基于人工黏性的二维激波小波多尺度数值计算

基于自适应小波配点法和人工黏性，构造了二维激波问题的小波数值计算格式。利用最细尺度的小波系数构造两组激波定位函数，分别用以控制两个正交方向人工黏性的大小和分布。对三类二维激波问题进行计算和验证，结果表明：自适应小波配点法利用小波阈值滤波删除变化平缓区域大量网格点，而保留变化急剧区域的网格点，比传统方法计算效率高，且分辨率越高，计算效率更高；利用最细层小波系数构建的幂函数形式的激波定位函数能准确判断激波位置和控制X、Y方向人工黏性的大小和分布，从而捕捉不同方向和不同强度的激波。     

Modeling satellite battery aging for an operational satellite simulator

During the satellite’s operations, simulation tools perform an important role in ensuring the space mission success. In this sense, the models implemented in the context of an operational satellite simulator that enables analysis of health status and maintenance during operations shall reflect the current satellite behavior with high fidelity. Moreover, it is complicated to obtain all analytical models of a satellite’s disciplines, considering its aging. This paper proposes an Artificial Neural Network (ANN) to reproduce the battery voltage behavior of a large sun-synchronous remote sensing satellite, the CBERS-4, currently in operation. Using the genetic algorithm to find the best network architecture of ANN, the neural model for this application presented an error of less than 1%, demonstrating its feasibility to obtain a high fidelity model for an operational simulator enabling extend analyses. The paper addresses advanced techniques aligned with the space industry’s future, increasing the ability to analyze a large amount of data and improve the space system’s operation.     

Non-intrusive reduced-order model for predicting transonic flow with varying geometries

A Non-Intrusive Reduced-Order Model (NIROM) based on Proper Orthogonal Decomposition (POD) has been proposed for predicting the flow fields of transonic airfoils with geometry parameters. To provide a better reduced-order subspace to approximate the real flow field, a domain decomposition method has been used to separate the hard-to-predict regions from the full field and POD has been adopted in the regions individually. An Artificial Neural Network (ANN) has replaced the Radial Basis Function (RBF) to interpolate the coefficients of the POD modes, aiming at improving the approximation accuracy of the NIROM for non-samples. When predicting the flow fields of transonic airfoils, the proposed NIROM has demonstrated a high performance.     

A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network

There are remarkable ionospheric discrepancies between space-borne (COSMIC) measurements and ground-based (ionosonde) observations, the discrepancies could decrease the accuracies of the ionospheric model developed by multi-source data seriously. To reduce the discrepancies between two observational systems, the peak frequency (foF2) and peak height (hmF2) derived from the COSMIC and ionosonde data are used to develop the ionospheric models by an artificial neural network (ANN) method, respectively. The averaged root-mean-square errors (RMSEs) of COSPF (COSMIC peak frequency model), COSPH (COSMIC peak height model), IONOPF (Ionosonde peak frequency model) and IONOPH (Ionosonde peak height model) are 0.58 MHz, 19.59 km, 0.92 MHz and 23.40 km, respectively. The results indicate that the discrepancies between these models are dependent on universal time, geographic latitude and seasons. The peak frequencies measured by COSMIC are generally larger than ionosonde’s observations in the nighttime or middle-latitudes with the amplitude of lower than 25%, while the averaged peak height derived from COSMIC is smaller than ionosonde’s data in the polar regions. The differences between ANN-based maps and references show that the discrepancies between two ionospheric detecting techniques are proportional to the intensity of solar radiation. Besides, a new method based on the ANN technique is proposed to reduce the discrepancies for improving ionospheric models developed by multiple measurements, the results indicate that the RMSEs of ANN models optimized by the method are 14–25% lower than the models without the application of the method. Furthermore, the ionospheric model built by the multiple measurements with the application of the method is more powerful in capturing the ionospheric dynamic physics features, such as equatorial ionization, Weddell Sea, mid-latitude summer nighttime and winter anomalies. In conclusion, the new method is significant in improving the accuracy and physical characteristics of an ionospheric model based on multi-source observations.     

