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.     

机器人铣削加工动态变形误差预测与在线感知

航天舱段零件的机器人铣削加工中,强激励铣削力作用下的动态加工变形是影响加工精度的重要因素,复杂工况下动态误差的离线理论预测难以准确反映实际变形误差。为此,考虑加工过程中复杂工况下不确定性因素对动态变形误差的影响,本文提出了融合在线测量位姿、力数据的动态误差预测方法。首先,建立动态变形误差理论预测模型,引入加工误差预测偏差项;其次,结合有限实验数据对预测偏差的影响因素进行主成分分析(PCA),提取信息贡献率大的主成分作为特征向量,利用支持向量机(SVM)建立预测偏差关于特征向量的动态误差预测回归模型;最后,构建基于机器人关节位置和切削力测量数据的加工动态误差预测模型,实现动态误差在线感知,为加工精度控制提供数据基础。     

Model based Computerized Ionospheric Tomography in space and time

Reconstruction of the ionospheric electron density distribution in space and time not only provide basis for better understanding the physical nature of the ionosphere, but also provide improvements in various applications including HF communication. Recently developed IONOLAB-CIT technique provides physically admissible 3D model of the ionosphere by using both Slant Total Electron Content (STEC) measurements obtained from a GPS satellite - receiver network and IRI-Plas model. IONOLAB-CIT technique optimizes IRI-Plas model parameters in the region of interest such that the synthetic STEC computations obtained from the IRI-Plas model are in accordance with the actual STEC measurements. In this work, the IONOLAB-CIT technique is extended to provide reconstructions both in space and time. This extension exploits the temporal continuity of the ionosphere to provide more reliable reconstructions with a reduced computational load. The proposed 4D-IONOLAB-CIT technique is validated on real measurement data obtained from TNPGN-Active GPS receiver network in Turkey.     

Remaining useful life prediction of aircraft lithium-ion batteries based on F-distribution particle filter and kernel smoothing algorithm

As an emergency and auxiliary power source for aircraft, lithium (Li)-ion batteries are important components of aerospace power systems. The Remaining Useful Life (RUL) prediction of Li-ion batteries is a key technology to ensure the reliable operation of aviation power systems. Particle Filter (PF) is an effective method to predict the RUL of Li-ion batteries because of its uncertainty representation and management ability. However, there are problems that particle weights cannot be updated in the prediction stage and particles degradation. To settle these issues, an innovative technique of F-distribution PF and Kernel Smoothing (FPFKS) algorithm is proposed. In the prediction stage, the weights of the particles are dynamically updated by the F kernel instead of being fixed all the time. Meanwhile, a first-order independent Markov capacity degradation model is established. Moreover, the kernel smoothing algorithm is integrated into PF, so that the variance of the parameters of capacity degradation model keeps invariant. Experiments based on NASA battery data sets show that FPFKS can be excellently applied to RUL prediction of Li-ion batteries.     

A new SINS/GPS sensor fusion scheme for UAV localization problem using nonlinear SVSF with covariance derivation and an adaptive boundary layer

The state estimation strategy using the smooth variable structure filter(SVSF) is based on the variable structure and sliding mode concepts. As presented in its standard form with a fixed boundary layer limit, the value of the boundary layer width is not precisely known at each step and may be selected based on a priori knowledge. The boundary layer width reflects the level of uncertainty in the model parameters and disturbance characteristics, where large values of the boundary layer width lead to robustness without optimality and small values of the boundary layer width provide optimality with poor robustness. As a solution and to overcome these limitations, an adaptive smoothing boundary layer is required to achieve greater robustness and suitable accuracy.This adapted value of the boundary layer width is obtained by minimizing the trace of the a posteriori covariance matrix. In this paper, the proposed new approach will be considered as another alternative to the extended Kalman filters(EKF), nonlinear H1 and standard SVSF-based data fusion techniques for the autonomous airborne navigation and self-localization problem. This alternative is based on strapdown inertial navigation system(SINS) and GPS data using the nonlinear SVSF with a covariance derivation and adaptive boundary layer width.Furthermore, the full mathematical model of the SINS/GPS navigation system considering the unmanned aerial vehicle(UAV) position, velocity and Euler angle as well as gyro and accelerometer biases will be used in this paper to estimate the airborne position and velocity with better accuracy.     

基于eN-数据库方法复杂构型飞机转捩预测

为探索边界层转捩对大型运输机在起降条件和有较大层流区的巡航条件下的气动力精确计算问题，通过在三维RANS求解器中引入eN-数据库方法来预测飞行器表面的转捩位置，并探索转捩对气动力的影响规律。方法与目前流行的基于间歇因子控制方程的转捩预测方法相比，具有计算效率高、易于工程应用、且考虑TS不稳定性转捩因素的特点。在此基础上，通过计算NASA梯形翼来分析起降构型条件下气动力受转捩影响的规律，并通过计算DLR－F6翼身组合体来探索三维构型在巡航条件下的气动力精度。使用eN-数据库转捩判断方法的计算结果与实验值吻合较好，验证了所构建的基于RANS求解器的eN-数据库转捩预测方法的有效性，并为大型运输机气动力精确计算提供了分析工具。     

跨声速轴流压气机失速边界预测方法

研究了跨声速、单级设计压比为1.82的Stage 35轴流压气机与单级设计压比为2.05的Stage37轴流压气机在不同转速下的特性.在HARIKA原型程序基础上,改进了其叶排效率、落后角、理论能头计算模型,采用了两种轴流压气机失速边界的预测方法,第1种为HARIKA原型程序的分离流量预测方法,第2种为Koch所提出的失速静压升系数预测方法,所得特性计算结果与实验点吻合较好,Stage 35设计点效率的误差由原型的3.9%降低到改进后的1.5%,Stage 37设计点效率的误差由3.1%降低到1.9%.两种预测方法对失速边界流量的预测误差最小分别可达1.3%与1.6%,表明两种失速边界预测方法都是可行的.     

某型燃气轮机进气系统内流场的数值分析

为了揭示燃机进气系统内的流动特性,并为进气系统的性能预测、优化设计提供理论依据,以某型燃气轮机的进气系统为研究对象,基于混合网格的SIMPLE算法和标准的k-ε湍流模型,运用FLUENT软件对燃气轮机进气系统内的流场进行数值模拟,并从压力分布、气流角分布2个方面进行分析。结果表明:对气动性能产生重要影响的损失区域主要集中在进气外环上方和支板附近,蜗壳内锥的绕流分离也是损失源之一。应进行尽可能消除进气外环上方的气流分离,同时将绕流分离控制在一定范围内的优化设计和改进。