天线组阵中相位差的递推最小二乘估计与滤波

相位差的估计精度是影响深空网天线组阵中信号合成性能的关键因素。利用天线组阵S UMPLE或SIMPLE相关算法中相位差估计值之间的几何关系,提出了相位差变化率的最小二乘 估计,并在此基础上提出了相位差的递推最小二乘滤波方法。经过理论分析和仿真实验表明 ,所提出的方法能够大大提高相位差的估计精度;对于给定的例子,估计精度提高了接近两 个数量级。相位差经滤波后,天线阵的信号合成性能显著增强,从而大大提高对深空通信中 微弱信号的数据接收能力。
     

基于工程和数值方法的导弹气动特性计算

导弹气动特性对导弹设计及使用具有重大意义,气动特性的确定是弹道计算、控制参数的选择和结构强度设计的原始依据,其优劣直接影响导弹的飞行性能。针对某型导弹风洞模型,采用一般工程计算方法及FLUENT软件,分别从工程与数值两个方面对其在亚声速条件下的气动特性进行研究,然后将计算所得的主要气动力系数与风洞实验结果进行对比,从而对不同计算方法的精度进行评价。结果表明,二者所计算的气动参数在一定范围内满足工程设计的精度要求,能较为准确地反映导弹的气动特性。     

A fully automated framework for helicopter rotor blades design and analysis including aerodynamics,structure, and manufacturing

This study describes an integrated framework in which basic aerospace engineering aspects (performance, aerodynamics, and structure) and practical aspects (configuration visualiza-tion and manufacturing) are coupled and considered in one fully automated design optimization of rotor blades. A number of codes are developed to robustly perform estimation of helicopter config-uration from sizing, performance analysis, trim analysis, to rotor blades configuration representa-tion. These codes are then integrated with a two-dimensional airfoil analysis tool to fully design rotor blades configuration including rotor planform and airfoil shape for optimal aerodynamics in both hover and forward flights. A modular structure design methodology is developed for real-istic composite rotor blades with a sophisticated cross-sectional geometry. A D-spar cross-sectional structure is chosen as a baseline. The framework is able to analyze all realistic inner configurations including thicknesses of D-spar, skin, web, number and ply angles of layers of each composite part, and materials. A number of codes and commercial software (ANSYS, Gridgen, VABS, PreVABS, etc.) are implemented to automate the structural analysis from aerodynamic data processing to sec-tional properties and stress analysis. An integrated model for manufacturing cost estimation of composite rotor blades developed at the Aerodynamic Analysis and Design Laboratory (AADL), Aerospace Information Engineering Department, Konkuk University is integrated into the framework to provide a rapid and dynamic feedback to configuration design. The integration of three modules has constructed a framework where the size of a helicopter, aerodynamic performance analysis, structure analysis, and manufacturing cost estimation could be quickly investigated. All aspects of a rotor blade including planform, airfoil shape, and inner structure are considered in a multidisciplinary design optimization without an exception of critical configuration.     

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life (RUL) estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine (RVM) is a data-driven algorithm used to estimate a battery’s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery’s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.     

基于改进Cycle-GAN的光流无监督估计方法CSCD

卷积神经网络为光流的计算提供了一种新的方式,但作为一种数据驱动技术,用于训练网络的大规模光流真值在现实世界中不易获取。为了解决这个弊端,基于Cycle-GAN的循环对抗机制,提出了一种光流无监督估计方法。首先,引入双判别器机制在生成器生成的光流样本的底层和高层特征上进行鉴别,迫使生成器提高光流生成的精度。其次,引入Spynet作为教师网络,在生成器训练前期对其进行指导,防止网络陷入模式崩塌。最后,改进损失函数,提出了光流一致性损失和轮廓一致性损失函数,进一步提升光流估计精度。实验结果表明,与现有的先进算法相比,提出的方法达到与有监督算法相同的精度水平。     

无失效数据下液体火箭发动机的E-Bayes可靠性分析

为了解决航空航天产品、造价昂贵的产品及高可靠性产品的可靠性评估,本文在无失效数据情况下,对液体火箭发动机进行了可靠性分析.应用E-Bayes(Expected-Bayesian)估计法,在有关研究的基础上,针对液体火箭发动机热试车过程中可能出现的无失效数据情况,确定了失效概率的先验分布,并给出了E-Bayes估计和多层...     

高超声速飞行器气动力/热参数辨识研究综述

飞行器气动力和气动热参数辨识是高超声速飞行器设计的关键技术之一。笔者对高超声速飞行器的气动力和气动热参数辨识技术进行了综述。介绍了飞行器气动力、气动热参数辨识的基本原理与主要方法,气动力、气动热参数辨识技术在高超声速飞行器研发中的应用情况与发展趋势。同时也简要介绍了中国空气动力研究与发展中心（CARDC）在气动力和气动热参数辨识研究方面的研究概况。     

表面热流辨识技术在边界层转捩位置测量中的应用初步研究

目前工程飞行试验中主要采用近壁热电偶测温法来确定飞行器表面流动的边界层转捩位置,由于测点离表面较近,对温度传感器量程和结构强度有较高要求。为此,提出了基于表面热流辨识技术确定转捩位置的基本思想和处理方法,测点可以距离表面相对较远。但是,当测点越远离受热面,辨识问题的不适定性会越强,因此需要采用仿真辨识方法来对传感器安装位置进行合理选取。在给出二维传热模型表面热流辨识算法的基础上,对两个算例进行了仿真辨识分析。结果表明：基于表面热流辨识技术确定转捩位置是可行的,能给出较为准确的转捩区域判断。     

飞机结构振动疲劳分析研究进展

结构的振动疲劳分析研究对飞行器设计及其可靠性具有重要意义。全面阐述了国内外在结构振动疲劳研究方面的新进展,特别对随机振动载荷下结构的疲劳寿命估算作了详细的评述与讨论,综合最新的研究方法和取得的成果,对一些新出现的研究方向,比如材料的振动疲劳寿命曲线、飞机结构抗振动疲劳设计等进行了阐述,最后指出了结构振动疲劳分析尚待进一步研究的问题。     

大中型民用飞机起飞总重估算方法研究

推导了基于发动机推力估算起飞总重和基于旅客座位数估算起飞总重的公式。通过多种大中型喷气式民用客机相关参数的统计值,建立起飞总重对相关参数的多元回归分析模型,通过显著性检验,说明模型是有效的,并据此建立了新的起飞总重估算公式。通过算例结果分析表明,新的起飞总重估算公式具有很高精度。在新机的方案设计阶段若能给定此公式所需的相关参数,可以计算得到相对合理的设计起飞总重值。