流量预测下智能寻优门限值的AOS帧生成算法

针对AOS空间通信系统的自适应帧生成算法帧生成时间门限值需要人工设置问题,提出一种流量预测下智能寻优门限值的AOS帧生成算法.该算法使用小波神经网络对ON/OFF模型产生的自相似流量进行预测,根据预测流量计算复用效率和平均包时延,使用人工鱼群算法对建立的综合评价函数进行智能寻优,从而得到帧生成算法的最佳门限值.该算法可...     

功能分析与失效物理结合的可靠性预计方法

 可靠性预计是产品设计、研发过程中的重要工作,全面准确的可靠性预计可以评价产品的可靠性水平,也可以为设计提供信息,指导设计。全面分析总结当前电子设备可靠性预计相关技术方法,以当前基于失效物理(POF)技术的系统可靠性预计方法中,并未考虑产品功能组成关系的缺陷为突破点,建立了一种以失效物理分析为基础,综合考虑电路功能组成关系的电子设备可靠性预计方法。该方法从电路功能出发,通过灵敏度仿真和主成分分析两种方法,确定对电路性能起主要影响的关键单元,再通过失效物理分析或统计规律明确单元的失效概率分布,通过混合分布获得系统的分布,得到系统可靠性指标。最后以某航空机电产品的电源电路为案例,对本预计方法进行验证。     

Development cost prediction of general aviation aircraft projects with parametric modeling

The study of the development cost of general aviation aircraft is limited by small samples with many cost-driven factors. This paper investigates a parametric modeling method for prediction of the development cost of general aviation aircraft. The proposed technique depends on some principal components, acquired by utilizing P value analysis and gray correlation analysis. According to these principal components, the corresponding linear regression and BP neural network models are established respectively. The feasibility and accuracy of the P value analysis are verified by comparing results of model fitting and prediction. A sensitivity analysis related to model precision and suitability is discussed in detail. Results obtained in this study show that the proposed method not only has a certain degree of versatility, but also provides a preliminary prediction of the development cost of general aviation aircraft.     

Analysis of GNSS clock prediction performance with different interrupt intervals and application to real-time kinematic precise point positioning

Continuous and timely real-time satellite orbit and clock products are mandatory for real-time precise point positioning (RT-PPP). Real-time high-precision satellite orbit and clock products should be predicted within a short time in case of communication delay or connection breakdown in practical applications. For prediction, historical data describing the characteristics of the real-time orbit and clock can be used as the basis for performing the prediction. When historical data are scarce, it is difficult for many existing models to perform precise predictions. In this paper, a linear regression model is used to predict clock products. Seven-day GeoForschungsZentrum (GFZ) final clock products sampled at 30 s are used to analyze the characteristics of GNSS clocks. It is shown that the linear regression model can be used as the prediction model for the satellite clock products. In addition, the accuracy of the clock prediction for different satellites are analyzed using historical data with different periods (such as 2 and 10 epochs). Experimental results show that the accuracy of the clock with the linear regression prediction model using historical data with 10 epochs is 1.0 ns within 900 s. This is higher accuracy than that achieved using historical data of 2 epochs. Finally, the performance analysis for real-time kinematic precise point positioning (PPP) is provided using GFZ final clock prediction results and state space representation (SSR) clock prediction results when communication delay or connection breakdown occur. Experimental results show that the positioning accuracy without prediction is better than that with prediction in general, whether using the final clock product or the SSR clock product. For the final clock product, the positioning accuracy in the north (N), east (E), and up (U) directions is better than 10.0 cm with all visible GNSS satellites with prediction. In comparison, the 3D positioning accuracy of N, E, and U directions with visible GNSS satellites whose prediction accuracy is better than 0.1 ns using historical data of 10 epochs is improved from 15.0 cm to 7.0 cm. For the SSR clock product, the positioning accuracy of N, E, and U directions is better than 12.0 cm with visible GNSS satellites with prediction. In comparison, the 3D positioning accuracy of N, E, and U directions with visible GNSS satellites whose prediction accuracy is better than 0.1 ns using historical data of 10 epochs is improved from 12.0 cm to 9.0 cm.     

