多级涡轮多目标气动优化设计流程

在准三维设计基础上,采用多目标优化设计方法,给出一个多级涡轮气动优化设计流程,优化联合采用人工神经网络和遗传算法,流场计算采用全三维粘性流N-S方程求解。此优化设计流程有三个特点:针对每列叶栅的气动特性进行局部优化;各列叶栅反复多次优化;粗细网格交替使用。并采用此设计流程对一三级涡轮进行优化设计,效率提高1%,说明此方法可以有效的用于多级涡轮气动优化设计。     

基于星载气象雷达的非线性调频信号优化设计

随着天气观测、研究和规划的进展,气象雷达技术的开发工作取得了重大进展。脉冲压缩使新一代低成本、紧凑型星载天气雷达系统成为可能。为了抑制脉压带来的距离旁瓣,目标探测雷达通常采用基于幅度调制和失配滤波的方法,但由于其存在主瓣扩展和功率损失等缺点,不适用于气象观测。非线性调频（NLFM）信号可以调整其功率谱密度,在明显不降低信噪比的情况下提供较低的旁瓣输出。本文设计了一种新的波形优化框架。该框架通过多目标粒子群优化算法（MOPOS）构造了一种针对气象粒子目标的非线性调频（NLFM）脉冲压缩波形,实现了极低的距离旁瓣电平和较高的多普勒容限,可以显著地缓解非常有限的卫星峰值功率的限制和卫星平台运动导致的多普勒对系统影响。仿真实验结果表明：改进的非线性调频波形具有良好的性能。     

基于多目标规划的交会对接推力器指令分配方法

将由控制律得到的控制量转化为推力器控制指令的过程称为推力器指令分配。由于交会对接涉及的推力器配置复杂,且对追踪航天器姿轨控精度的要求很高,因此其推力器指令分配问题十分重要。本文针对该问题,分析了基于线性规划的指令分配方法存在的不足,提出了将推力器指令分配问题转化为非线性多目标规划问题,并应用分枝定界法进行寻优的新方法。新方法可以在指令分配的误差和燃料消耗之间进行折衷,并且考虑了推力器最小开机时间的限制。仿真结果验证了新方法的有效性。     

Multi-objective design optimization of the transverse gaseous jet in supersonic flows

The mixing process between the injectant and the supersonic crossflow is one of the important issues for the design of the scramjet engine, and the efficiency mixing has a great impact on the improvement of the combustion efficiency. A hovering vortex is formed between the separation region and the barrel shock wave, and this may be induced by the large negative density gradient. The separation region provides a good mixing area for the injectant and the subsonic boundary layer. In the current study, the transverse injection flow field with a freestream Mach number of 3.5 has been optimized by the non-dominated sorting genetic algorithm (NSGA II) coupled with the Kriging surrogate model; and the variance analysis method and the extreme difference analysis method have been employed to evaluate the values of the objective functions. The obtained results show that the jet-to-crossflow pressure ratio is the most important design variable for the transverse injection flow field, and the injectant molecular weight and the slot width should be considered for the mixing process between the injectant and the supersonic crossflow. There exists an optimal penetration height for the mixing efficiency, and its value is about 14.3 mm in the range considered in the current study. The larger penetration height provides a larger total pressure loss, and there must be a tradeoff between these two objection functions. In addition, this study demonstrates that the multi-objective design optimization method with the data mining technique can be used efficiently to explore the relationship between the design variables and the objective functions.     

