我国GEO航天器总体综合优化技术研究进展

总体综合优化设计是航天器研制持续追求的目标,多学科集成设计是有效途径之一。围绕航天器总体综合优化设计,总结了近年来我国在地球静止轨道(GEO)航天器系统与学科建模、多学科集成优化设计策略、寻优算法、软件工具开发及工程应用实施等方面研究进展,针对我国GEO航天器发展新需求以及现阶段综合优化设计技术发展的不足,结合系统工程技术发展新趋势,指出航天器综合优化设计后续应重点加强集成建模、工程化应用研究,为高轨航天器总体优化设计技术发展提供指导。     

舰载无人机拦阻着舰中机身冲击响应分析

针对某舰载无人机拦阻着舰过程中的机体强度问题,以其中机身结构为主要研究对象,首次设计了包括中机身结构与前后机身、机翼假件以及拦阻钩等构件的地面拦阻模拟试验方案,并搭建了相应装置,采用地面试验和刚柔耦合仿真模拟2种方法,对拦阻着舰过程中拦阻力冲击下中机身结构的动态响应特性进行了全面分析。试验与仿真结果表明：中机身最大航向过载沿两条主传力路径自后机身到前机身方向衰减,下传递路径点的过载峰值明显大于上传递路径点的峰值;发现最大过载点位于拦阻接头处,应变危险点位于机腹梁前段处;中机身结构上各测点的试验和仿真过载误差均在5%以内,应变误差均在8%以内,验证了试验结果的有效性和刚柔耦合数值仿真方法的可行性。地面拦阻试验及数值仿真的联合分析可为舰载无人机机身结构强度设计提供重要参考,并为后续舰载无人机的拦阻着舰分析以及机身结构响应预测提供依据。     

基于闭环电流平均值法的永磁同步电机谐波抑制方法

由于逆变器的非线性及永磁同步电机(PMSM)的气隙磁场畸变,在运行过程中PMSM定子电流中含有大量的谐波分量,导致电流波形发生畸变,从而引起较大的转矩脉动。针对传统谐波抑制方法采用低通滤波器(LPF)提取5、7次谐波分量,动态响应时间长,稳态误差大的问题,提出了闭环电流平均值法提取谐波分量。仿真结果表明,闭环电流平均值法可以提高系统的快速性和稳定性。将电流平均值法应用于PMSM谐波抑制系统,仿真结果表明:基于闭环电流平均值法的谐波电压注入法可以降低电流波形的畸变率及5、7次谐波含量。     

Z源NPC三电平变换器恒功率并网控制策略研究

Z源中点钳位式(NPC)三电平变换器把逆变和升压两种环节结合在一起,结构简单、工作效率高,但是针对这种结构输出的无功功率研究较少。提出了一种Z源NPC三电平变换器的恒功率并网控制策略。首先对这种变换器的结构及原理进行分析;再把恒功率控制引入到这种变换器的并网控制中,并采用空间矢量脉宽调制(SVPWM)实现了变换器的并网控制。MATLAB/Simulink软件仿真结果证明了这种变换器能够提高电压升压比。该控制策略可使变换器输出的有功功率、无功功率稳定于设定值,且并网电流谐波较低。     

Online condition diagnosis for a two-stage gearbox machinery of an aerospace utilization system using an ensemble multi-fault features indexing approach

China manned space station is designed to operate for over ten years. Long-term and sustainable research on space science and technology will be conducted during its operation. The application payloads must meet the ‘‘long life and high reliability" mission requirement. Gearbox machinery is one of the essential devices in an aerospace utilization system, failure of which may lead to downtime loss even during some disastrous catastrophes. A fault diagnosis of gearbox has attracted attentions for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. A novel fault diagnosis method based on the Ensemble Multi-Fault Features Indexing(EMFFI) approach is proposed for the condition monitoring of gearboxes. Different from traditional methods of signal analysis in the one-dimensional space, this study employs a supervised learning method to determine the faults of a gearbox in a two-dimensional space using the classification model established by training the features extracted automatically from diagnostic vibration signals captured. The proposed method mainly includes the following steps. First, the vibration signals are transformed into a bi-spectrum contour map utilizing bi-spectrum technology,which provides a basis for the following image-based feature extraction. Then, Speeded-Up Robustness Feature(SURF) is applied to automatically extract the image feature points of the bi-spectrum contour map using a multi-fault features indexing theory, and the feature dimension is reduced by Linear Discriminant Analysis(LDA). Finally, Random Forest(RF) is introduced to identify the fault types of the gearbox. The test results verify that the proposed method based on the multi-fault features indexing approach achieves the target of high diagnostic accuracy and can serve as a highly effective technique to discover faults in a gearbox machinery such as a two-stage one.     

