自适应预测补偿的迭代制导方法及其应用研究

针对迭代制导完成后入轨参数或终端程序角修正问题,研究一种基于模型参考的自适应预测补偿迭代制导算法在运载火箭上的应用。该算法在经典迭代制导算法的基础上,根据预测的迭代终端程序角和飞行视加速度的参考模型,对关机点参数进行补偿,依据补偿后的终端指标重新规划飞行轨迹,进而得出满足入轨参数或终端程序角偏差修正的制导指令,提升迭代制导对入轨参数偏差或终端程序角的修正能力。此外,阐述了经典迭代制导的基本算法,概括了自适应预测补偿迭代制导算法的基本原理,并以大推力直接入轨、终端程序角大偏差以及满足终端程序角约束为例,给出相应工况的自适应预测补偿的迭代制导算法。仿真结果表明：该算法对入轨参数和终端程序角偏差具有一定的修正能力。     

基于项目集识别过程的航空领域重大基础研究项目发现

航空领域专项计划的实施需要发现大量重大基础研究项目进行支撑,而目前航空科研管理部门在项目发现过程中以被动管理为主,未能充分发挥其作用。通过分析自上而下航空领域重大基础研究项目内涵及现状基础上,借鉴项目集识别过程及其项目识别特点,构建自上而下的航空领域重大基础研究项目发现过程及机制,分析重大基础研究项目发现所涉及的航空领域专项计划主管部门、航空科研管理部门以及科研人员间的相互关系和功能,并结合某航空科研管理部门进行实例应用。表明基于项目集识别过程梳理和完善了航空领域重大基础研究项目发现过程及机制,强化了航空科研管理部门作用,提升了项目发现有效性。     

China’s Aviation Industry: a Global Partnership Strategy

This article discusses one of the main strategies which China has implemented to develop its competency in the aviation field. It is strategic for China to boost its aviation industry via international cooperation and several models of involvement into the global aviation value chain with leading aviation industries. As it is depicted as a strategy here, it is not necessary to say that a formal strong national strategy in encouraging exists. Actually, it is clearly a way China’s aero industry takes to develop its competency and business at the context it encounters. From an observer’s perspective, it is indeed a critical strategy. This article describes this strategy via the context, policies, program cases, as well as the evolution of the cooperation models in order to synthesize the strategy.     

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning (RL) techniques are being studied to solve the Demand and Capacity Balancing (DCB) problems to fully exploit their computational performance. A locally generalised Multi-Agent Reinforcement Learning (MARL) for real-world DCB problems is proposed. The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management (ATFM) region to quickly obtain a satisfactory solution. In this method, agents of all flights in a scenario form a multi-agent decision-making system based on partial observation. The trained agent with the customised neural network can be deployed directly on the corresponding flight, allowing it to solve the DCB problem jointly. A cooperation coefficient is introduced in the reward function, which is used to adjust the agent’s cooperation preference in a multi-agent system, thereby controlling the distribution of flight delay time allocation. A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated. Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method. From a statistical point of view, it is proven that the proposed method is generalised within the scope of the flights and sectors of interest, and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods. The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.     

航空公司欧美国际航线运营持续时间生存分析

航空公司航线进入后的运营可持续性直接影响其盈利水平。基于生存分析方法探究影响航线运营持续时间的因素，从成本和供给特征、航线和机场特征、市场结构特征、航司合作类型和时间异质性5个方面选取关键因素，以自由化程度较高的欧美跨大西洋市场为实证对象，研究年份为2003—2021共19年，采用Kaplan-Meier方法进行单因素分析，并构建威布尔参数生存模型分析影响因素与航线运营持续时间的定量关系。结果表明：（1）航司在欧美航线的平均运营持续时间为5年，持续运营超过10年的航线占比为16%;(2)距离、航司投入总运力、航线结构、端点机场类型和竞争程度、航司类型及所属区域和联盟对航司航线运营持续时间具有显著影响；（3）联盟和代码共享独立变量和交互变量均对航线生存产生显著影响，星空和天合联盟成员的航线运营持续时间更长，联盟成员在代码共享合作时承担不同承运人角色对航线退出风险影响存在显著差异。     

军用飞机维修大纲修订流程研究

本文首先论述了维修大纲修订的必要性和实际状况,接着介绍了B777、Y8和某型军机的维修大纲修订情况,以及波音的维修大纲修订实施程序,最后重点阐述了如何从实际情况出发,并借鉴波音的经验,建立一套适用于我国军用飞机维修大纲修订工作的实施流程,以有效地指导该项工作,提高军用飞机的可用度,降低维修成本。