基于协同仿真的柔性机翼动力学建模及应用*

利用多领域协同仿真技术,应用气动弹性分析理论和方法,对柔性机翼进行结构有限元建模、动力学特性分析、固有振动分析,并针对垂直阵风载荷减缓进行讨论.仿真结果验证了该方法在工程中是有效的.     

基于传递函数法的带外挂大柔性机翼颤振分析

基于带外挂大柔性机翼结构和气动特点,使用带有半解析半数值特性的传递函数方法进行处理.首先,将变形后的柔性机翼视为曲梁,基于曲梁的运动微分方程,结合传递函数方法,将曲梁运动微分方程整理成状态空间方程形式.然后,借鉴有限元方法的思想将单元进行组集,组集时结合机翼挂载处内力平衡和位移协调条件,得到了机翼整体平衡方程,结合求解复特征值的方法,完成了带外挂大展弦比大柔性机翼的动气动弹性稳定性分析.对比通过有限元方法得到的仿真结果,证实了文章提出计算方法的准确性和高效性.文章结尾,分析了外挂质量、转动惯量、位置分布及数量等变量对带外挂大展弦比大柔性机翼的动气动弹性稳定性的影响.     

机翼气动结构耦合数值分析方法

针对机翼的静气动弹性问题,为准确预测其气动特性,研究一种实用有效的气动结构耦合仿真方法.以客机机翼设计为例,通过机翼的静气动弹性问题分析和机翼的气动结构耦合分析流程的分解,建立参数化、自动化、模块化的气动结构耦合仿真分析平台.该平台的流程包括基于全速势方程的气动分析、基于MSC Nastran的结构仿真、应用MATLAB的载荷到结构模型的传递、结构变形向气动外形的映射等环节.算例表明该方法能较好地解决机翼的静气动弹性分析问题.     

Z型折叠机翼的气动力计算及非线性动力学分析

近年来,由于变体飞行器能够通过改变结构几何参数从而有效地改善飞行特性,成为了各国研究的热点问题.然而气动弹性问题一直是制约变体飞行器发展的瓶颈.所以,通过计算特殊形状变体飞行器的气动特性并分析结构的动态稳定性,能更多的了解变形结构的气动特性,也将推动变体飞行器中气动力计算的发展及相关领域的研究.本文主要研究了Z型折叠机翼的气动力计算及非线性动力学分析.根据亚音速下气流的特点,采用薄翼型理论计算Z型折叠机翼的环量分布,然后利用Kutta-Joukowski升力定理,推导了在理想不可压流体来流条件下定常气动升力的解析公式.将上述得到的气动力作用在Z型折叠机翼中,将Z型折叠机翼简化为以刚性铰链相连接的三块碳纤维复合材料层合板,应用Hamilton原理建立了在亚音速气动载荷作用下Z型折叠机翼结构的非线性动力学方程,并根据特殊的边界条件得到了结构的模态函数.利用Galerkin方法离散得到了结构带有折叠角度的常微分运动控制方程.利用数值方法分析了一定气动力作用下Z型折叠机翼结构在折叠过程中的非线性动力学特性,分析了折叠角度对变形机翼结构稳定性的影响.     

大展弦比复合材料机翼动力学分析

针对结构非线性对大展弦比机翼动力学特性影响很大的问题,使用MSC Patran和MSC Nastran软件进行有限元建模及分析,将大展弦比机翼建成薄壁盒型梁模型．研究大变形对机翼动力学特性的影响,比较复合材料盒型梁模型和金属盒型梁模型的计算结果,并讨论复合材料铺层顺序的改变对机翼动力学特性的影响．研究表明：复合材料机翼的各阶固有频率明显高于铝合金机翼;铺层顺序会影响复合材料机翼的固有频率．     

基于ANSYS/CFX耦合的机翼颤振分析

在飞行器飞行气动特性的研究中,为避免传统方法进行颤振点预测时的"准模态"假设,能够更加准确地仿真机翼在流场中的真实运动情况,根据CFD/CSD一体化设计思想,采用了ANSYS/CFX紧耦合算法,对国际标准气动弹性模型AGARD 445.6机翼作了颤振分析,验证性地研究了亚音速和跨音速颤振机理,将仿真计算结果和实验数据进行了比较.表明耦合计算所得的颤振速度和颤振频率和实验值吻合,在亚音速阶段,机翼颤振主要是机翼的弯曲扭转耦合运动引起,而跨音速阶段则主要是机翼的弯曲运动的不稳定性引起,与理论定性分析得到的结果一致,证明ANSYS/CFX全耦合的应用为求解非线性流固耦合问题提供了有效的方法.     

飞翼式柔性飞机纵向动力学建模与稳定性分析

柔性飞行器在飞行过程中容易发生大变形,这种变形将导致机翼甚至整个飞行器的气动弹性和飞行动力学特性发生变化,特别是对稳定性的影响.本文采用三段式刚体假设,以变上反角的方式来描述机翼的展向弯曲变形,对一类飞翼式柔性飞行器进行了纵向动力学建模,并进一步分析了操纵面、推力和迎角与上反角的关系,以及变上反角对飞行稳定性的影响.结果表明,在保持速度和高度不变的情况下,稳定性受上反角的影响比较明显,如果变形过大,飞行器将变为动不稳定,且短周期模态不能保持.因此,为了保持飞机的纵向稳定性,必须要控制飞机的变形.     

