固体发动机喷管延伸锥展开前级间分离的热环境分析

通过FLUENT流场计算软件,采用RNGK k-ε湍流模型,针对带有延伸喷管的多级固体发动机级间热分离的热环境进行了数值分析。研究表明,延伸喷管尾流受到连接筒、前封头、延伸段的阻碍后,其流动特征变化显著,尤其在一级前封头的影响下形成回流,并在喷管内形成激波。计算得到的热环境参数,对发动机的热防护设计具有一定的参考价值。     

一种液体火箭发动机试验台螺纹拧紧力矩量化控制方法

针对液体火箭发动机试验台工艺装配可靠性提升的需求,提出一种螺纹拧紧力矩量化控制方法。利用非试验和试验环境的力矩值统计与分析,确定液体火箭发动机试验台螺纹拧紧力矩值区间;通过拧紧力矩强度理论计算,验证力矩值区间的合理性;结合试验台螺纹空间位置的具体特点,确定适用不同空间位置的力矩量化控制方法和流程,并在试验中进行了应用。应用结果表明,本文提出的方法可有效提高发动机试验工艺的可靠性。     

Vibration signal-based chatter identification for milling of thin-walled structure

In high speed milling aeronautical part, tool condition monitoring(TCM) is very important, because it is prone to get a chatter owing to the low stiffness of thin-walled structures. And the TCM is key technology for automated machining. In this paper, aiming to chatter monitoring in thin-walled structure milling, a variational mode decomposition – energy distribution(VMD-ED)method is proposed to improve the identification accuracy. And a moving average root mean square – mean value(MARMS-MV) ide...     

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating machinery is vital in manufacturing system, which can prevent catastrophic failure and reduce financial losses. Recently, Deep Learning (DL)-based fault diagnosis method becomes a hot topic. Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically. This paper develops a fault diagnosis method using CNN for InfRared Thermal (IRT) image. First, IRT technique is utilized to capture the IRT images of rotating machinery. Second, the CNN is applied to extract fault features from the IRT images. In the end, the obtained features are fed into the Softmax Regression (SR) classifier for fault pattern identification. The effectiveness of the proposed method is validated using two different experimental data. Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.     

A new bearing fault diagnosis method based on modified convolutional neural networks

Fault diagnosis is vital in manufacturing system. However, the first step of the traditional fault diagnosis method is to process the signal, extract the features and then put the features into a selected classifier for classification. The process of feature extraction depends on the experimenters’ experience, and the classification rate of the shallow diagnostic model does not achieve satisfactory results. In view of these problems, this paper proposes a method of converting raw signals into two-dimensional images. This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process. And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network (CNN), which can automatically accomplish the process of the feature extraction and fault diagnosis. The effect of this method is verified by bearing data. The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed. The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.     

Real-time clock comparison and monitoring with multi-GNSS precise point positioning: GPS,GLONASS and Galileo

The precise point positioning (PPP) technique is widely used in time and frequency applications. Because of the real-time service (RTS) project of the International GNSS Service, we can use the PPP technique for real-time clock comparison and monitoring. As a participant in the RTS, the Centre National d’Etudes Spatiales (CNES) implements the PPPWIZARD (Precise Point Positioning with Integer and Zero-difference Ambiguity Resolution Demonstrator) project to validate carrier phase ambiguity resolution. Unlike the Integer-PPP (IPPP) of the CNES, fixing ambiguities in the post-processing mode, the PPPWIZARD operates in the real-time mode, which is also called real-time IPPP (RT-IPPP). This paper focuses on applying the RT-IPPP for real-time clock comparison and monitoring. We review the principle of real-time clock comparison and monitoring, and introduce the methodology of the RT-IPPP technique. The observations of GPS, GLONASS and Galileo were processed for the experiments. Five processing modes were provided in the experiment to analyze the benefits of ambiguity resolution and multi-GNSS. In the clock comparison experiment, the average reduction ratios of standard deviations with respect to the G PPP mode range from 9.7% to 35.0%. In the clock monitoring experiment, G PPP mode can detect clock jumps whose magnitudes are larger than 0.9 ns. The RT-IPPP technique with GRE PPP AR (G) mode allows for the detection of any clock jumps larger than 0.6 ns. For frequency monitoring, G PPP mode allows detection of frequency changes larger than 1.1 × 10⁻¹⁴. When the RT-IPPP technique is applied, monitoring with GRE PPP AR (G) mode can detect frequency changes larger than 6.1 × 10⁻¹⁵.     

