An internal power angle control strategy for high-speed sensorless brushless DC motors

High-speed Brushless DC Motors (BLDCMs) usually adopt a sensorless control strategy and operate in three-phase six-state drive mode. However, the sampling errors of the rotor position and the driving method increase the Internal Power Angle (IPA), resulting in a decrease in the efficiency of the system. Conventional IPA reduction strategies are either sensitive to motor parameters, or ignore diode freewheeling during the commutation process, or require additional current sensors. In this paper, a new strategy to reduce the IPA is proposed. Firstly, a Zero-Crossing Point (ZCP) detection method for the back-EMF without filter is proposed to reduce the sampling errors of the rotor position. Secondly, the relationship between the non-energized terminal voltage and the ZCP of the corresponding back-EMF is analyzed. The non-energized terminal voltage that has completed the diode freewheeling is divided into two triangles by half of the bus voltage. When the IPA is suppressed, the areas of the two triangles are equal. Thirdly, an advanced angle for reducing the IPA is obtained through a PI regulator which can eliminate the deviation between the two areas. Finally, both a simulation model and an experimental circuit are built to verify the proposed control strategy.     

Breakthrough detection in electrochemical discharge drilling to enhance machining stability

Film cooling holes are widely used in aero-engine turbine blades. These blades feature large numbers of holes with complex angles and require a high level of surface integrity. Electrochemical discharge drilling(ECDD) combines the high efficiency of electrical discharge drilling(EDD) with high quality of electrochemical drilling(ECD). However, due to the existence of a variety of energy for material removal, accurate and timely detection of breakthroughs is fraught with difficulties. An insuffic...     

基于数字孪生的航天电推进器优化设计方法

针对以引力波探测为代表的空间科学任务和以“国网星座计划”为代表的商用卫星网络任务对推进器的特殊需求，本文提出了一种基于数字孪生的推进器优化设计方法。该方法首先建立由机理模型模块和测试数据集模块组成的数字孪生体。机理模型模块依据推进器的物理过程建立模型，对难以测量的数据进行仿真模拟；测试数据集模块通过实验对推进器进行测试，依靠测试数据建立可测参数的数学模型。将数字孪生体与实验进行对比，通过对比结果反馈调节机理模块从而不断提高孪生体的准确性，最终为优化设计提供依据。结果表明：(1)该方法能够构建微波离子推进器的数字孪生体；(2)该数字孪生体的预测结果存在一定差异，通过分析发现该差异与机理模型的精细度以及测试数据集的数据量有关。     

基于双回路控制的电动负载模拟器研究

电动舵机负载模拟器可在实验条件下模拟飞行器飞行过程中舵机受到的空气动力铰链力矩,是半实物仿真的重要设备。在介绍电动负载模拟器研究进展并指出目前伺服系统研究所面临的难题的基础上,以改善系统动态频响为目标,设计基于双回路电机的加载方案并建立双回路系统的数学模型;通过系统仿真,分析系统输出力矩对指令力矩的跟踪能力,验证数学模型的准确性。结果表明:双回路方案有效地抑制了系统多余力,其工作频带在舵机扰动的情况下仍可达35 Hz,为电动负载模拟器提供了一种频带更高、匹配性更好的实现方案。     

电动膨胀循环液氧甲烷变推力发动机动态特性研究

电动泵压式液体火箭发动机受到了广泛的关注，然而电池有限的输出功率和过于沉重的质量成为限制电动泵压式发动机发展的重要因素。为此本文提出了一种电机驱动燃料泵和涡轮驱动氧泵的电动膨胀循环液体火箭发动机方案，并着重研究了该型发动机的动态响应特性。首先给出了20 kN级电动膨胀循环发动机的技术指标和部组件参数，进一步基于AMESim平台建立了全系统动力学模型，验证了方案的可行性和部组件动力学模型的准确性，并深入研究了单点工况和调节工况的动态响应特性。结果表明，针对启动过程而言，涡轮泵调整时间较电动泵长，这降低了系统响应速度，但工况越高，系统响应速度越快；高工况启动时，甲烷在冷却通道内的剧烈相变和跨临界状态的不连续物性相互耦合易引发系统振荡；就调节过程而言，推力调节时普遍存在超调或凹坑现象，且系统在两相同工况之间调节时，正调响应速度快于负调，这也导致阶跃幅值相等条件下的系统调整时间随目标工况升高而缩短。     

An atmosphere-breathing propulsion system using inductively coupled plasma source

CubeSats have attracted more research interest recently due to their lower cost and shorter production time. A promising technology for CubeSat application is atmosphere-breathing electric propulsion, which can capture the atmospheric particles as propulsion propellant to maintain long-term mission at very low Earth orbit. This paper designs an atmosphere-breathing electric propulsion system for a 3 U CubeSat, which consists of an intake device and an electric thruster based on the inductively coupled plasma. The capture performance of intake device is optimized considering both particles capture efficiency and compression ratio. The plasma source is also analyzed by experiment and simulation. Then, the thrust performance is also estimated when taking into account the intake performance. The results show that it is feasible to use atmosphere-breathing electric propulsion technology for CubeSats to compensate for aerodynamic drag at lower Earth orbit.     

Time resolved study of laser triggered electric discharge spark in atmosphere: Machine learning approach

In this paper we analyze the possibilities of using machine learning algorithms for analysis of optical spectra of electric discharge spark in atmosphere. Breakdown in air can be initiated by intense laser pulse, making plasma which has a significant electrical conductivity. The formed plasma can be further maintained by electric current obtained from capacitor discharge. In such a case the capacitor voltage can be much lower than the striking voltage (the voltage needed to initiate the electric breakdown in air). Present setup has timing precision and low jitter of fast laser and arbitrary high energies corresponding to capacitance and voltage to which the capacitor is charged. We have used a streak camera equipped with a spectrograph to analyze optical emission of plasma obtained in this way. Q-switched Nd:Yag laser was used to achieve the initial breakdown in air. Machine learning methods were used in order to classify optical spectra of plasmas with different electron temperatures obtained with different excitation energies. We have shown that, instead of using the usual way of identifying the spectral peaks and calculating their intensity ratio, it is possible to train the computer software to recognize the spectra corresponding to different electron temperatures. Principal component analysis was used to reduce the dimensionality of problem. We present possibilities of plasma electron temperature estimation based on several clustering algorithms.