基于一种改进的BP神经网络光伏电池建模

由于光伏电池具有高度非线性特性，难以建模，而传统的数学模型难以满足光伏控制系统设计和应用的要求。该文利用神经网络具有逼近任意复杂非线性函数的能力，将神经网络技术应用到光伏阵的建模中，避开了该模块内部的复杂性。模型以太阳能日照、温度以及负载电压作为神经网络辨识模型的输入量，光伏阵输出电流为输出量，采用改进型BP算法，建立了光伏电池的动态响应模型，然后预测了最大功率点。文中给出模型的结构，训练步骤和仿真结果。仿真结果表明，方法可行，建立的模型精度较高，从而为设计光伏实时控制系统奠定了基础。     

柔性梁线接触碰撞的动力学建模和实验研究

研究作旋转运动的柔性梁的线接触正碰撞问题.基于Goldsmith的线接触撞击力模型,分别用基于小变形的混合坐标法和基于大变形的绝对坐标法建立了柔性梁的动力学方程,考虑了几何非线性效应.在此基础上,进一步考虑非线性阻尼项的影响,将Hunt,Crossley的阻尼模型推广到线接触问题.介绍了柔性梁线接触碰撞的实验方法.计算结果显示,在考虑阻尼的情况下,计算结果与实验结果吻合很好.比较了混合坐标法和绝对坐标法的撞击力计算结果,与实验结果对比表明,绝对坐标法更适用于大变形的撞击问题.     

基于神经网络的电机摩擦力混合模型研究

在电机伺服系统优化建模的研究中,要求高精度伺服系统。由于系统摩擦力具有强非线性和非光滑特性,传统的神经网络无法进行有效辨识。将非线性摩擦特性理解成为由稳态部分和突变部分串联构成,以电机伺服系统为对象,引入柔性sigmoid函数描述非线性摩擦特性中的突变部分,并与传统的RBF神经网络串联,构造出描述非线性摩擦特性的神经网络混合模型。仿真结果表明,与传统的RBF神经网络辨识方法相比,模型在输入变化响应下均具有较高的模型精度,从而验证了建模方法的有效性。     

Optimization of airplane wing structures under gust loads

A methodology is presented for the optimum design of aircraft wing structures subjected to gust loads. The equations of motion, in the form of coupled integro-differential equations, are solved numerically and the stresses in the aircraft wing structure are found for a discrete gust encounter. The gust is assumed to be one minus cosine type and uniform along the span of the wing. In order to find the behavior of the wing structure under gust loads and also to obtain a physical insight into the nature of the optimum solution, the design of the typical section (symmetric double wedge airfoil) is studied by using a graphical procedure. Then a more realistic wing optimization problem is formulated as a constrained nonlinear programming problem based on finite element modeling and the optimum solution is found by using the interior penalty function method. A sensitivity analysis is conducted to find the effects of changes in design variables about the optimum point on the response quantities of the wing structure.     

Optimal Trajectory Planning for Flexible Link Manipulators with Large Deflection Using a New Displacements Approach

The main objective of the present paper is to determine the optimal trajectory of very flexible link manipulators in point-to-point motion using a new displacement approach. A new nonlinear finite element model for the dynamic analysis is employed to describe nonlinear modeling for three-dimensional flexible link manipulators, in which both the geometric elastic nonlinearity and the foreshortening effects are considered. In comparison to other large deformation formulations, the motion equations contain constant stiffness matrix because the terms arising from geometric elastic nonlinearity are moved from elastic forces to inertial, reactive and external forces, which are originally nonlinear. This makes the formulation particularly efficient in computational terms and numerically more stable than alternative geometrically nonlinear formulations based on lower-order terms. In this investigation, the computational method to solve the trajectory planning problem is based on the indirect solution of open-loop optimal control problem. The Pontryagin’s minimum principle is used to obtain the optimality conditions, which is lead to a standard form of a two-point boundary value problem. The proposed approach has been implemented and tested on a single-link very flexible arm and optimal paths with minimum effort and minimum vibration are obtained. The results illustrate the power and efficiency of the method to overcome the high nonlinearity nature of the problem.     

