涡轴发动机动力涡轮模拟轴动力特性研究

应用有限元方法完成了某新型涡轴发动机动力涡轮模拟实心轴动力特性-l临界转速、振型和不平衡响应的计算，分析了平衡卡箍对模拟轴动力特性的影响。最后进行了试验验证。计算和试验结果均表明：平衡卡箍对模拟轴临界转速和不平衡响应的影响甚微，对振型几乎无影响，是平衡细长高速柔性转子的理想平衡工装。     

横向裂纹对转子动力特性的影响

选取转子支承系统为研究对象，利用传递矩阵方法计算转子系统的动力特性，目的在于探求裂纹对转子动力特性的影响。计算结果表明：裂纹对转子的各种动力特性都有影响，但影响很小。这对于发动机的正常工作是非常有利的。但从故障检测方面考虑，若想通过振动特性的变化来检测裂纹故障是相当困难的。     

磁悬浮轴承转子系统动态特性的实验研究

通过激振实验获得磁悬浮轴承转子系统的动态特性参数,并与实际运行状态进行比较。结果表明,激振实验能够获得系统在高速旋转状态下的稳定性、临界转速及其与控制参数间的关系。另一方面,激振实验结果表明,某些临界转速值对控制参数的灵敏度很小,无法通过改变控制参数,改变这些临界转速值。      

高温变形参数对TC6钛合金微观组织的影响研究

在热模拟实验和金相实验的基础上,研究了变形参数(变形温度、应变速率、变形程度)对TC6钛合金微观组织的影响。研究结果表明:变形温度对TC6钛合金的变形组织有着显著影响。在两相区,随着变形温度的升高,组织中初生相的含量在减少,而相晶粒的尺寸有先增大后减小的趋势。应变速率对TC6钛合金变形组织中初生相的形态和尺寸有较大影响。较大的应变速率能促进变形时的动态再结晶,有利于晶粒的细化。变形程度存在着一临界值,当超过这一临界值后,变形程度的增加有利于晶粒的细化。     

一种变应变速率组合的TiAl合金细晶方法探讨

提出了一种先低后高的变应变速率组合等温压缩细晶方法,通过两次连续变形,得到70%以上变形量而不产生裂纹,获得了良好的细晶效果。流变应力曲线出现明显的动态软化现象,其机制为动态再结晶;在采用低应变速率进行第一次变形后,由于动态再结晶的发生,使第二次高应变速率变形时,应力降低并且可以获得较大变形量,晶粒细化充分;在两次变形之间增加保温处理,可以使细小动态再结晶晶粒得到静态球化,使细晶程度提高。     

涡轴发动机动力涡轮转子动力特性研究

应用有限元方法建立了某新型涡轴发动机动力涡轮转子动力特性的计算模型,对转子在不同情况下的动力特性——临界转速、振型和不平衡响应分别进行了系统的计算分析,得到了转子的前5阶临界转速、前5阶主振型和前两阶不平衡响应曲线,并计算了平衡卡箍对转子动力特性的影响。试验表明:计算模型能真实反映转子的动力特性。研究结果为转子的后续高速动平衡试验提供了参考和依据。      

一种估算测扭基准轴不平衡量的试验方法

对涡轴发动机动力涡轮转子测扭基准轴的不平衡量进行了研究,提出了一种通过高速动平衡试验估算出测扭基准轴不平衡量的试验方法.结果表明测扭基准轴的不平衡量虽然不是转子高速动平衡的决定性因素,但影响到传动轴的动平衡.因此,控制测扭基准轴的不平衡量仍然是有必要的.最终提出一些合理的建议.     

基于模糊奇偶方程的非线性系统传感器故障诊断

 给出了一种非线性系统传感器的故障诊断方法。该方法将T-S 模糊模型、全解耦奇偶方程和参数估计相结合,同时对非线性系统的多个传感器的故障进行检测、隔离与识别。设计出用于产生残差的线性系统全解耦奇偶方程,并给出了全解耦奇偶向量的存在条件,全解耦奇偶方程产生的残差仅对一个传感器故障敏感,而对系统状态、扰动输入和其它传感器输出解耦。引入T-S 模型将全解耦奇偶方程推广到非线性系统中得到了模糊奇偶方程。传感器的故障模型表示为刻度因子和偏差的形式,根据残差信息应用卡尔曼估计方法可识别出故障模型的参数。最后给出了某型号飞机控制系统传感器的故障诊断仿真实例。     

直升机旋翼/机体耦合非线性系统的动稳定性分析

为了准确反映直升机旋翼／机体耦合系统的动稳定性，建立了旋翼／机体耦合非线性动力学微分方程，在时域内求解微分方程得到各片桨叶的挥舞、摆振及机体的响应用以对系统进行数值模拟；为了获得系统稳定性的定量值，用快速傅立叶变换(FFT)确定模态频率，用基于傅立叶级数的移动矩形窗方法得到模态阻尼。地面共振分析表明，时域分析与特征值分析结果具有良好的相关性，并与试验值吻合，从而验证了该方法的有效性。大总距时，用时域分析得到的模态阻尼与试验值吻合得更好，该方法可用于具有非线性减摆器的直升机旋翼／机体耦合系统的动稳定性分析。     

Optimal assimilation for ionospheric weather – Theoretical aspect

Observation, specification and prediction of ionospheric weather are the key scientific pursuits of space physicists, which largely based on an optimal assimilation system. The optimal assimilation system, or commonly called data assimilation system, consists of dynamic process, observation system and optimal estimation procedure. We attempt to give a complete framework in this paper under which the data assimilation procedure carries through. We discuss some crucial issues of data assimilation as follows: modeling a dynamic system for ionospheric weather; state estimation for static or steady system in sense of optimization and likelihood; state and its uncertainty estimation for dynamic process. Meanwhile we also discuss briefly the observability of an observation system; system parameter identification. Some data assimilation procedures existed at present are reviewed in the framework of this paper. As an example, a second order dynamic system is discussed in more detail to illustrate the specific optimal assimilation procedure, ranging from modeling the system, state and its uncertainty calculation, to the quantitatively integration of dynamic law, measurement to significantly reduce the estimation error. The analysis shows that the optimal assimilation model, with mathematical core of optimal estimation, differs from the theoretical, empirical and semi-empirical models in assimilating measured data, being constrained by physical law and being optimized respectively. The data assimilation technique, due to its optimization and integration feature, could obtain better accurate results than those obtained by dynamic process, measurement or their statistical analysis alone. The model based on optimal assimilation meets well with the criterion of the model or algorithm assessment by ‘space weather metrics’. More attention for optimal assimilation procedure creation should be paid to transition matrix finding, which is usually not easy for practical space weather system. High performance computing hardware and software studies should be promoted further so as to meet the requirement of large storage and extensive computation in the optimal estimation. The discussion in this paper is appropriate for the static or steady state or transition process of dynamic system. Many phenomena in space environment are unstable and chaos. So space environment study should include and integrate these two branches of learning. This revised version was published online in August 2006 with corrections to the Cover Date.