An error analysis and optimization method for combined measurement with binocular vision

The accurate measurement of surfaces of large aviation components is vital for the assessment of manufacturing and assembly quality of such components. To satisfy the measurement requirement of large-size components, most current researches pay more attention to combined measurement methods utilizing different measuring instruments, but the related researches on error analysis and optimization methods are not taken enough attention. This paper proposes a combined laser-assisted measurement method with feature enhancement techniques, and it also develops an error propagation model of the main factors affecting the overall measurement error in detail. Firstly, the surface of a large-size component is measured by the measurement system at multiple stations. Secondly, a control point coordinate system is established as a bridge to unify all local measurement data into the global coordinate system. To improve the overall measurement accuracy, the pixel extraction error as a key factor causing the overall measurement error is analyzed in detail. Next, the error propagation model is established, and some optimization strategies of layout for minimizing measurement error and transformation error are researched. Finally, experiments are carried out to verify the effectiveness of the proposed method. The results show that the measurement error of the proposed method reaches 0.073% and 0.14% with a 1D standard ruler and a flat plate, respectively.     

基于寿命法的压力敏感涂料静/动态特性实验

压力敏感涂料测量技术在内外流表面压力测量方面具有独特的技术优势。涂料光谱特性及"传感器"特性的研究对于分析喷涂固化中环境差异等不可控因素的影响程度、检验拟用涂料配方特定试验的适用性具有十分重要的意义。为此，以一种在研的磷光压力敏感涂料为对象，基于自主研发的静态校准系统对发光寿命随压力与温度的变化规律进行了实验研究，通过对测量结果的拟合分析发现所用涂料的温度适用范围在30~60℃之间，且可进行温度敏感度修正；基于自主研发的高频动态压力光学校准系统对涂料在不同压力脉动频率下的涂料发光寿命峰峰值规律进行了实验研究，通过分析研究确定所用涂料基于发光寿命的截止频率上限为0.94 kHz。实验研究进一步验证了涂料特性静动态校准组合方法的可行性与有效性，为压力敏感涂料配方的研发及其应用拓展提供了重要的技术支撑。     

适用于多迎角结冰试验的混合翼型设计

为突破结冰风洞对翼型模型尺寸的限制,提出了一种新的混合翼型设计方法,可使用一套混合翼型模拟原始翼型在不同迎角下的结冰试验,弥补了以往混合翼型只能在单个设计迎角下使用的缺陷。方法采用多段翼的形式设计混合翼型,以多目标迎角等结冰试验条件作为设计输入,优化设计主翼外形和襟翼的位置、偏转角度,利用襟翼位置和偏转角度的变化实现混合翼型和原始翼型前缘表面的压力系数在不同迎角下能够保持一致,继而保证前缘结冰外形的一致性。设计的混合翼型较原始翼型弦长减小接近40%,在冰风洞中对混合翼型和原始翼型在目标结冰条件下进行试验,对比结果显示,在选取的多个目标迎角下,混合翼型和原始翼型二维截面的结冰外形基本一致,提出的混合翼型设计方法能够有效的模拟原始翼型在不同迎角下的结冰外形。     

PMSM电流预测控制参数误差量化分析方法

交流永磁同步电机（PMSM）电流环控制性能是制约交流伺服系统性能的关键。电流预测控制拥有更快的动态响应、更低的电流谐波和优良的转矩响应，但该算法依赖精确的电机模型，参数失配会引发稳态电流误差，无法输出额定转矩，进而导致电机转矩输出效率降低。根据永磁同步电机电流预测模型，详细分析了d、q轴电流静差产生的原因，以及电流预测控制对电机参数误差的敏感性，提出了一种参数误差量化分析方法。该方法引入了电机参数偏差因子，量化描述了电机电感、磁链参数误差与电流静差之间的制约关系。通过仿真分析，验证了所提方法的合理性，为高性能永磁伺服电流预测数字控制技术打下了良好基础。     

一种中心型间断有限元MMALE方法

针对多介质可压缩流体动力学问题,提出了一种单元中心型二维MMALE(Multi-Material Arbitrary Lagrangian-Eulerian)方法。在拉氏步,流体力学方程组采用中心型间断有限元方法求解。对于混合网格,采用Tipton压力松弛模型更新物理量,用等参坐标法更新物质中心点坐标。界面重构采用一种健壮的MOF(Moment of Fluid)方法。在重映步提出了基于多边形相交的二阶积分守恒重映方法。该方法分为四个部分:多项式重构、多边形相交、积分和后验校正。多边形相交使用"剪裁投影"算法,显著降低了多边形相交算法的复杂度。后验校正是基于MOOD (Multi-dimensional Optimal Order Detection)限制策略,并做了一些改动以适应多介质的计算。数值算例表明,该方法具有二阶的精度和较好的鲁棒性。     

