滑移边界条件对二维微喷管数值模拟结果的影响

采用N-S方程对微型缩放喷管内的流场进行二维数值模拟。引入滑移边界条件，通过比较有、无滑移边界条件的数值结果，发现微喷管内Kn小于0．0054时，不同壁面边界条件对流场和微喷管性能参数都没有太大影响；当Kn在0．0054～0．025时，滑移边界条件对流场的模拟已经有较大的影响，但是对预测微喷管的性能影响并不大，在3％以内；当Kn在0．025-0．045时，滑移边界条件不仅对流场的影响已经较明显，而且对微喷管性能预测也．影响较大。     

几乎最佳自相关序列偶搜索算法的研究

几乎最佳自相关序列偶是一种新的最佳信号,但其一般的搜索方法——穷举法,搜索量太大。根据几乎最佳自相关序列偶的必要条件,提出一种新的搜索方法,给出算法流程图,并进行了实验验证。实验结果表明,算法可大大减小搜索量,比穷举法效果好且耗时短。     

基于数字仿真的再入飞行器滚转异常边界条件研究

提出了一种基于数字仿真的再入弹头滚转异常边界条件的确定方法。即：首先给出可精确描述考虑各种气动力（矩）系数、各种干扰源作用时的六自由度运动方程。针对各种干扰源及其组合作用，对某弹头的滚转异常现象作了大量的数字仿真。文中采用一种拟合方法，妥善处理了这些仿真结果，拟合出该弹头滚转异常边界条件的近似解析式     

采样计算测量中的采样条件及混迭引起的测量误差的研究

分析了信号参数的测量与信号恢复的区别,讨论了用采样计算方法测量周期重复信号时的采样条件,定量地研究了采样不满足采样条件时,混迭引起的测量误差的大小及其性质,并给出了减小误差的均匀内插采样和随机采样方法。     

一种基于条件熵的航电设备故障诊断方法

针对目前航电设备故障诊断能力相对较弱的现状，提出一种将粗糙集中的条件熵算法应用于故障诊断的方法，并将该方法应用于实例．结果表明诊断结果准确简化，有助于提高装备可靠性和维修性。     

Boundary condition modelling and identification for cantilever-like structures using natural frequencies

The actual boundary conditions of cantilever-like structures might be non-ideally clamped in engineering practice, and they can also vary with time due to damage or aging. Precise modelling of boundary conditions, in which both the boundary stiffness and the boundary mass should be modelled correctly, might be one of the most significant aspects in dynamic analysis and testing for such structures. However, only the boundary stiffness was considered in the most existing methods. In this paper, a boundary condition modelling and identification method for cantilever-like structures is proposed to precisely model both the boundary stiffness and the boundary mass using sensitivity analysis of natural frequencies. The boundary conditions of a cantilever-like structure can be parameterized by constant mass, constant rotational inertia,constant translational stiffness, and constant rotational stiffness. The relationship between natural frequencies and boundary parameters is deduced according to the vibration equation for the lateral vibration of a non-uniform beam. Then, an iterative identification formulation is established using the sensitivity analysis of natural frequencies with respect to the boundary parameters. The regularization technique is also used to solve the potential ill-posed problem in the identification procedure.Numerical simulations and experiments are performed to validate the feasibility and accuracy of the proposed method. Results show that the proposed method can be utilized to precisely model the boundary parameters of a cantilever-like structure.     

具有周期性薄膜热源的多层介质非稳态传热数值仿真

为分析具有薄膜热源的多层介质非稳态传热问题,以某型飞机层合电加温风挡为例,构建了一种热流密度-温度边界条件,实现了薄膜热源热流和温度属性向多层介质内外两个方向上的实时传递,再现了薄膜热源温度对临近多层介质表面温度的影响,为具有薄膜热源的多层介质传热问题分析和加温控制规律设计提供了一种可行方法.     

A data-driven health indicator extraction method for aircraft air conditioning system health monitoring

Prognostics and Health Management (PHM) has become a very important tool in modern commercial aircraft. Considering limited built-in sensing devices on the legacy aircraft model, one of the challenges for airborne system health monitoring is to find an appropriate health indicator that is highly related to the actual degradation state of the system. This paper proposed a novel health indicator extraction method based on the available sensor parameters for the health monitoring of Air Conditioning System (ACS) of a legacy commercial aircraft model. Firstly, a specific Airplane Condition Monitoring System (ACMS) report for ACS health monitoring is defined. Then a non-parametric modeling technique is adopted to calculate the health indicator based on the raw ACMS report data. The proposed method is validated on a single-aisle commercial aircraft widely used for short and medium-haul routes, using more than 6000 ACMS reports collected from a fleet of aircraft during one year. The case study result shows that the proposed health indicator can effectively characterize the degradation state of the ACS, which can provide valuable information for proactive maintenance plan in advance.     

复合材料钻削刀具状态集成学习分类技术

刀具状态监测关系到工件加工质量,因此实时掌握刀具的磨损状态具有重要的意义。针对单一模式识别分类器的局限性,提出了基于异态集成学习模型的刀具状态监测系统。在该系统中,根据集成学习基分类器选择原则,选取了支持向量机(SVM)、隐马尔科夫模型(HMM)以及径向基神经网络(RBF)这3个单分类器作为基分类器。为了验证监测系统的有效性,进行了复合材料钻削实验,提取了加工过程中的钻削力信号、振动信号的时域特征并利用局部保持法(LPP)进行了特征选择。通过与单分类器和集成学习分类效果的对比,表明了集成学习模型相比于单一基分类器具有更高的分类精度和更好的稳定性。     

基于数据挖掘的航空发动机状态参数离群点分析

利用机载数据采集系统和传感器获得的发动机状态参数往往存在粗大误差，影响故障诊断的准确性。采用数据挖掘算法中基于统计学的离群点分析，并与厂家系统的分析结果相比较，结果表明，该方法能够有效地剔除粗大误差且不会误删有用数据。