基于蜂群算法的变循环发动机最小耗油率优化

性能寻优控制是使变循环发动机满足长寿命、低油耗和大推力需求的主要途径,也是实现飞机推进系统综合控制的关键技术.基于双外涵变循环发动机非线性数学模型,在满足发动机各部件物理约束条件和推力条件下,采用人工蜂群算法对变循环发动机典型亚声速巡航点(H=11 km,Ma=0.8)耗油率进行了寻优分析,找到了耗油率最小条件下发动机6个几何可调参数、5个发动机循环参数最优值.结果表明:亚声速巡航点的耗油率在优化后比优化前降低了4.5％,从而证实人工蜂群算法能够完成变循环发动机多维优化问题.     

Investigations of the evolution of optical characteristics and dynamics of proper rotation of uncontrolled geostationary artificial satellites

On the base of the photometrical data an analysis of the periods and light curves of the uncontrolled artificial satellites was performed.     

Gender specific changes in cortical activation patterns during exposure to artificial gravity

Keeping astronauts healthy during long duration spaceflight remains a challenge. Artificial gravity (AG) generated by a short arm human centrifuges (SAHC) is proposed as the next generation of integrated countermeasure devices that will allow human beings to safely spend extended durations in space, although comparatively little is known about any psychological side effects of AG on brain function.     

点覆盖数值仿真法设计区域覆盖Walker星座

利用点覆盖数值仿真法进行了区域覆盖最优 Walker星座设计研究 ,给出了合理的优化目标顺序和设计变量循环顺序 ,通过对 8种不同区域单、四重覆盖的设计结果进行分析 ,得到了一些有关 Walker星座区域覆盖特性的结论 ,可用于指导星座设计。     

编队飞行卫星群相对轨道摄动运动分析

编队飞行卫星群在运行过程中将受到各种摄动因素的影响,相对运动队形可能受到破坏而影响飞行任务的完成。针对中、低轨道编队飞行任务,分析了各种摄动因素对编队卫星相对队形的影响;导出了编队飞行卫星群相对队形的稳定条件;最后,结合稳定条件对典型编队队形进行了分析。     

测量带有常值偏差时卫星自主定轨系统可观性分析

考虑测量带有常值偏差时卫星自主定轨系统的状态观测和常值偏差估计问题,将观测常值偏差扩增为状态后进行卡尔曼滤波是解决这一问题的一个途径。为使卡尔曼滤波稳定,就必须分析系统的随机可观测性。文章利用线性时变系统的可观性理论对测量带有常值偏差时卫星自主定轨系统的可观性进行了分析,数值仿真结果显示了所得理论的正确性。     

分布式SAR超分辨成像的卫星编队构形优化设计方法

编队构形设计是分布式卫星系统总体设计的关键问题,提出了一种编队构形优化设计方法。针对主星带辅星群体制分布式合成孔径雷达(SAR)超分辨率任务,从优化系统效能的角度出发,将满足超分辨性能的时间比率和分辨率改善因子作为编队构形优化设计的目标函数,以基线矢量为中间变量,建立了目标函数与编队卫星轨道根数的关系,并采用遗传算法求解。对不同卫星数目的编队系统进行了优化设计仿真,并与干涉车轮和钟摆编队构形方案进行了分析比较,数值结果验证了该优化设计方法的有效性和正确性。该设计方法还可改变优化目标函数,以适应用户的任务要求。     

非合作目标自主交会对接的椭圆蔓叶线势函数制导

针对非合作式航天器自主交会对接任务的安全性要求,提出了一种基于椭圆蔓叶线的人工势函数制导方法。首先根据视线坐标系建立了相对动力学方程与状态方程。进而应用人工势函数制导方法解决了非合作目标航天器自主交会对接与静态障碍物躲避问题,并且把势函数方法与椭圆蔓叶线函数相结合,解决了追踪航天器在接近目标航天器时运行在安全走廊中的安全性要求。应用Lyapunov稳定性理论证明了在所提出的制导方法控制下系统的稳定性。最后,用精确的数学模型进行了计算机数值仿真,验证了所提出的制导控制方法的正确性和有效性。
     

Interaction of oblique wave beam with ionospheric layer F2

Interaction of a powerful obliquely incident wave beam of decameter radio waves with the ionospheric F2 layer is analyzed. Much like the linear case, propagation through the natural anti-waveguide layer F2 splits the initial beam. Some part of its energy leaks through the ionospheric layer, the other part goes back along a downward trajectory. However, nonlinearity leads to further stratification of the ionospheric layer. A new feature, in comparison with the linear case, is appearing a narrow waveguide beneath the F2 layer maximum which traps a small part of the beam energy. We study the relationship between these parts of the wave field in a simplified model of parabolic F2 layer, with nonlinearity caused by thermal plasma expulsion from the high field intensity region. Analytical results are supplemented with numerical estimates of the effects.     

On developing data-driven turbulence model for DG solution of RANS

High-order Discontinuous Galerkin(DG) methods have been receiving more and more attentions in the area of Computational Fluid Dynamics(CFD) because of their high-accuracy property. However, it is still a challenge to obtain converged solution rapidly when solving the Reynolds Averaged Navier–Stokes(RANS) equations since the turbulence models significantly increase the nonlinearity of discretization system. The overall goal of this research is to develop an Artificial Neural Networks(ANNs) model with low complexity acting as an algebraic turbulence model to estimate the turbulence eddy viscosity for RANS. The ANN turbulence model is off-line trained using the training data generated by the widely used Spalart–Allmaras(SA) turbulence model before the Optimal Brain Surgeon(OBS) is employed to determine the relevancy of input features.Using the selected relevant features, a fully connected ANN model is constructed. The performance of the developed ANN model is numerically tested in the framework of DG for RANS, where the‘‘DG+ANN" method provides robust and steady convergence compared to the ‘‘DG+SA" method. The results demonstrate the promising potential to develop a general turbulence model based on artificial intelligence in the future given the training data covering a large rang of flow conditions.