Deorbiter CubeSat mission design

This paper presents the mission design for a CubeSat-based active debris removal approach intended for transferring sizable debris objects from low-Earth orbit to a deorbit altitude of 100 km. The mission consists of a mothership spacecraft that carries and deploys several debris-removing nanosatellites, called Deorbiter CubeSats. Each Deorbiter is designed based on the utilization of an eight-unit CubeSat form factor and commercially-available components with significant flight heritage. The mothership spacecraft delivers Deorbiter CubeSats to the vicinity of a predetermined target debris, through performing a long-range rendezvous maneuver. Through a formation flying maneuver, the mothership then performs in-situ measurements of debris shape and orbital state. Upon release from the mothership, each Deorbiter CubeSat proceeds to performing a rendezvous and attachment maneuver with a debris object. Once attached to the debris, the CubeSat performs a detumbling maneuver, by which the residual angular momentum of the CubeSat-debris system is dumped using Deorbiter’s onboard reaction wheels. After stabilizing the attitude motion of the combined Deorbiter-debris system, the CubeSat proceeds to performing a deorbiting maneuver, i.e., reducing system’s altitude so much so that the bodies disintegrate and burn up due to atmospheric drag, typically at around 100 km above the Earth surface. The attitude and orbital maneuvers that are planned for the mission are described, both for the mothership and Deorbiter CubeSat. The performance of each spacecraft during their operations is investigated, using the actual performance specifications of the onboard components. The viability of the proposed debris removal approach is discussed in light of the results.     

插装式液控能源选择阀的设计与分析

针对液压系统油源的安全性及可靠性问题，提出油源冗余设计方法，即多油源供给多负载，当某一油源发生故障时能源选择阀切断故障油源，由其他油源给负载提供基本液压能源，系统能完成必须的服役性能。针对液压系统能源选择阀存在的频繁换向与振动问题，提出一种插装式液控能源选择阀，其先导阀采用锥阀和可变阻尼形式，主阀采用三台肩且终端有缓冲功能的滑阀。建立了插装式液控能源选择阀的数学模型，分析了能源选择阀在两种模拟故障下的切换压力及切换时间、油源压力脉动等特性。理论分析结果表明，油源压力阶跃和线性变化时，某能源选择阀切换时间分别为5 ms和7 ms。当压力发生波动时，主阀不会发生振动，还可通过液控先导阀结构设计来控制切换压力和回复压力大小。     

GEO/IGSO混合区域导航星座的设计与优化算法

区域导航星座能够以较低成本和较短时间获得目标区域导航能力，且地球同步轨道是构建非极区区域导航星座的重要轨道类型。提出一种基于GEO（地球静止轨道）和IGSO（倾斜地球同步轨道）的区域导航星座设计方法。基于星下点轨迹特性构造对称星座设计参数和优化参数集，并考虑地球扁率长期摄动影响，计算星座轨道参数。以导航服务区的统计GDOP（几何精度因子）为目标函数，利用差分进化算法构建星座优化模型。以印度IRNSS的7星星座为例，仿真检验了设计和优化算法的正确性，讨论了IRNSS星座优化构型和轨道类型选取。本方法采用的对称星座设计参数少，能够提升GEO/IGSO混合区域导航星座的全局优化效率，为后续非对称星座快速提供最佳星数和构型设计。     

Application of a modified generalized sail model on a solar sail with wrinkles

In this paper, we present an analysis of effect of wrinkles on the solar sail performance. We describe different analytical, semi-analytical and numerical approaches to the calculation of general large-scale curvature of a solar sail as well as parameters of so-called wrinkled domains, and introduce the impact of such wrinkles on the thrust and torque of the solar sail. Finally, we present a model of an optically-orthotropic surface for such non-ideal sail, providing a connection with the Generalized Sail Model, and other solar sail thrust models.     

厚覆冰导线升力突变机理及数值模拟研究

针对新月形厚覆冰导线的升力系数在风攻角15°附近存在突变的问题,分别采用基于k-ωSST湍流模型的雷诺时均法和大涡模拟(LES)的数值方法对新月形厚覆冰导线在风攻角10°~20°范围进行了模拟。通过对比两种数值方法计算得到的覆冰导线气动力系数、流场结构和表面风压,发现LES方法能够更好地捕捉新月形覆冰导线表面的小尺度涡结构,得到的覆冰导线气动力参数计算结果与风洞试验数据高度吻合;而k-ωSST湍流模型难以模拟壁面上小尺度涡,捕捉不到升力系数的突变。根据覆冰导线不同壁面区域的压力分布,发现上侧壁面处的涡结构影响整体流场,并在下侧壁面曲率、来流夹角和壁面切线方向共同作用下导致升力系数突变。LES的气动力参数模拟结果可为覆冰导线防舞提供参考。     