LC9专用挤压型材应用过时效试验

试验并分析了ＬＣ９合金专用挤压型材淬火并人工时效和淬火并人工过时效状态对其电导率、常规力学性能、抗疲劳性能、抗腐蚀性能的影响。另外,在减少淬变形上也做了一些试验。试验结果表明,ＬＣ９合金在过时效状态下有较好的抗腐蚀性能和抗疲劳性能。淬火变形也大大减小。     

内齿轮传动啮合齿面稳态温度场分析

以2K-V行星传动机构中齿轮为研究对象,以齿轮啮合原理、传热学等为理论依据,分析了齿轮在不同啮合位置的相对滑动速度和齿面的接触应力的变化规律。精确计算了稳态温度场边界条件,包括齿轮一个啮合周期内的平均摩擦热流量和不同齿面的对流换热系数。建立单齿三维模型并导入ANSYS Workbench进行温度场的有限元分析。通过将啮合齿面划分成多个沿齿宽方向的条形区域来实现不同啮合位置摩擦热流量的加载,得到齿轮稳态温度场分布,并分析了条形区域的数量对温度场的影响。结果表明,当条形区域数量多于50时,温度场基本趋于稳定。     

基于图像识别技术的火箭飞行时序判读

在运载火箭开展射前综合测试时,控制系统在预先指定的飞行时序向末端发送一系列指令,指示末端设备做出相应动作,以达成对应的目标。这些动作主要依赖火工品完成,这些动作变化会体现在火工品等效器指示灯上。当前控制系统无法检测到末端等效器的电流情况变化,因此无法对末端动作进行有效监测,测试缺乏可靠性、准确性与可追溯性的问题亟需改善。通过综合运用人工智能、数字图像处理与计算机视觉技术,经由特征匹配、指示灯定位、指示灯状态建模等步骤,可对末端火工品等效器状态变化实现精准监测,进而实现飞行时序自动判读。与标准判据比对后,本文提出的火箭飞行时序判读系统准确率可达98.89%。     

成像卫星在轨智能处理技术研究进展

从成像卫星在轨智能处理技术的重要性出发,分析了现阶段国内外研究现状,并总结了星上计算资源受限、实时性要求高、缺少大规模数据集、空间环境特殊性等在轨智能面临的主要挑战.针对问题,系统总结了智能算法、硬件平台和优化技术三个方面的相关技术,其中对于遥感目标智能检测算法,详细描述了数据集、模型架构、实时性等多个方面.最后,对成...     

增雨防雹火箭作业系统设计及试验

基于人工影响天气的基本原理，阐述了人工增雨防雹概念及其对火箭作业系统的技术要求，介绍了ＷＲ－１Ｂ增雨防雹火箭作业系统和试验作业的设计。     

对转螺旋桨/桨扇差动行星齿轮机构动力学设计方法及分析

在对转桨扇涡桨推进系统专有的外流对转桨推进器-对转减速齿轮机构-涡桨发动机的总体匹配设计问题中,给出了拖动共轴对转螺旋桨/桨扇类气动负荷的差动行星齿轮机构的一种设计与分析方法.不同于常规,该总体设计方法以机构的动力学参数为输入,诸传动比、诸半径、诸稳态传动受力等为输出,通过方程组得到了该类传动机构诸参数之间的联系.讨论了该类传动机构的设计原则,使用形式,适用的航空原动机类型.通过某当代算例给出了桨和传动机构诸参数的变化区间,变化特点,参数重要性的分析.结果表明:该方法可以快速得到一般总体设计问题中该类齿轮机构所有稳态设计参数的可行解区间与优化解,进而还可以为推进系统部件法数模的非设计点特性模拟提供计算方法.