设备故障诊断,设计研制了一台模拟大型旋转机械动力特性的多支承轴承-转子试验台。采用有限元算法,经过系统动力学设计计算和理论分析,表明本试验台具有良好的动力学特性和足够的稳定性裕度,能够进行多种转子动力学实验研究,具备机组机械振动监测、故障模拟、轴承负荷在线监测等多项功能。     

同步电机转子绕组接地监测灵敏度的仿真研究

对电阻平衡电桥接地监测的原理进行了详尽地论述,利用Matlab 6.5/Simulink 5.0的强大功能,对其进行灵敏度仿真分析,仿真结果与理论分析相吻合.     

Yarn Properties Prediction Based on Machine Learning Method

Although many works have been done to constructprediction models on yarn processing quality, the relationbetween spinning variables and yam properties has not beenestablished conclusively so far. Support vector machines(SVMs), based on statistical learning theory, are gainingapplications in the areas of machine learning and patternrecognition because of the high accuracy and goodgeneralization capability. This study briefly introduces theSVM regression algorithms, and presents the SVM basedsystem architecture for predicting yam properties. Model.selection which amounts to search in hyper-parameter spaceis performed for study of suitable parameters with grid-research method. Experimental results have been comparedwith those of artificial neural network(ANN) models. Theinvestigation indicates that in the small data sets and real-life production, SVM models are capable of remaining thestability of predictive accuracy, and more suitable for noisyand dynamic spinning process.     

The Yarn Curve and Tension in Rotor Spinning

The effective way of finding the yarn curve shape and tension of OE（open end） yarn inside the rotor is carried out and treated by dividing the yarn curve into two parts, from which the trajectory of the yarn sliding on the navel surface is also obtained. It is pointed out that the yarn curve shape and tension will both fluctuate with the trajectory＇s revolving cycle inside the rotor. However, the fluctuation is not too great, so the motion can still be regarded as approximately steady for dynamic analysis.