控制策略对无轴承开关磁阻电机

分析了无轴承开关磁阻电机(BSRM)的3种负载控制策略(方波控制策略、最小磁势控制策略和平均悬浮力控制策略),以及每种策略下由悬浮力方程和转矩方程求解超前角和绕组电流的方法。由于控制对象和求解过程所引入约束条件的不同,即便相同负载状况时,3种控制策略求取的超前角和绕组电流结果也不同。利用瞬态有限元法获得了电机的动态磁密分布,并研究了3种控制策略下电机分别表现出的磁场特性。通过双频法分离铁损,分别计算了3种控制策略在不同转速时的电机涡流损耗和磁滞损耗,给出了电机铁心各部分损耗随转速变化的关系,由此得到了3种控制策略对铁心损耗的影响。     

振梁加速度计温度误差模型及补偿技术研究

研究了振梁加速度计温度误差建模及补偿问题。分析了振梁加速度计受温度影响的主要因素,应用最小二乘算法得到零偏、标度因数与温度的多项式拟合关系,进而得到振梁加速度计的温度误差模型。利用该误差模型进行温度补偿。结果表明：经过温度建模和补偿,振梁加速度计精度有明显的提高。     

基于FEM的惯导系统电磁屏蔽效能仿真分析

机箱电磁屏蔽效能的优劣影响惯导系统的电磁兼容性能,而孔缝是造成电磁泄露的重要原因。本文运用FEM分析计算软件Ansoft HFSS,分析了缝隙的形状和不同组合对机箱屏蔽效能和谐振频率的影响,提出了改进措施提高屏蔽效能。并对惯导系统电子设备机箱的设计进行了比较,为选择屏蔽效能较好的设计提供了依据。     

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.     

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.     

Designing method of acceleration and deceleration control schedule for variable cycle engine

Studies show that different geometries of a Variable Cycle Engine (VCE) can be adjusted during the transient stage of the engine operation to improve the engine performance. However, this improvement increases the complexity of the acceleration and deceleration control schedule. In order to resolve this problem, the Transient-state Reverse Method (TRM) is established in the present study based on the Steady-state Reverse Method (SRM) and the Virtual Power Extraction Method (VPEM). The state factors in the component-based engine performance models are replaced by variable geometry parameters to establish the TRM for a double bypass VCE. Obtained results are compared with the conventional component-based model from different aspects, including the accuracy and the convergence rate. The TRM is then employed to optimize the control schedule of a VCE. Obtained results show that the accuracy and the convergence rate of the proposed method are consistent with that of the conventional model. On the other hand, it is found that the new-model-optimized control schedules reduce the acceleration and deceleration time by 45% and 54%, respectively. Meanwhile, the surge margin of compressors, fuel–air ratio and the turbine inlet temperature maintained are within the acceptable criteria. It is concluded that the proposed TRM is a powerful method to design the acceleration and deceleration control schedule of the VCE.     

Integration of high-fidelity model of forward variable area bypass injector into zero-dimensional variable cycle engine model

Forward Variable Area Bypass Injector (FVABI) is one of key components which contributes to modulate the cycle parameters of Variable Cycle Engine (VCE) under various operation conditions. The modeling method of zero-dimensional FVABI was reviewed and its deficiency was analyzed based on FVABI flow characteristic. In order to improve the accuracy of VCE performance simulation, the high-fidelity modeling method of FVABI was developed based on its working characteristics. Then it was coupled with the zero-dimensional VCE model and the multi-level VCE model was built. The results indicate that the geometric and aerodynamic parameters can affect the interaction between the two airflows and the zero-dimensional FVABI model is too simple to predict the component performance accurately, especially when the FVABI inner bypass is chocked. Based on the performance curves for single bypass mode and the regression model of multi-scale support vector regression for double bypass mode, the high-fidelity model can predict FVABI performance accurately and rapidly. The integration of high-fidelity FVABI model into zero-dimensional VCE model can be done by adjusting iterative variables and balance equations. The multi-level model has good convergence and it can predict VCE performance when the FVABI inner bypass is chocked.     

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.     

基于矩化GO法的某型电动飞机扰流板系统可靠性

扰流板冗余系统提升了新能源电动飞机的可靠性。采用GO法建立电动飞机扰流板系统可靠性模型,设计SIMULINK仿真界面,针对操作符11给出一种新的运算模块。针对扰流板冗余系统设计一种矩化操作符,给出运算流程,有效减少传统GO法在冗余系统建模工作量,利用故障树分析法对结果进行验证。结果表明,本文提出的方法切实可行,为新能源电动飞机扰流板系统可靠性分析提供了新的思路。     

几何尺寸约束的超燃冲压发动机推力喷管设计

针对机体/推进系统一体化要求,提出了一种几何尺寸约束的超燃冲压发动机推力喷管设计方法,以实现在满足几何约束条件下优化喷管的气动性能。采用数值模拟对提出的设计方法展开了研究,在此过程中,首先校核了数值方法有效性并确定了网格分辨率。进一步就喷管设计过程中2个关键设计因子——比例因子和非对称因子开展了影响研究。最后为验证本文提出的设计方法的有效性和优越性,将其与典型截短设计方法进行对比分析。结果表明,相比截短方法,本文提出的设计方法不仅能够获得满足几何约束的喷管,同时可使喷管推力系数、升力和俯仰力矩分别提升33.36%、265.75%和37.21%。此外,本文提出的设计方法还可以通过调整设计因子来获得不同气动性能的喷管,进而满足应用过程中的实际需求。