燃气轮机仿真中部件特性处理方法的准确性研究

在基于部件的燃气轮机性能仿真建模过程中,部件特性线处理方法的准确性是影响整个机组仿真精度的关键因素。分别介绍了采用传统离散插值处理方法、人工神经网络法和偏最小二乘法进行部件特性线处理的原理和方法,利用一部分已知样本特性数据和不同处理方法对某燃气轮机进行性能仿真,并将仿真结果与另一部分已知样本特性数据进行比较,分析不同处理方法对部件特性和整机性能计算精度的影响。结果表明:在仿真过程中利用偏最小二乘法可直接使用重构方程求解部件特性,无需对部件特性离散化和插值处理,避免了传统部件特性处理方法的计算误差,提高了燃气轮机热力模型求解的精度。     

基于相关向量机的加速度计参数变化规律研究

针对温度、振动等多种影响因素影响石英挠性加速度计内部参数变化,使石英挠性加速度计参数随着时间发生漂移,从而难以准确描述其变化规律的问题,基于相关向量机具有稀疏的模型结构,适合进行不确定系统建模的优点,提出了一种采用相关向量机进行石英挠性加速度计参数规律研究的方法。为了验证方法的有效性,针对2.5年自然贮存条件的石英挠性加速度计的参数标定值进行了时间序列建模,计算结果表明相关向量机具有很好的效果。     

升力体高超声速飞行器非定常滚转力矩建模研究

为了深入研究升力体高超声速飞行器相对薄弱的横侧向稳定性问题,对滚转自由振荡风洞试验数据进行了谱分析,建立了多个气动频率余弦和形式的非定常滚转力矩模型。由于升力体高超声速飞行器的滚转自由振荡曲线呈现非定常、非线性和一定的周期性特征,且对试验结果的谱分析发现,在多种气动状态下,都存在除机械振动频率外的三个振动频率,将滚转力矩表达为此三个频率余弦和的形式。该滚转非定常气动力矩数学模型捕捉了试验的基本趋势涵盖了其主要的量值范围,反映了升力体高超声速飞行器横向流场扰流的多尺度和周期性特征。     

新型目标压力分布下的Licher双翼反设计方法研究北大核心CSCD

为了提高超声速气动构型的升阻比,减小激波阻力,使用反设计方法结合CFD技术优化Licher双翼,实际算例表明,来流马赫数1.7,无粘情况下只需迭代15步即可得到优化结果,优化后翼型波阻可减小25.5%,升阻比提高30.5%。随后依据翼型目标压力分布这一反设计关键点,分析了非零迎角下翼型各边的受力情况,指出了原目标压力分布的不足,并提出了一种新的阶梯形目标压力分布形式,该方法的优化结果可使升阻比提高49.8%。此外基于NS方程的优化结果表明,原目标压力分布的优化效果被削弱,升阻比仅能提高17%,而新目标压力分布的优化结果受到的影响较小,升阻比仍可提高49.2%,说明在考虑流动粘性特征时,阶梯形目标压力分布形式更具实用价值。     

主动重心控制功能仿真建模与应用

减小飞行阻力、节省燃油消耗,是主动重心控制功能的重要收益之一,对功能方案的迭代和优化设计具有重要影响。基于主动重心控制技术原理和方案,耦合飞控系统模型、自动飞控系统模型、燃油系统模型、气动力模型、发动机模型和飞机六自由度（6-DOF）模型,建立某型飞机主动重心控制功能仿真模型。该模型计及燃油消耗引起的重心变化的影响,在...     

复合材料参数化桨叶的动力学减振优化设计

为了进行桨叶动力学优化设计,建立面向工程设计的复合材料多闭室C型梁桨叶剖面参数化模型,实现了桨叶剖面气动外形、内部结构组件、复合材料铺层设计的参数化,并提出了一种保持C型梁纤维面积恒定的参数化设计方法.采用全局寻优能力较强的多种群遗传算法(MPGA),集成参数化设计模型与旋翼有限元气动弹性综合分析模型,通过桨叶各剖面结构组件的参数优化实现了旋翼动力学减振.算例给出了"海豚"直升机桨叶剖面特性实测值与参数化桨叶模型计算值的对比,整体误差不超过3%,并用该参数化模型对桨叶进行动力学减振优化,实现了旋翼加权优化振动载荷系数减小4.15%,经过优化后桨叶的配重位置更加分散,有利于缓解桨叶内部应力/应变突变;而且部分配重分配到桨尖,提高了旋翼的自转惯量,增加了旋翼自转下滑的安全性.     

