An aero-engine life-cycle maintenance policy optimization algorithm: Reinforcement learning approach

An aero-engine maintenance policy plays a crucial role in reasonably reducing maintenance cost. An aero-engine is a type of complex equipment with long service-life. In engineering,a hybrid maintenance strategy is adopted to improve the aero-engine operational reliability. Thus,the long service-life and the hybrid maintenance strategy should be considered synchronously in aero-engine maintenance policy optimization. This paper proposes an aero-engine life-cycle maintenance policy optimization algorithm that synchronously considers the long service-life and the hybrid maintenance strategy. The reinforcement learning approach was adopted to illustrate the optimization framework, in which maintenance policy optimization was formulated as a Markov decision process. In the reinforcement learning framework, the Gauss–Seidel value iteration algorithm was adopted to optimize the maintenance policy. Compared with traditional aero-engine maintenance policy optimization methods, the long service-life and the hybrid maintenance strategy could be addressed synchronously by the proposed algorithm. Two numerical experiments and algorithm analyses were performed to illustrate the optimization algorithm in detail.     

基于深度学习的超临界裂解煤油流量特性研究

为了明晰超临界裂解煤油的流量特性,实现对超临界裂解煤油流量的准确预测,采用实验方法测量了较大压力和温度范围内超临界裂解煤油的流量,对超临界裂解煤油流量特性进行了分析,基于多元线性回归方法,多元多项式回归方法和深度学习方法分别建立了超临界裂解煤油流量预测模型并给出了模型评估指标。研究结果表明,超临界裂解煤油流量主要与压降和温度有关,并且流量与压降和温度之间存在着很强的非线性关系;基于深度学习方法的深度神经网络模型性能优于多元线性回归模型和多元多项式回归模型,能够更加准确地刻画超临界裂解煤油的流量特性,其平均相对预测误差在1.1%左右,最大相对预测误差在7%以下。     

涡轮盘疲劳寿命评估流程及应用

建立了较完整的涡轮盘疲劳寿命评估流程,并以一个涡轮转子模型的分析实例给出了寿命评估流程的实施过程,同时探索了涡轮盘考核部位模拟试验件设计的方法。结果表明:所建立的涡轮盘寿命评估流程具有较强的工程实用性,所发展的寿命方程参数确定方法可有效利用现有材料手册中各种材料基础试验数据并能获得一组物理意义明确的参数,此寿命方程预测的寿命精度在2倍分散带之内;通过涡轮盘计算分析发现应力梯度是影响涡轮盘中心孔、螺栓孔边以及过渡圆角等部位寿命的关键因素,需在设计过程中重点关注;通过带缺口的平板模型算例模拟了涡轮盘危险部位的应力梯度特征,实现试验件缺口部位的应力梯度与涡轮盘考核部位的相对梯度特征接近,并提供了缺口模拟试验件设计方法,为使用简单缺口试验件评估涡轮盘考核点的寿命提供了技术途径。      

考虑建模误差的某型火箭末级制导方法研究

运载火箭末级发动机点火后,在制导系统的导引下,火箭最终抵达入轨位置、取得入轨速度,卫星进入运行轨道。火箭末级飞行动力学模型为非线性微分方程形式,难以利用解析方法得到火箭姿态变化特性,进而控制火箭飞行过程中的状态变量。将火箭入轨约束条件转化为最优控制的性能指标函数,利用庞德里亚金极小值原理与牛顿梯度法相结合的方式来解决动力学方程求解问题,得到末级飞行标准轨道;火箭动力学建模过程中,由于参数难以精确计量等的影响,存在建模误差,导致火箭实际飞行轨道会偏离标准轨道。对模型进行线性化处理,引入状态反馈,可以使实际飞行轨道接近于标准轨道,提高卫星荷载入轨要求精度。仿真结果表明:该制导算法可较好地减小火箭载荷入轨误差。     

基于多智能体深度强化学习的多星观测任务分配方法

为应对多星环境中复杂多约束条件下的任务分配场景,提出一种多星自主决策观测任务分配算法,该算法采用基于集中式训练、分布式执行的多智能体深度强化学习算法。通过这种方式训练后的卫星智能体,即使在没有中心决策节点或通信受限的情况下,仍具有一定的自主协同能力及独立实现多星观测任务的高效分配能力。     

Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

The maneuvering time on the ground accounts for 10%–30% of their flight time, and it always exceeds 50% for short-haul aircraft when the ground traffic is congested. Aircraft also contribute significantly to emissions, fuel burn, and noise when taxiing on the ground at airports. There is an urgent need to reduce aircraft taxiing time on the ground. However, it is too expensive for airports and aircraft carriers to build and maintain more runways, and it is space-limited to tow the aircraft fast using tractors. Autonomous drive capability is currently the best solution for aircraft, which can save the maneuver time for aircraft. An idea is proposed that the wheels are driven by APU-powered (auxiliary power unit) motors, APU is working on its efficient point; consequently, the emissions, fuel burn, and noise will be reduced significantly. For Front-wheel drive aircraft, the front wheel must provide longitudinal force to tow the plane forward and lateral force to help the aircraft make a turn. Forward traction effects the aircraft’s maximum turning ability, which is difficult to be modeled to guide the controller design. Deep reinforcement learning provides a powerful tool to help us design controllers for black-box models; however, the models of related works are always simplified, fixed, or not easily modified, but that is what we care about most. Only with complex models can the trained controller be intelligent. High-fidelity models that can easily modified are necessary for aircraft ground maneuver controller design. This paper focuses on the maneuvering problem of front-wheel drive aircraft, a high-fidelity aircraft taxiing dynamic model is established, including the 6-DOF airframe, landing gears, and nonlinear tire force model. A deep reinforcement learning based controller was designed to improve the maneuver performance of front-wheel drive aircraft. It is proved that in some conditions, the DRL based controller outperformed conventional look-ahead controllers.     

深空探测器总体技术

提出了深空探测器的总体设计要点,分析了总体设计约束,以及深空探测任务对探测器需求特点。基于技术的继承性和衔接性,提出了未来我国火星、小行星、金星以及木星等深空探测器的总体方案构想,讨论了任务目标、总体指标、构型和飞行过程等。研究对我国未来深空探测任务的方案设计有一定的参考价值。     

航空Ti600合金与其梯度功能修复涂层的腐蚀特性对比分析

为修复损伤的 Ti600合金结构件,开发新型耐蚀涂层,应用 Nd:YAG 激光在 Ti600表面制备了钛基梯度功能涂层材料(Functional Gradient Materials,FGM),并测试其耐腐蚀性能.结果表明,Ti600基体和 FGM 耐蚀性均较好;在微观上均呈现局部腐蚀的特性,表面呈腐蚀微坑形貌;极化电位较低;自腐蚀电流小,在30~90 nA/cm2之间.FGM 耐蚀性能较 Ti600得到提高,腐蚀电流为 Ti600的72%     

时变参数辨识梯度算法稳定性分析

研究用于时变参数辨识的梯度算法稳定性问题.基于随机过程有界性判据对时变参数辨识梯度算法进行了稳定性分析,给出了梯度算法稳定的充分条件.指出在待辨识参数变化率有界,观测噪声是零均值白噪声,且系统满足持续激励条件的情况下,梯度算法参数选择满足一定条件时,能够确保参数辨识误差的有界性.上述研究与以往工作的不同之处在于稳定性证明过程中仅要求待辨识参数的变化率是有界的,而不要求参数变化率是零均值白噪声.     

非稳态表面热流反演算法研究

表面热流的反演是一类典型的热传导逆问题。在采用有限体积法数值求解三维非稳态热传导问题的基础上,将该热传导逆问题转化为优化问题,建立了顺序函数法和共轭梯度法这两类反演算法。采用这两类算法对一个典型算例的计算结果表明:建立的两类反演算法都是有效的,具有较好的抗噪性能。顺序函数法的优势在于算法的推导和实现较为简便,但算法中存在人为确定的参数r,该参数的选取对反演结果有较大影响。共轭梯度法的算法中没有自由参数,但算法的推导和实现都较为复杂,其反演结果的精度和顺序函数法的反演结果精度基本相当。