Adaptive-surrogate-based robust optimization of transonic natural laminar flow nacelle

Natural Laminar Flow (NLF) technology is very effective for reducing the skin friction drag of aircraft engine nacelle, but the aerodynamic performance of NLF nacelle is highly sensitive to uncertain working conditions. Therefore, it’s imperative to incorporate uncertainties into the design of NLF nacelle. In this study, for a robust optimization of NLF nacelle and for improving its efficiency, an adaptive-surrogate-based robust optimization strategy is established, which is an iterative optimization process where the surrogate model is updated to obtain the real Pareto front of multi-objective optimization problem. A case study is carried out to validate its feasibility and effectiveness. The results show that the optimization increases the favorable pressure gradient region and the volume ratio of the nacelle by increasing its lip radius and reducing its maximum diameter. And the aerodynamic robustness of the NLF nacelle is mainly determined by the lip radius, maximum diameter of nacelle and location of the maximum diameter. Compared to the initial nacelle, the optimized nacelle maintains a wide range of low drag and high laminar flow ratio in the disturbance space, which extends the average laminar flow region to 21.6% and facilitates a decrease of 1.98 counts in the average drag coefficient.     

多冲量近程交会最优化模型

文章基于Lawden方程对椭圆参考轨道的近程最优交会问题进行了研究,并提出了一种混合遗传算法求解最优近程交会问题。首先在一定假设条件下给出了目标在椭圆参考轨道的近距离相对运动模型——Lawden方程,构建了多脉冲最优交会问题模型并进行了理论分析。性能指标选为轨道交会过程中燃料消耗和时间消耗加权最小的多目标优化指标,优化参数为脉冲大小和脉冲施加时刻,终端状态受到相对位置和相对速度的约束。然后介绍了具有较强全局和局部寻优能力的混合遗传算法。最后以四脉冲为例进行仿真计算。仿真结果表明,是否考虑第一次脉冲位置,总燃料消耗变化不明显。因此,追踪航天器一旦捕获到目标信息即可施加第一次脉冲。仿真结果还证明了混合遗传算法在求解最优交会问题时的有效性。因此,混合遗传算法对基于Lawden方程的椭圆参考轨道近程最优交会问题的求解可行。     

基于多柔体动力学的飞行器多目标优化设计

研究了柔性航天器总体设计中基于结构与姿态控制的多目标优化问题。利用拉格朗日 方程建立了刚柔耦合系统动力学模型,提出以附件质量和微分矩阵最大实特征值为目标函数 的多目标优化问题;采用非支配排序进化求解算法（NSGA-II）,对某柔性航天器进行了多目 标优化分析设计;最优决策为具有一定规律性的空间曲线,该优化结果对柔性卫星的总体分 析设计具有一定的指导意义。
     

Optimization of fixed aviation support resource station configuration for aircraft carrier based on aircraft dispatch mission scheduling

Before the dispatch of the carrier-based aircraft, a series of pre-flight preparation operations need to be completed on the flight deck. Flight deck fixed aviation support resource station configuration has an important impact on operation efficiency and sortie rate. However, the resource station configuration is determined during the aircraft carrier design phase and is rarely modified as required, which may not be suitable for some pre-flight preparation missions. In order to solve the above defects, the joint optimization of flight deck resource station configuration and aircraft carrier pre-flight preparation scheduling is studied in this paper, which is formulated as a two-tier optimization decision-making framework. An improved variable neighborhood search algorithm with four original neighborhood structures is presented. Dispatch mission experiment and algorithm performance comparison experiment are carried out in the computational experiment section. The correlation between the pre-flight preparation time (makespan) and flight deck cabin occupancy percentage is given, and advantages of the proposed algorithm in solving the mathematical model are verified.     

Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model

To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy(KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions(LF-NDS). Secondly, aiming to obtain the High-Fidelity(HF) sample points located in promising are...     

Sequential dynamic resource allocation in multi-beam satellite systems:A learning-based optimization method

Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources. One of the primary challenges accompanying with Multi-Beam Satellites(MBS) is an efficient Dynamic Resource Allocation(DRA) strategy. This paper presents a learning-based Hybrid-Action Deep Q-Network(HADQN) algorithm to address the sequential decision-making optimization problem in DRA. By using a parameterized hybrid action space,HADQN makes it possible to schedule the beam patte...