基于改进粒子群算法辨识Volterra级数的目标机动轨迹预测

目标机动轨迹预测是空战态势感知和目标威胁评估的重要前提。针对传统目标机动轨迹预测模型复杂度大、预测精度低等问题，结合目标机动轨迹时间序列的混沌特性，提出一种基于相空间重构理论和Volterra泛函级数的目标机动轨迹预测模型。该模型首先采用0-1检测法验证了目标机动轨迹时间序列具有混沌特性；其次，利用C-C法确定嵌入维数和时间延迟，对目标机动轨迹时间序列进行了相空间重构；然后，引入Volterra泛函级数预测模型，为了克服高阶Volterra核函数求解复杂的难题，提出一种混沌变异自适应粒子群算法，构建一种基于改进粒子群算法辨识的Volterra级数预测模型，并将其应用于目标机动轨迹预测；最后，将所提算法与卡尔曼滤波算法以及机器学习算法进行单步和多步预测对比，同时将改进粒子群算法与其他智能算法进行性能比较。仿真结果表明：所提预测模型具有良好的单步和多步预测性能，改进的粒子群算法具有参数辨识精度高、收敛速度快的优点。     

Aeroacoustic and aerodynamic optimization of propeller blades

It is of great significance to develop a high-efficiency and low-noise propeller optimization method for new-generation propeller aircraft design. Coupled with free form deformation method, dynamic mesh interpolation technology, optimization algorithm, surrogate model, aerodynamic calculation and aeroacoustic prediction model module, the integrated aerodynamic and aeroacoustic design method of propeller is built. The optimization design for the six-blade propeller is carried out. The non-reduction in efficiency, thrust coefficient and the minimum of aerodynamic noise is treated as the optimization design objective. The spatial vorticity distribution of the propeller before and after the design is also analyzed by using unsteady computational fluid dynamics method. The results show that the optimized propeller can effectively reduce the aerodynamic noise level. The maximum total sound pressure level can be reduced by 5 dB without reducing its aerodynamic performance. The developed method has good application potential in low-noise optimization design of propeller and other rotating machinery.     

基于仿真优化的某航天产品装配线规划方法研究

航天产品的装配工艺复杂,具有典型的小批量、多品种特点,因此动态设置装配工位成为装配线规划的重要内容.本文以装配线平滑指数最小化为目标,以装配工艺、空间布局、路径等为约束条件,建立装配线工位数量优化模型.本文先对装配现场的不确定影响因素进行分析与建模,并提出基于仿真的优化方法,利用仿真模型模拟装配线运行状态,并通过遗传算法确定最优的工位数量,实现装配线的动态平衡,从而有效地提高装配线单元工位规划的精确度与效率.     

具有时间约束的无人机遗传算法航迹规划

为无人机进行航迹规划不仅要满足路径的有效性,还需要考虑无人机的到达时间约束.将遗传算法应用于动态环境中无人机的航迹规划,通过编码生成初始航迹,确定合理的适应度函数和进化操作,同时根据要求到达时间约束为每个航迹加入过点速度和到达时间信息,最终搜索规划出可飞且满足时间约束的航迹.在仿真实验中实现了针对移动目标的规划和突然出现威胁的局部重规划.     

Multi-objective optimization of parachute triggering algorithm for Mars exploration

A multi-objective optimization procedure to design parachute triggering algorithm, based on Monte Carlo analysis of flight uncertainties, has been developed in this paper. Most of Mars explorations missions utilize parachute for a safe descent through the lowest of the atmosphere. The parachute triggering algorithm is designed to accommodate the range of off-nominal entry trajectories, and is aimed to parachute opening in certain range of Mach numbers, dynamic pressure and altitude. Our novel algorithm takes the fight uncertainty into the account through Monte Carlo analysis, selects maximization of altitude statistical mean and minimization of Mach number statistical mean as two objectives, then employs multi-objective evolutionary algorithm based on decomposition (MOEA/D), to search the Pareto-front framework. Such a methodology can be implemented on the future design of entry, descent, and landing (EDL) mission.     