基于深度神经网络的客机总体设计参数敏感性分析

飞机总体主要设计参数敏感性分析揭示了总体主要设计参数对飞机特性指标的影响，有助于总体设计方案的决策。针对宽体客机总体主要设计参数敏感性问题，根据其总体主要设计参数和特性指标的特点，以及多学科间的耦合关系，建立了深度神经网络模型。该深度神经网络模型以客机总体主要设计参数为输入，对特性指标进行预测。在深度神经网络模型中，设置了多个输入层、多个输出层以及多个分块的隐藏层，从而模拟客机总体主要设计参数对特性指标的影响以及不同特性指标之间的相互作用。测试结果表明，与传统代理模型相比，深度神经网络模型对客机特性指标的预测精度更高，多参数适应性更好。利用该深度神经网络模型对客机总体主要设计参数进行敏感性分析。分析结果表明，机翼1/4弦线后掠角在30°~31.5°时，有利于减少最大起飞重量和起飞平衡场长；发动机海平面最大静推力和机翼面积对客机直接使用成本、最大起飞重量等特性指标的影响最为显著。     

金属增材制造数据处理与工艺规划研究综述

金属增材制造数据处理与工艺规划是金属增材制造软件系统的核心,涵盖了金属支撑结构设计、模型切片以及路径规划等内容,决定着最终金属零部件的产品性能。从金属增材制造模型前处理出发,较为全面地概述了与之相关的多类型支撑设计和新型支撑优化等数据处理内容,针对金属增材制造数据处理中模型切片这一关键环节,分别从平面切片、自适应切片和曲面切片等方面进行了研究进展综述,同时对金属增材制造工艺中的路径规划研究进展进行了概述,最后对其发展趋势做了初步展望。     

Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling

In the manufacturing of thin wall components for aerospace industry, apart from the side wall contour error, the Remaining Bottom Thickness Error (RBTE) for the thin-wall pocket component (e.g. rocket shell) is of the same importance but overlooked in current research. If the RBTE reduces by 30%, the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component. Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation. This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing. In this paper, the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components, which enables accurate prediction and compensation of RBTE value for the thin wall components. The physics based model considers the primary root cause, in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness (AMRT) variation, for the RBTE formation. And to incorporate the dynamic and inherent coupling of the complicated manufacturing system, the multi-feature fusion and machine learning algorithm, i.e. kernel Principal Component Analysis (kPCA) and kernel Support Vector Regression (kSVR), are incorporated with the physics based model. Therefore, the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy. The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling, and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling, respectively.     

飞行器智能设计愿景与关键问题

未来飞行器正朝着多元化、无人化和智能化的方向发展，高超声速、超隐身和变体等新型飞行器不断涌现。而传统飞行器解耦分拆的设计方法越来越难以满足未来飞行器综合性能全面提升的要求，只有通过整体化设计才能充分发掘飞行器的潜能。通过分析传统飞行器设计中存在的问题，提出满足全生命周期要求的飞行器智能设计体系理念，利用知识库的构建将智能赋予飞行器平台系统设计、制造生产和运维这3个阶段，并通过数字孪生技术进行飞行器全生命周期的仿真、分析和预测，以对飞行器设计、运行等数据进行更新，使该体系形成闭环。就飞行器智能设计体系中需要的关键技术及涉及的科学问题等进行了讨论，并给出了未来发展方向以供参考。     

涡轮基组合循环发动机分布式控制系统通信网络混合拓扑结构优化方法

涡轮机组合循环(Turbine based combined cycle,TBCC)发动机控制系统通信网络拓扑结构是其分布式控制系统方案设计的重要部分,优化网络拓扑结构可提高发动机推重比和控制系统可靠性。本文基于智能优化算法提出TBCC分布式控制系统网络拓扑结构优化方法。基于图论建立TBCC几何模型和网格模型,以重量和可靠性为优化性能指标,同时考虑发动机表面高温区域以及控制节点的工作可靠性,分别采用粒子群算法和遗传算法优化星形结构中智能中央节点位置、中央节点的环形拓扑结构,获得星形-环形混合拓扑结构。仿真实例表明,基于本文方法优化所得的混合拓扑结构相较于星形集中式控制结构,系统重量降低了51.9%。