Real-time sensor fault detection and isolation for LEO satellite attitude estimation through magnetometer data

Attitude estimation is a critical component of the Attitude Determination and Control System (ADCS) of any satellite. It is used to convert the sensor observation data to an estimated attitude using filtering algorithms. However, in the presence of sensor faults, the ADCS fails to achieve the desired attitude accuracy. In this paper, the Fault Tolerant Extended Kalman Filter (FTEKF) is proposed to handle this imperfection. In accordance, various filtering steps are included in the FTEKF design to enhance both attitude estimation and sensor fault detection. The developed algorithm can detect and isolate any unexpected sensor faults in real time, which provides a reliable attitude estimation. A comparative study with the classical and robust Kalman filters is performed through numerical simulations in order to validate the effectiveness of the adopted filter in case of magnetometer fault data.     

Effects of splined shaft bending rigidity on cylinder tilt behaviour for high-speed electro-hydrostatic actuator pumps

High-speed axial piston pumps are hydraulic power supplies for electro-hydrostatic actuators(EHAs). The efficiency of a pump directly affects the operating performance of an EHA, and an understanding of the physical phenomena occurring in the cylinder/valve plate interface is essential to investigate energy dissipation. The effects of the splined shaft bending rigidity on the cylinder tilt behaviour in an EHA pump need to be considered, because the deflection and radial expansion of a steel shaft rotating at a high speed cannot be ignored. This paper proposes a new mathematical model to predict the cylinder tilt behaviour by establishing a quantitative relationship between the splined shaft deflection, the cylinder tilt angle, and the tilt azimuth angle. The moments exerted by the splined shaft are included in the equilibrium equation of the cylinder. The effects of solid and hollow splined shafts equipped in an EHA pump prototype are compared at variable speeds of 5000–10,000 r/min. With a weight saving of 29.7%, the hollow shaft is experimentally found to have almost no influence on the volumetric efficiency, but to reduce the mechanical efficiency by 0.6–2.4%. The results agree with the trivial differences of the simulated central gap heights of the interface between the two shafts and the enlargement of the simulated tilt angles by the hollow shaft. The findings could guide designs of the cylinder/valve plate interface and the splined shaft to improve both the efficiency and power density of an EHA pump.     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).     

变几何变工质涡轮性能预测及变工况性能计算

为适应涡轮在变工质，大变工况条件下的性能预测，研究了工质的性质，涡轮各种损失与涡轮流通通道内的临界堵塞流动，变几何涡轮的性能计算方法。这些研究的结果是变几何变工质涡轮理论分析的有用工具。     

舰载机理想着舰点垂直运动的预估与补偿

理想着舰点的垂直运动是影响着舰精度和安全的一个主要因素,因此必须加强舰载机对理想着舰点垂直运动的同步跟踪能力.为此,提出对理想着舰点垂直运动的位置和速度信号进行预估和补偿的方法,将垂直运动的位置和速度信号经过预估和补偿后分别引入到纵向自动着舰引导系统和飞控系统的垂向速度通道中,使得舰载机可以准确跟踪理想着舰点的垂直运动,以减小甲板运动对着舰的影响.针对不同海况条件,对设计的补偿器和预估器进行仿真验证,并与其他方法进行比较.结果表明本文提出的理想着舰点垂直运动预估与补偿方法可有效地补偿由甲板运动引起的着舰误差,显著提高了着舰的安全性和精确性.     

CFD predictions of LBO limits for aero-engine combustors using fuel iterative approximation

Lean blow-out (LBO) is critical to operational performance of combustion systems in propulsion and power generation. Current predictive tools for LBO limits are based on decadesold empirical correlations that have limited applicability for modern combustor designs. According to the Lefebvre’s model for LBO and classical perfect stirred reactor (PSR) concept, a load parameter (LP) is proposed for LBO analysis of aero-engine combustors in this paper. The parameters contained in load parameter are all estimated from the non-reacting flow field of a combustor that is obtained by numerical simulation. Additionally, based on the load parameter, a method of fuel iterative approximation (FIA) is proposed to predict the LBO limit of the combustor. Compared with experimental data for 19 combustors, it is found that load parameter can represent the actual combustion load of the combustor near LBO and have good relativity with LBO fuel/air ratio (FAR). The LBO FAR obtained by FIA shows good agreement with experimental data, the maximum prediction uncertainty of FIA is about ±17.5%. Because only the non-reacting flow is simulated, the time cost of the LBO limit prediction using FIA is relatively low (about 6 h for one combustor with computer equipment of CPU 2.66 GHz · 4 and 4 GB memory), showing that FIA is reliable and efficient to be used for practical applications.