Performance deterioration modeling and optimal preventive maintenance strategy under scheduled servicing subject to mission time

Servicing is applied periodically in practice with the aim of restoring the system state and prolonging the lifetime. It is generally seen as an imperfect maintenance action which has a chief influence on the maintenance strategy. In order to model the maintenance effect of servicing, this study analyzes the deterioration characteristics of system under scheduled servicing. And then the deterioration model is established from the failure mechanism by compound Poisson process. On the basis of the system damage value and failure mechanism, the failure rate refresh factor is proposed to describe the maintenance effect of servicing. A maintenance strategy is developed which combines the benefits of scheduled servicing and preventive maintenance. Then the optimization model is given to determine the optimal servicing period and preventive maintenance time, with an objective to minimize the system expected life-cycle cost per unit time and a constraint on system survival probability for the duration of mission time. Subject to mission time, it can control the ability of accomplishing the mission at any time so as to ensure the high dependability. An example of water pump rotor relating to scheduled servicing is introduced to illustrate the failure rate refresh factor and the proposed maintenance strategy. Compared with traditional methods, the numerical results show that the failure rate refresh factor can describe the maintenance effect of servicing more intuitively and objectively. It also demonstrates that this maintenance strategy can prolong the lifetime, reduce the total lifetime maintenance cost and guarantee the dependability of system.     

Intelligent modeling and identification of aircraft nonlinear flight

In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks（ANNs）. In general, aircraft flight dynamics is considered as a nonlinear and coupled system whose modeling through ANNs, unlike classical approaches, does not require any aerodynamic or propulsion information and a few flight test data seem sufficient. In this study, for identification and modeling of the aircraft dynamics, two known structures of internal and external recurrent neural networks（RNNs） and a proposed structure called hybrid combined recurrent neural network have been used and compared.In order to improve the training process, an appropriate evolutionary method has been applied to simultaneously train and optimize the parameters of ANNs. In this research, it has been shown that six ANNs each with three inputs and one output, trained by flight test data, can model the dynamic behavior of the highly maneuverable aircraft with acceptable accuracy and without any priori knowledge about the system.     

Recent advance in nonlinear aeroelastic analysis and control of the aircraft

A review on the recent advance in nonlinear aeroelasticity of the aircraft is presented in this paper. The nonlinear aeroelastic problems are divided into three types based on different research objects, namely the two dimensional airfoil, the wing, and the full aircraft. Different non- linearities encountered in aeroelastic systems are discussed firstly, where the emphases is placed on new nonlinear model to describe tested nonlinear relationship. Research techniques, especially new theoretical methods and aeroelastic flutter control methods are investigated in detail. The route to chaos and the cause of chaotic motion of two-dimensional aeroelastic system are summarized. Var- ious structural modeling methods for the high-aspect-ratio wing with geometric nonlinearity are dis- cussed. Accordingly, aerodynamic modeling approaches have been developed for the aeroelastic modeling of nonlinear high-aspect-ratio wings. Nonlinear aeroelasticity about high-altitude long- endurance （HALE） and fight aircrafts are studied separately. Finally, conclusions and the chal- lenges of the development in nonlinear aeroelasticity are concluded. Nonlinear aeroelastic problems of morphing wing, energy harvesting, and flapping aircrafts are proposed as new directions in the future.     

基于改进的混合粒子群算法的变循环发动机模型求解

为了降低变循环发动机模型求解时对初始值的依赖性,提升算法的全局收敛性,同时提高模型求解的效率,提出了一种基于改进的混合粒子群算法的变循环发动机模型求解思路。首先建立了变循环发动机的部件级模型,并建立了发动机的共同工作方程组;然后采用Broyden法对牛顿-拉夫森算法中的雅可比矩阵进行更新计算,在经典粒子群算法的基础上引入粒子中心,作为干扰项,并引入限制因子和自适应时变惯性系数;最后,综合了两种改进的算法,提出改进的混合粒子群算法。实验结果表明:该算法不仅继承了牛顿-拉夫森算法的高计算效率,还吸收了改进的粒子群算法的全局收敛优点,可实现模型大范围收敛。