Thermoelastohydrodynamic analysis of misaligned bearings with texture on journal surface under high-speed and heavy-load conditions

Hydrodynamic journal bearings are used to support the planetary gears in the transmission system of a Geared Turbo Fan engine, and they operate under severe working conditions, such as misalignment, high speed, and heavy load. Surface texture is regarded as an effective approach to improve mechanical properties under severe working conditions. However, the effects of surface texture on misaligned journal bearings under severe conditions have not been well studied. Therefore, a thermoelastohydrodynamic(TEHD) lubricating model with a textured and misaligned journal is presented in this study. Solid deformation, heat transfer between oil and the bearing, and heat convection between air and the bearing are considered in the model. The effects of cavitation and viscosity-temperature are also taken into account. The impacts of surface texture parameters and the misalignment angle on lubricating properties are investigated in the model. Results indicate that the bearing lubricating performance is greatly improved by optimal surface texture considering journal misalignment. The maximum oil film pressure and load-carrying capacity of a journal bearing increase with journal misalignment. Meanwhile, the oil film temperature increases sharply while considering misalignment.     

A new error-controllable method for smoothing the G01 commands

In real machining, the tool paths are composed of a series of short line segments, which constitute groups of sharp corners correspondingly leading to geometry discontinuity in tangent. As a result, high acceleration with high fluctuation usually occurs. If these kinds of tool paths are directly used for machining, the feedrate and quality will be greatly reduced. Thus, generating continuous tool paths is strongly desired. This paper presents a new error-controllable method for generating continuous tool path. Different from the traditional method focusing on fitting the cutter locations, the proposed method realizes globally smoothing the tool path in an error-controllable way. Concretely, it does the smoothing by approaching the newly produced curve to the linear tool path by taking the tolerance requirement as a constraint. That is, the error between the desired tool path and the G01 commands are taken as a boundary condition to ensure the finally smoothed curve being within the given tolerance. Besides, to improve the smoothing ability in case of small corner angle, an improved local smoothing method is also proposed by symmetrically assigning the control points to the two adjacent linear segments with the constrains of tolerance and G3 continuity. Experiments on an open five-axis machine are developed to verify the advantages of the proposed methods.     

Aircraft engine fault detection based on grouped convolutional denoising autoencoders

Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System (ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low.     

A survey on numerical simulations of drag and heat reduction mechanism in supersonic/hypersonic flows

Along with the survey on experimental investigations drawing attention to the drag and heat reduction mechanism, the authors simultaneously focus on the recent advances of numerical simulations on the schemes applied to supersonic/hypersonic vehicles. The CFD study has evolved as an irreplaceable method in scheme evaluation and aircraft optimization. Similar to our previous experimental survey, the advances in drag and heat reduction schemes are reviewed by similar kinds of mechanism in this article, namely the forward-facing cavity, the opposing jet, the aerospike, the energy deposition and their combinational configurations. This review article puts an emphatic eye on the flow conditions, numerical methods, novel schemes and analytical conclusions given in the simulations. Further, the multi-objective design optimization concept has also been illustrated due to the observable advantages of using CFD over experimental method, especially those performances conducted in drag reduction and thermal protection practice, and this would possess reference value in the design of aircraft system.     

超声速涡轮叶型全局气动优化设计

针对涡轮叶型全局优化设计计算时间长、样本空间大等难点提出一种可行的优化设计方法,该方法将控制叶型的17个参数作为优化变量,采用第二代多目标遗传算法进行全局自动寻优。基于此方法,搭建了涡轮叶型全局优化设计平台。利用此平台,分别采用轴向稠度固定和自由优化两种方式对超声速涡轮叶型进行了优化设计。数值计算结果表明,两组优化设计叶型在设计工况下总压损失系数比参考叶型分别低19.5%和10.0%,流道中的激波强度更弱,且在变工况条件下都具有较好的气动性能。深入分析流场与激波结构后发现,外尾激波相比于内尾激波对总损失的影响更大,通过减小气流膨胀转折角或内尾激波气流转折角能够有效削弱外尾激波强度。