Weibull分布参数估计值对细节疲劳额定强度的影响

将新材料、新工艺应用于民机结构设计时，细节疲劳额定强度（DFR）法仍是重要的评估方法。根据新型铝合金疲劳试验数据，全面分析了Weibull分布形状参数对DFR计算参数的影响；分析标准S-N曲线斜度参数对DFR的影响时，应考虑可靠性寿命的变化，采用低可靠度寿命计算曲线斜度参数可降低Weibull分布形状参数的影响；比较了采用不同分布参数估计值与传统上采用波音公司给定值进行DFR计算的差异。结果表明，对于通过符合性检验的试验数据，使用不同Weibull分布的参数估计值计算得到的DFR都大于按传统方法（用波音公司给定值）的计算结果，其中，三参数Weibull分布计算的DFR最大，至少增加10%以上。     

基于双滤波器的高精度目标跟踪估计方法

针对连续推力的合作航天器，采用双重无迹卡尔曼滤波(DUKF)算法估计其状态和加速度。通过状态滤波器和参数滤波器的配合，提升滤波精度，完成运动状态和参数的估计，从而实现合作目标的运动轨迹跟踪。与合作航天器相比，非合作航天器存在大小未知、发生时刻未知的机动，无法获得加速度，且信息获取和运动状态的估计难度大。针对非合作航天器，基于简化的相对运动方程，结合天基平台获得目标的观测信息，采用两个扩展卡尔曼滤波(EKF)及基于半正焦弦的机动检测策略实现多未知脉冲机动的运动状态的估计。仿真结果表明：相比于无迹卡尔曼滤波（UKF），DUKF在对合作航天器的状态和加速度估计方面具有更快的收敛速度和更高的滤波精度；对于存在未知机动的非合作航天器，通过对比验证机动检测策略与滤波器切换策略相结合的方法的有效性，该方法能够检测到多次机动并且减少误判。     

涡轮基组合动力与火箭的耦合特性分析及匹配优化设计

基于高斯伪谱航迹优化方法，建立了"火箭辅助型涡轮基组合动力"的飞行器/推进系统匹配分析方法，针对地面水平起降、以马赫数5巡航的高超声速飞行器，以巡航航程最远为目标，完成了涡轮基组合动力（TBCC）与火箭的耦合特性分析及匹配优化设计。研究结果表明：对于可行的TBCC方案（起飞推重比为1.0），引入合适推力的火箭有助于提升加速爬升段的总效率并降低质量消耗，且对巡航航程有着一定的提升（4%起飞重量推力火箭可增加航程0.97%）；对于不可行的TBCC方案（起飞推重比为0.8），引入火箭不仅可实现推进系统方案的收敛，且其巡航航程相比可行的TBCC方案最多可增加7.9%。考虑到TBCC较大的"死重"和较低的单位迎面推力对巡航性能的不利影响，结构质量占比为25%的巡航型飞行器建议采用"13%起飞重量推力火箭辅助起飞推重比为0.7的TBCC "推进系统。相比之下，结构质量占比为55%的加速型飞行器建议采用" 5%起飞重量推力火箭辅助起飞推重比为0.98的TBCC"推进系统。     

A progressive approach to predict shot peening process parameters for forming integral panel of Al7050-T7451

In this paper, a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed. Firstly, the invariable parameters in the forming process including shot size, mass flow, peening distance and peening angle are determined according to the empirical and machine type. Then, the optimal value of air pressure for the whole shot peening is selected by the experimental data. Finally, the feeding speed for every shot peening path is predicted by regression equation. The integral panel part with thickness from 2 mm to 5 mm and curvature radius from 3200 mm to 16000 mm is taken as a research object, and four experiments are conducted. In order to design specimens for acquiring the forming data, one experiment is conducted to compare the curvature radius of the plate and stringer-structural specimens, which were peened along the middle of the two stringers. The most striking finding of this experiment is that the outer shape error range is below 3.9%, so the plate specimens can be used in predicting feeding speed of the integral panel. The second experiment is performed and results show that when the coverage reaches the limit of 80%, the minimum feeding speed is 50 mm/s. By this feeding speed, the forming curvature radius of the specimens with different thickness from the third experiment is measured and compared with the research object, and the optimal air pressure is 0.15 MPa. Then, the plate specimens with thickness from 2 mm to 5 mm are peened in the fourth experiment, and the measured curvature radius data are used to calculate the feeding speed of different shot peening path by regressive analysis method. The algorithm is validated by forming a test part and the average deviation is 0.496 mm. It is shown that the approach can realize the forming of the integral panel precisely.     

Relevant experience learning: A deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.