基于CFD/CSD耦合的叶轮机叶片失速颤振计算

为了研究叶轮机叶片的失速颤振特性,发展了一种计算流体力学与计算结构力学(CFD/CSD)时域耦合方法。该方法通过每一物理时刻CFD和CSD的循环迭代实现了耦合计算。在CFD分析中,采用鲁棒性较好的空间离散格式AUSM+-UP,并基于延迟脱体涡模型(DDES)模拟了带分离流动。在结构分析中,通过模态法构建了旋转叶片动力学方程并运用杂交多步方法进行求解。以孤立转子Rotor37为例,计算了不同工况下流场总体与细节参数,与实验结果的对比验证了CFD算法的精度。对某转子叶片进行了颤振特性研究,计算所得的广义位移时间响应曲线表明该叶片在近失速工况下会发生失速颤振,其表现形式为一阶弯曲模态发散且各阶模态之间不耦合。分析表明,流场不稳定和非定常效应是引起失速颤振的关键因素,同时折合频率的降低也会导致原本气动弹性稳定的叶片发生失速颤振。     

波音777飞机ACARS系统及马航MH370航班失联事件分析

马来西亚航空公司MH370客机失联事件一度引发媒体与公众的诸多猜测,猜测的焦点之一就是执飞的波音777飞机的飞机通信寻址与报告系统(Aircraft Communication Addressing and Reporting System,简称ACARS)功能是如何被关闭的。从专业角度出发,首先采用Sys ML建模的方式描述了波音777飞机ACARS系统的详细架构和人机接口,并据此深入分析了马航MH370航班失联事件中ACARS被关闭的情景。最后,提出了此次事件暴露出的现代民用飞机设计中存在的问题以及改进方法。     

End-to-End Modeling of the Solar Terrestrial System

Traditionally modeling for space science has concentrated on developing simulations for individual components of the solar terrestrial system. In reality these regions are coupled together. This coupling can be as simple as the driving of the magnetosphere – ionosphere – thermosphere system by the solar wind or as a complicated as the feedback of the ionospheric conductivity and currents on the magnetosphere. As part of the CISM project we are beginning a concentrated effort to compressively model the entire system. This approach includes chains of models. In the first chain physics based numerical models are utilized while in the second chain empirical models are coupled together. The first half of this paper discusses the numerical modeling approach by highlighting the coupling of pairs of regions within the system. In the second section we present results from empirical models which are combined to make long term forecasts of conditions in the geospace environment. It is expected that a validated and reliable forecast model for space weather can be obtained by combining the strongest elements of each chain.     

基于液压自然爆膜的压力传感器动态标定方法

介绍了一个基于液压自然爆膜的压力传感器动态校准系统，研究了标定实验数据的处理方法，分析了该系统标定结果的重复性。试验结果表明该标定系统重复性好，可以满足0～30MPa，0～50kHz压力传感器动态标定的要求。     

模拟中性大气边界层中污染物传输与扩散的动力相似讨论

文章从湍流雷诺数相等出发,推导出风洞中模拟中性大气边界层动力相似准则一充分发展的湍流中,运动相似的流动动力也相似。文章还讨论了模拟烟流抬升和扩散的相似准则,概述了国内外一些主要实验室应用的近似模拟技术。     

Simulating planetary wave propagation to the upper atmosphere during stratospheric warming events at different mountain wave scenarios

Parameterization schemes of atmospheric normal modes (NMs) and orographic gravity waves (OGWs) have been implemented into the mechanistic Middle and Upper Atmosphere Model (MUAM) simulating atmospheric general circulation. Based on the 12-members ensemble of runs with the MUAM, a composite of the stratospheric warming (SW) has been constructed using the UK Met Office data as the lower boundary conditions. The simulation results show that OGW amplitudes increase at altitudes above 30 km in the Northern Hemisphere after the SW event. At altitudes of about 50 km, OGWs have largest amplitudes over North American and European mountain systems before and during the composite SW, and over Himalayas after the SW. Simulations demonstrate substantial (up to 50–70%) variations of amplitudes of stationary planetary waves (PWs) during and after the SW in the mesosphere-lower thermosphere of the Northern Hemisphere. Westward travelling NMs have amplitude maxima not only in the Northern, but also in the Southern Hemisphere, where these modes have waveguides in the middle and upper atmosphere. Simulated variations of PW and NM amplitudes correspond to changes in the mean zonal wind, EP-fluxes and wave refractive index at different phases of the composite SW events. Inclusion of the parameterization of OGW effects leads to decreases in amplitudes (up to 15%) of almost all SPWs before and after the SW event and their increase (up to 40–60%) after the SW in the stratosphere and mesosphere at middle and high northern latitudes. It is suggested that observed changes in NM amplitudes in the Southern Hemisphere during SW could be caused by divergence of increased southward EP-flux. This EP-flux increases due to OGW drag before SW and extends into the Southern Hemisphere.     

云制造环境下机床装备资源选择方法

针对云制造环境下机床装备资源具有数量大、异质异构、服务制约因素多等问题，提出了基于多准则决策的机床装备资源选择方法。首先，利用本体论对机床装备资源建模，将物理资源虚拟化封装，实现虚拟资源与物理资源的逻辑映射。进一步地，使用语言变量量化决策者评估信息，采用模糊决策与试验评价实验室（FDEMATEL）和熵值法（EW）相结合的组合赋权法，引入模糊VIKOR（FVIKOR）多属性决策方法对机床装备资源进行综合评估，建立FDEMATEL-EW-FVIKOR的多准则混合决策模型。最后，将该方法应用于某生产制造企业，实验对比决策结果验证了多准则混合决策模型的可行性和有效性。     

面向空间天线的TWF复合材料性能研究

利用碳纤维三向编织物（triaxial woven fabric，TWF）作为增强材料，与柔性基体材料硅橡胶复合，制成兼具一定柔性和刚性的复合材料壳膜结构，用于卫星天线反射器，是一种新型的高精度可展开天线实现方案。采用复合材料细观方法，对碳纤维增强硅橡胶TWF复合材料进行了力学性能的研究。首先由纤维和基体的材料特性得到均质纤维束的材料特性；接下来考虑纤维束交织情况（起伏波动）建立由纤维束组成的单胞实体有限元模型；然后对单胞实体有限元模型进行均匀化分析，施加周期性边界条件，最终得到等效的均质材料特性；为满足天线反射器高性能需求，分析了碳纤维种类、纤维体积分数对单胞等效性能的影响规律。对该材料力学性能的研究，可以为其未来应用于大型高精度可展开天线反射器提供一定的理论依据。     

Modeling irregular small bodies gravity field via extreme learning machines and Bayesian optimization

Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface.     

基于 Kriging与多点取样算法的压气机特性建模

针对航空燃气轮机流量系数插值建模过程中,获取样本数据时的实验成本较高以及插值模型精度偏低这 2个问题,根据样本点在设计空间中的分布与模型精度的关系,在 Kriging插值模型的基础上,采用把垂距作为设计变量取值标准的取样算法。首先,将样本点与相邻 2点之间连线的垂距与垂距阈值进行比较,筛选出符合条件的数据点,形成候选点集;其次,选用高斯函数判定基础点集和候选点集的相关性;最后,使用该算法筛选后的样本点构建 Kriging插值模型。实验结果表明,该方法使用的样本点为原来数量的 70%左右,并且预测精度得到了提高,该方法是有效可行的。