多机并联火箭羽流流场及其底部热环境分析

For the problem that the plume flow field structure of a multi engine parallel rocket is complicated and the bottom thermal environment is extremely harsh, which may cause the failure of the engine structural components, the plume flow field and thermal environment at different altitudes are studied through numerical simulation. The result is compared with the measured results in flight which shows that when the rocket is flying at a low altitude, the plume of the engines do not interfere with each other. As the flight altitude increases, the plumes gradually expand and begin to interfere with each other, and finally there is an obvious backflow at the bottom of the rocket. The maximum heat flux at the moment of take off is basically the same as the measured value in flight. Before the backflow occurs, the heat flux mainly consists of radiant heat, the convective heat flow increases as the flight altitude grows, but it is also much smaller than the peak heat flow at takeoff. The result has certain guiding significance for the optimal design of engine structure thermal protection.     

基于脑电功率谱密度的作业人员脑力负荷评估方法

脑力负荷状态的准确识别对减少因作业人员无效脑力负荷导致的人因事故具有重要意义。针对人-机系统中作业人员脑力负荷客观评估问题开展了基于MATB-Ⅱ平台的3种不同脑力负荷水平下的航空情境实验，记录16名被试的NASA任务负荷指数（NASA-TLX）量表数据和脑电（EEG）信号，提出了一种基于脑电功率谱密度（PSD）和支持向量机（SVM）的个体脑力负荷评估方法。结果表明：随着实验设计脑力负荷水平增加，被试的主观脑力负荷得分显著提高（p<0.001），这表明该实验任务设计较好地诱发了低负荷、中负荷和高负荷情境。在此基础上，通过网格搜索法确定个体脑力负荷评估模型的统一优化参数，惩罚系数取3 000，核函数参数取0.000 1，模型测试正确率达到0.966 5±0.029 8，宏平均的受试者工作特征曲线下的面积（Macro-AUC）达到0.991 0±0.011 4。本文为作业人员脑力负荷状态的客观和准确评估提供了一种新的办法，为后期作业人员脑力负荷状态的实时判别提供模型基础。     

磁选态单束铯束管束光学参数的模拟计算和设计

为提高铯束管跃迁信号强度,在束光学参数设计中,用蒙特卡罗方法模拟计算铯原子通过率和未跃迁比例。通过改变模拟计算中的束光学参数,分析铯原子通过率、未跃迁比例和倍增器首极电流的变化趋势,得到了主要束光学参数的合理取值范围。在铯束管实际装配和试验中使用束光学参数设计结果,有效地增加了倍增器首极电流。     

Fault Estimation and Accommodation for a Class of Nonlinear System Based on Neural Network Observer

The problem of fault estimation and accommodation of nonlinear systems with disturbances is studied using adaptive observer and neural network techniques.A robust adaptive learning algorithm based on switchingβsmodification is developed to realize the accurate and fast estimation of unknown actuator faults or component faults.Then a fault tolerant controller is designed to restore system performance.Dynamic error convergence and system stability can be guaranteed by Lyapunov stability theory.Finally,simulation results of quadrotor helicopter attitude systems are presented to illustrate the efficiency of the proposed techniques.     

A surface parametric control and global optimization method for axial flow compressor blades

An aerodynamic optimization method for axial flow compressor blades available for engineering is developed in this paper. Bezier surface is adopted as parameterization method to control the suction surface of the blades, which brings the following advantages:(A) significantly reducing design variables;(B) easy to ensure the mechanical strength of rotating blades;(C) better physical understanding;(D) easy to achieve smooth surface. The Improved Artificial Bee Colony(IABC) algorithm, which significantly increases the convergence speed and global optimization ability, is adopted to find the optimal result. A new engineering optimization tool is constructed by combining the surface parametric control method, the IABC algorithm, with a verified Computational Fluid Dynamics(CFD) simulation method, and it has been successfully applied in the aerodynamic optimization for a single-row transonic rotor(Rotor 37) and a single-stage transonic axialflow compressor(Stage 35). With the constraint that the relative change in the flow rate is less than0.5% and the total pressure ratio does not decrease, within the acceptable time in engineering, the adiabatic efficiency of Rotor 37 at design point increases by 1.02%, while its surge margin 0.84%,and the adiabatic efficiency of Stage 35 0.54%, while its surge margin 1.11% after optimization, to verify the effectiveness and potential in engineering of this new tool for optimization of axial compressor blade.     

任务型教学法的任务设计

任务型教学法是新兴的一种强调“在做中学”(learning by doing)的语言教学方法，是我国外语课程教学改革的一个走向，吸收了以往多种教学法的优点而形成。任务是任务型教学法中一个重要概念。要使任务型教学法更好地发挥作用，任务的设计就显得尤为重要。在任务设计中，要注意任务与人本主义、任务与文化性、任务与教师角色之...     

Particle swarm optimization applied to hypersonic reentry trajectories

This paper presents the novel use of the particle swarm optimization(PSO)to generate the end-to-end trajectory for hypersonic reentry vehicles in a quite simple formulation.The velocitydependent bank angle profile is developed to reduce the search space of unknown parameters based on the constrained PSO algorithm.The path constraints are enforced by setting the fitness function to be infinite on condition that the particles violate the maximum allowable values.The PSO algorithm also provides a much easier means to satisfy the terminal conditions by adding penalty terms to the fitness function.Furthermore,the approximate reentry landing footprint is fast constructed by incorporating an interpolation model into the standardized bank angle profiles.Numerical simulations demonstrate that the PSO method is a feasible and flexible tool to generate the end-to-end trajectory and landing footprint for hypersonic reentry vehicles.     

战斗机嵌入式训练系统中的智能虚拟陪练

智能化"实虚"对抗是现代先进战斗机嵌入式训练系统的重要功能需求。自主空战决策控制技术在未来空战装备发展中扮演关键角色。将当前的功能需求和发展中的技术结合起来,得到了空战智能虚拟陪练的概念。先进控制决策技术的引入使得智能虚拟陪练能够帮助飞行员完成复杂的战术训练,而训练中真实的对抗场景为技术的验证提供了理想的环境,大量的训练数据为技术的持续迭代优化提供了保障。作为可学习和进化的空战战术专家,智能陪练在人机对抗和自我对抗中不断优化,当其具备与人相当甚至超越人的战术能力时,可应用于未来的无人空战系统。智能虚拟陪练需要具备4项基本能力：智能决策能力、知识学习能力、对抗自优化能力和参数化表示能力。对其包含的关键技术进行了分析,提出并实现了一个基于模糊推理、神经网络和强化学习的解决方案,展示了其各项基本能力及目前达到的空战水平。未来更多的模型和算法可在智能虚拟陪练的框架中进行验证和优化。     

基于火箭橇的无人机碰撞民用飞机试验技术研究

无人机碰撞民用飞机关键部位损伤程度的研究是民航领域关注的新型热点问题。本文首次提出采用 火箭橇试验评估无人机碰撞民用飞机的安全性。通过对碰撞速度、碰撞位置、无人机姿态、参数测量等进行研 究,设计碰撞技术方案,论述火箭橇的设计、弹道控制设计、强度校核和测试方案,并进行试验验证和仿真计算。 结果表明:本文设计的火箭橇碰撞试验方案是可行的,实现了单一弹道上的连续多点碰撞及多视角、全覆盖、高 稳定的全过程记录;可为后续开展同类碰撞试验提供必要的技术参考,所开展的多发次碰撞试验也为无人机碰 撞民用飞机的安全性评估提供了有效的试验数据。