弹道跟踪数据融合处理的快速算法

针对弹道跟踪数据融合处理中的大计算量环节研究了快速算法。用样条函数表示弹道参数,建立了多测元的联合观测模型和弹道参数的非线性融合计算模型,给出了弹道参数的求解算法,分析了弹道参数融合计算中的大型矩阵运算问题,利用基础线性代数函数库提高了大型矩阵的运算速度。建立了样条模型计算的非线性约束优化模型,给出了确定样条节点位置的优化算法,通过分析样条模型的计算原理设计了并行算法,实现了样条模型的并行化计算。仿真结果表明,弹道参数融合计算和样条模型计算的效率都得到了显著提高,计算时间减少了65.47%,对缩短数据处理周期有重要意义。     

基于粒子群算法的导弹PID控制参数选择

PID控制在导弹控制系统的设计中得到了较多的应用,但PID控制器的性能依赖于其参数的整定和优化。利用粒子群算法对控制器的参数进行优化,并对俯仰通道控制系统进行了仿真研究,结果表明,攻角和俯仰角速度都取得了较好的效果,当气动参数具有不确定性时,效果依然较好,验证了方法的有效性。     

低HSI噪声旋翼桨尖外形优化设计方法

建立了一套基于计算流体力学(CFD)/FW-H_pds方程(Ffowcs Williams-Hawkings equations with penetrable data surface)的气动噪声预估技术和组合优化算法的低噪声旋翼桨尖平面外形设计方法。首先,采用积分形式的可压雷诺平均Navier-Stokes(RANS)方程作为旋翼流场求解控制方程,围绕旋翼流场的网格采用嵌套网格方法生成。在优化过程中,桨叶网格生成采用提出的高效参数化的网格自动生成方法。在建立的CFD方法求解基础上,采用基于可穿透旋转积分面的鲁棒性较好的FW-H_pds方程来求解旋翼高速脉冲(HSI)噪声。然后,以降低旋翼HSI噪声为目标,以旋翼悬停气动性能为约束,提出具备前掠-后掠-尖削等组合特征的桨尖外形方案并进行优化分析。将基于拉丁超立方(LHS)方法和径向基函数(RBF)的代理模型方法耦合到遗传算法过程中,建立了一种高效的组合优化算法。在当前的计算状态下,优化后的桨尖外形的负压峰值相比于矩形桨叶降低了58.4%,优化后的桨叶有效地减弱了旋翼桨尖区域的跨声速"离域化"现象,因此可以降低旋翼HSI噪声特性,同时可以减弱旋翼桨尖涡强度达30%,旋翼悬停性能提高了2%~3%。     

Parallel algorithm and its convergence of spatial domain decomposition of discrete ordinates method for solving radiation heat transfer problem

To improve the computational efficiency and hold calculation accuracy at the same time,we study the parallel computation for radiation heat transfer. In this paper, the discrete ordinates method(DOM) and the spatial domain decomposition parallelization(DDP) are combined by message passing interface(MPI) language. The DDP–DOM computation of the radiation heat transfer within the rectangular furnace is described. When the result of DDP–DOM along one-dimensional direction is compared with that along multi-dimensional directions, it is found that the result of the latter one has higher precision without considering the medium scattering. Meanwhile, an in-depth study of the convergence of DDP–DOM for radiation heat transfer is made. Analyzing the cause of the weak convergence, we relate the total number of iteration steps when the convergence is obtained to the number of sub-domains. When we decompose the spatial domain along one-,two- and three-dimensional directions, different linear relationships between the number of total iteration steps and the number of sub-domains will be possessed separately, then several equations are developed to show the relationships. Using the equations, some phenomena in DDP–DOM can be made clear easily. At the same time, the correctness of the equations is verified.     

基于遗传算法与评估模型的飞行载荷实测研究

为了得到飞行载荷实测所需的最优载荷方程,建立了可表征载荷方程数学意义和物理意义的评估模型,与遗传算法(GA)结合,以载荷方程评估驱动载荷方程建立,形成一种新的载荷方程建立方法——EMGA。通过建立某机翼根部剪力方程,比较了穷尽搜索(ES)法、传统GA和EMGA所建载荷方程的评估模型参数。最后将3种方法建立的方程用于飞行载荷实测。结果发现:和其他两种方法比较,本文提出的EMGA可得到最优的载荷方程;EMGA测得的飞行载荷分散性小,最优载荷方程的测量结果可作为最终的飞行载荷。