Improved predictions of equatorial TEC for real time applications

It is important to use models developed specifically for the equatorial ionospheric estimation for real-time applications, particularly in Satellite Navigation. This work demonstrates a methodology for improved predictions of VTEC in real time using the model developed for the equatorial ionosphere by the authors. This work has been done using TEC data of the low solar activity period of 2005 obtained using dual frequency GPS receivers installed under the GAGAN project of ISRO. For the purpose, the model is first used in conjunction with Kriging technique. Improvement in accuracy is observed when compared with the estimations from the model alone using the measurements as true reference. Further improvement is obtained by Bayesian combination of these estimates with independent Neural Network based predictions. Statistical performance of improvement is provided. An improvement of ∼1 m in confidence level of estimation of VTEC is obtained.     

振动传感器的微加速度动态校准

阐述了高灵敏度振动传感器在超低频微小加速度下进行动态校准的校准装置工作原理、校准方法 ,分析了其校准不确定度。该装置采用曲率半径为 10m的虚拟摆结构 ,从较强的环境干扰噪声中利用数字滤波技术成功地拾检微弱振动信号 ,使微小标准加速度获得复现。该装置可在 (0 .0 0 1～ 0 .1)Hz超低频率范围内和 (1× 10 -6～ 1× 10 -2 )m s2 微小加速度范围内实现高准确度激光绝对校准和比较法校准 ,其校准不确定度达 0 .5%。     

凸轮弹性连杆复合机构的动力学修改

本文以胶印机递纸机构为研究对象，该机构为一两自由度凸轮连杆复合机构。在建立该机构运动弹性动力学（ＫＥＤ）有限元分析模型和现场袂测固有频率的基础上，提出了一种利用实 固有频率数据修正复合机构有限元动力模型的方法。其基本思想是利用机构各个运动位置上的固有频率实测值与有限元模型的计算值之间的加权误差建立目标函数，然后利用优化方法使目标函数极小化，从而求出有限元模型参数的修正值。计算结果表明该方法是简便有     

基于电流注入法的加速度表测试方法研究

针对不同结构的加速度表 ,从理论上研究了利用电流注入法模拟加速度表运动特性的方法 ,研究了加速度表接入闭环测试系统后的相关问题 ,为加速度表的动态测试提供了新的方法     

现代飞机俯仰跟踪时诱发振荡预测分析

根据飞机纵向ＰＩＯ的特点，提出了预测分析ＰＩＯ所用的人－机闭环系统数学模型，其中飞机采用等效系统数学模型，驾驶员操纵采用ＭｃＲｕｅｒ模型，参数按地面人－机模拟飞行跟踪试验确定，集中介绍了四种飞机纵向ＰＩＯ预测准则，Ｓｍｉｔｈ－Ｇｅｄｄｅｓ准则，修正带宽准则，增益，相位裕度准则，Ｎｅａｌ－Ｓｍｉｔｈ原则，并提供了相应的计算方法。     

多级最优控制问题的提法与求解变分法的应用

本文讨论多级最优控制问题,给出该问题的数学提法以及变分法求解原理。特别,详细研究了级间分离时刻的连接条件。本文的工作为解决多级最优控制问题提供了途径。     

结构比强度及其在优化设计中的应用

本文从结构重量角度推导了比强度(含比模量)表达式,分析了对同一材料不同比强度的关系,给出了比强度在结构动态分析中及结构优化设计中的应用。     

Lift coefficient prediction at high angle of attack using recurrent neural network

In this paper, identification of dynamic stall effect of rotor blade is considered. Recurrent Neural Networks have the ability to identify the nonlinear dynamical systems from training data. This paper describes the use of recurrent neural networks for predicting the coefficient of lift (C_Z) at high angle of attack. In our approach, the coefficient of lift (C_Z) obtained from the experimental results (wind tunnel data) at different mean angle of attack θ_mean is used to train the recurrent neural network. Then the recurrent neural network prediction is compared with experimental ONERA OA212 airfoil data. The time and space complexity required to predict C_Z in the proposed method is less and it is easy to incorporate in any commercially available rotor code.     

Development process of muzzle flows including a gun-launched missile

Numerical investigations on the launch process of a gun-launched missile from the muzzle of a cannon to the free-flight stage have been performed in this paper. The dynamic overlapped grids approach are applied to dealing with the problems of a moving gun-launched missile. The high-resolution upwind scheme(AUSMPW+) and the detailed reaction kinetics model are adopted to solve the chemical non-equilibrium Euler equations for dynamic grids. The development process and flow field structure of muzzle flows including a gun-launched missile are discussed in detail.This present numerical study confirms that complicated transient phenomena exist in the shortly launching stages when the gun-launched missile moves from the muzzle of a cannon to the freeflight stage. The propellant gas flows, the initial environmental ambient air flows and the moving missile mutually couple and interact. A complete structure of flow field is formed at the launching stages, including the blast wave, base shock, reflected shock, incident shock, shear layer, primary vortex ring and triple point.     

Practical compensation for nonlinear dynamic thrust measurement system

The real dynamic thrust measurement system usually tends to be nonlinear due to the complex characteristics of the rig, pipes connection, etc. For a real dynamic measuring system,the nonlinearity must be eliminated by some adequate methods. In this paper, a nonlinear model of dynamic thrust measurement system is established by using radial basis function neural network(RBF-NN), where a novel multi-step force generator is designed to stimulate the nonlinearity of the system, and a practical compensation method for the measurement system using left inverse model is proposed. Left inverse model can be considered as a perfect dynamic compensation of the dynamic thrust measurement system, and in practice, it can be approximated by RBF-NN based on least mean square(LMS) algorithms. Different weights are set for producing the multi-step force, which is the ideal input signal of the nonlinear dynamic thrust measurement system. The validity of the compensation method depends on the engine’s performance and the tolerance error0.5%, which is commonly demanded in engineering. Results from simulations and experiments show that the practical compensation using left inverse model based on RBF-NN in dynamic thrust measuring system can yield high tracking accuracy than the conventional methods.