基于刚体运动与弹性运动的机械系统动力学建模研究

讨论了具有刚体运动与柔性变形的机械系统的动力学建模,将刚体自由度与弹性变形自由度看作广义坐标,利用有限元法对具有刚性运动与弹性变形的机械系统的运动与变形进行了描述,得到了以刚体位移与弹性变形位移表示的单元的广义惯性力;从应力应变入手,得到了表示单元弹性变形与几何非线性变形的结构刚度矩阵与几何非线性刚度矩阵,使用Kane方程推导了弹性连杆机构的单元运动方程,这种建模方法,可以使用在任意结构的机械系统。     

气动油压伺服系统的智能PID控制研究

针对经典的基于对象精确模型的PID控制方法自适应性差,难以适应具有非线性、时变不确定性的被控对象,提出了一种基于RBF神经网络的、结构简单的PID自适应控制方法。将该智能PID控制应用于气动油压伺服系统中,实验结果表明:具有自学习和自适应能力的RBF网络PID控制方法,能够适应被控对象在较大范围内的变化,具有较强的鲁棒性,其控制品质明显优于常规PID控制方法,将其应用于气动油压伺服系统是可行的。     

A combination of linear and nonlinear activation functions in neural networks for modeling a de-superheater

This paper deals with modeling a power plant component with mild nonlinear characteristics using a modified neural network structure. The hidden layer of the proposed neural network has a combination of neurons with linear and nonlinear activation functions. This approach is particularly suitable for nonlinear system with a low grade of nonlinearity, which can not be modeled satisfactorily by neural networks with purely nonlinear hidden layers or by the method of least square of errors (the ideal modeling method of linear systems). In this approach, two channels are installed in a hidden layer of the neural network to cover both linear and nonlinear behavior of systems. If the nonlinear characteristics of the system (i.e. de-superheater) are not negligible, then the nonlinear channel of the neural network is activated; that is, after training, the connections in nonlinear channel get considerable weights. The approach was applied to a de-superheater of a 325 MW power generating plant. The actual plant response, obtained from field experiments, is compared with the response of the proposed model and the responses of linear and neuro-fuzzy models as well as a neural network with purely nonlinear hidden layer. A better accuracy is observed using the proposed approach.     

Motion control and shape optimization of a suitlike flexible arm

A method of motion control as well as shape optimization is proposed for the preliminary design of a suitlike flexible arm, which is composed of some variable-length and fixed-length beams. The large deformation, variable geometry and motion of the flexible structure are calculated by dynamic finite element analysis (FEA) using the step by step time integration method. A neural-networks-inverse-model, which learns nonlinear behaviour of the flexible structure, has been applied for the motion control as an inverse model of the flexible arm. For this geometrically nonlinear structure and time response problem, the optimum shape of the cross-section has been calculated under constraints of stress, stiffness and minimum weight with FEA and sensitivity analysis combined with fuzzy rules. This method has been applied for the design of a flexible arm, which simulates a process of lifting a human body and moving it. The calculated optimum shape has a much higher stiffness with decrease in weight in comparison with the initial shape. Moreover, the calculated motion agrees well with the one aimed for and the flexible arm reduces the impact force.     

基于高阶神经网络扩展卡尔曼滤波器逆算法的非线性挠性结构的姿态控制

本文针对非线性挠性结构的姿态控制,提出了一种基于高阶神经网络及径向基函数网络（ＲＢＦＮ）相结合的网络模型,用于非线性挠性结构的动态系统辨识,以及基于卡尔曼滤波器（ＥＫＦ）逆算法的控制策略。针对神经网络辨识时的模型误差,提出了一种简单有效的补偿方法,给出了建模误差补偿与补偿时仿真结果。仿真得出,该方法具有收敛快,算法简单,并能有效消除建模误差等优点。     

Geometrically nonlinear analysis of multibody systems

A method for the dynamic analysis of geometrically nonlinear inertia-variant flexible systems is presented. Systems investigated consist of interconnected rigid and flexible components that undergo large rigid body rotations as well as nonlinear elastic deformations. The differential equations of motion are formulated using Lagrange's equation and nonlinear constraint equations describing mechanical joints in the system are adjoined to the system differential equations of motion using Lagrange's multipliers. A computer program that systematically constructs and numerically solves the system equations of motion is used to predict the effect of the geometric elastic nonlinearities on the dynamic response of flexible multibody systems. The automated formulation presented imposes no limitations on the size of the mechanical systems to be treated. Two examples, namely a slider crank and six-bar mechanisms, are presented to illustrate the effect of introducing geometric nonlinearities to the dynamics of flexible multibody systems.     

Modeling nonlinear elastic behavior of reinforced soil using artificial neural networks

This paper presents the application using a multilayer neural network to model nonlinear elastic behavior of composite soil reinforced with fiber and stabilized with lime. First, shear modulus of the reinforced soil was assumed to be a nonlinear function of multiple variables such as contents of short fiber and lime powder, confining pressure, sample-aging period as well as shear strain. Secondly, a multilayer neural network was designed to map the highly nonlinear relationship between shear stress and strain. Thirdly, conventional triaxial shearing tests have been conducted for 34 sets of soil samples to provide experimental data for training and validating the neural network model. Finally, the neural network-based parameter sensitivities have been analyzed. The results of sensitivity analysis indicate that the lime content and the sample curing time play more significant roles than the fiber content in improving soil mechanical properties. It is the first attempt to apply the neural network to modeling of elastic behavior of composite soils, and has been found that modeling of reinforced soil using a multilayer neural network can provide more quality information on the performance of reinforced soil for better decision-making and continuous improvement of construction material designs.