Characterization of ionospheric amplitude scintillations using wavelet entropy detrended GNSS data

The extensive monitoring networks of Global Navigation Satellite System (GNSS) ionospheric scintillation have been established to continuously log observation data. Further, the amplitude scintillation index and the phase scintillation index, which are derived from scintillation observations, are anticipated to accommodate the accuracy requirement of both the user level and the monitoring station level. However, raw scintillation observations essentially measure superposed waveform impairments of GNSS signals propagating through ionosphere and troposphere. It implies that fluctuations of raw scintillation observations are caused by multiple factors from the entire radio propagation environment. Hence, it is crucial to characterize ionospheric scintillations from GNSS observation data. And the characterization is implemented through extracting fluctuations of raw observations merely induced by ionospheric scintillations. Designed to address this problem by means of Fourier filtering detrending, the present work investigates the influence of varying detrending cutoff frequencies on wavelet statistical energy and wavelet entropy distributions of scintillation data. It consequently derives criteria on the optimum detrending cutoff frequency for three types of raw amplitude scintillation data, which are classified by their wavelet energy distributions. Results of the present work verify that detrending with specific optimum cutoff frequencies rather than the fixed and universally applicable one renders the validity and credibility of characterizing ionospheric scintillations as the part of GNSS observation fluctuations purely induced by ionosphere electron density irregularities whose scale sizes are comparable with or smaller than the Fresnel scale.     

A novel control method for turboshaft engine with variable rotor speed based on the Ngdot estimator through LQG/LTR and rotor predicted torque feedforward

In order to compensate for the disturbance of wide variation in rotor demanded torque on power turbine speed and realize the fast response control of turboshaft engine during variable rotor speed, a cascade PID control method based on the acceleration estimator of gas turbine speed (Ngdot) and rotor predicted torque feedforward is proposed. Firstly, a two-speed Dual Clutch Transmission (DCT) model is applied in the integrated rotor/turboshaft engine system to achieve variable rotor speed. Then, an online estimation method of Ngdot based on the Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) is proposed for power turbine speed cascade control. Finally, according to the cascade PID controller based on Ngdot estimator, a rotor demanded torque predicted method based on the Min-batch Gradient Descent-Neural Network (MGD-NN) is put forward to compromise the influence of rotor torque interference. The simulation results show that compared with cascade PID controller based on Ngdot estimator and the one combined with collective pitch feedforward control, the novel control method proposed can reduce the overshoot of power turbine speed by more than 20%, which possesses faster response, superior dynamic effect and satisfactory robustness performance. The control method proposed can realize the fast response control of turboshaft engine with variable rotor speed better.     

航空发动机全飞行包线稳态划分方法研究

航空发动机在鲁棒控制器设计过程中存在飞行包线区域难以系统划分的问题,为此,提出基于推力耗油率特性和基于动压耗油率特性的航空发动机飞行包线划分法。根据某型涡扇发动机在全包线范围内稳态工作时的推力、耗油率及动压特性,结合大气条件的客观规律,通过两种划分方法将飞行包线划分为65 个区域,用每个区域对应标称点的参数代替其周围小偏差区域和边界点参数。通过对该发动机全包线内各区域标称点与边界点参数的对比,证明两种方法均对全飞行包线划分有效,可为后续航空发动机控制器设计提供理论基础。     

三轴承矢量喷管红外辐射特性

针对涡扇发动机用三轴承矢量喷管（3BSN）,通过数值模拟的方法研究了在巡航和垂直起降（VTOL）状态即非矢量和90°矢量状态下三轴承矢量喷管的红外辐射空间分布特征,并分析了其影响机理。结果表明,在非矢量状态下由于特殊的几何型面使得喷管下壁面出现了局部高温区,喷流形状也变成椭圆锥形,造成垂直探测面上正探测角壁面红外辐射较负探测角最大增加44.6%,水平探测面上的燃气红外辐射大于垂直探测面;90°矢量状态下,由于偏转的喷管结构对前端高温部件的遮挡,总辐射峰值仅为非矢量状态的43.3%;喷管90°矢量偏转使得远离曲率中心一侧的气流速度降低温度升高,喷管外侧出现了大范围的局部高温区,导致垂直探测面负探测角范围的壁面辐射大于正探测角范围,最大相对差值达到71.9%。喷管偏转也遮挡了部分喷管内部的高温燃气,垂直探测面负探测角的仅能覆盖喷管出口处的高温燃气区域,明显小于正探测角,造成燃气辐射出现了20%的最大相对差值。