高速飞行器热结构工作时变模态参数辨识

高速飞行器由于其很高的飞行速度而无可避免地受到气动加热作用的影响,进而引起结构特性的时变。采用理论或有限元方法(FEM)进行数值分析,难以获取反映结构在飞行(工作)状态下的真实模态参数。通过辨识获取高速飞行器热环境下的时变结构模态参数是一项十分具有挑战性的任务。针对此问题,引入参数化时频域的最大似然方法,对气动加热作用下的高速飞行器升力面结构的时变模态参数进行了辨识。通过模拟真实飞行状态的数值算例研究,说明参数化时频域的最大似然方法能够很好地辨识出低信噪比(SNR)情况下的模态频率和模态振型,验证了参数化时频域最大似然方法适用于具有显著时变特征的高速飞行器热结构的时变结构模态参数辨识,可为将来相关的工程研究和应用提供良好的理论支持。     

基于叶尖定时的叶片耦合振动参数辨识与仿真

为了实时掌握叶片振动情况、预防故障发生,目前最常用的非接触振动测量方法是基于单自由度模型的叶尖定时法,但当叶片耦合振动时2个振动峰相隔很近,该方法无法同时识别出其振动参数。采用基于2自由度模型的曲线拟合方法,得到叶片耦合振动2个峰的初始相位等参数,用相位遍历法辨识出叶片的振动阶次和频率。结果表明:采用基于2自由度模型的曲线拟合方法提高了叶片耦合振动参数辨识的精度,辨识了相邻2个峰的振动参数。辨识振动与仿真参数设置基本一致。     

基于自适应Kalman滤波的改进PSO算法

针对基于Kalman滤波的PSO算法在设计与应用过程中存在的不足,提出了基于自适应Kalman滤波的改进PSO算法。利用粒子群状态空间Markov链模型,建立粒子群系统状态方程;采用粒子的速度和位置作为观测量,构建观测方程;引入记忆衰减因子动态调整Kalman滤波模型参数及噪声方差阵,降低模型误差,提高粒子的位置估计精度。仿真实验表明:改进的PSO算法无论在优化精度、收敛速度,还是在稳定性方面都有很大的改进和提高,这就有效避免了粒子的"早熟"收敛问题;尤其在处理复杂多峰问题上,改进算法表现出很明显的优越性。     

基于粒子群算法的极短弧定轨

针对经典的初轨计算方法在极短弧定轨中不适用的情况,建立了一种基于粒子群算法的极短弧(TooShort-Arc,TSA)定轨的计算方法。该方法将问题转化为两个三变量的分层优化问题,采用(a,e,M)作为优选变量,在保持问题维数较低的同时,实现了计算结果和观测资料的解耦。由于实测资料处理中的野值剔除方法不适用于粒子群算法,所以,采用稳健估计法,通过在适值函数中使用最小中值二乘准则,实现了稳健的极短弧计算方法。同时,应用MATLAB计算软件,选用缺省参数实现该算法,以进行数据验证。基于实测数据的数值验证表明,方法对于近圆轨道目标30s以下的弧段仍可以获得有效的结果,10s弧段误差仅为16km。此精度满足后续处理的需要,且方法稳健,具有很高的崩溃点。     

潜在电路分析中的拓扑模式识别算法研究

在网络树拓扑识别理论的分析方法与算法基础上 ,推导出网络树特定的五种基本拓扑模式识别的工程算法 ,以解决计算机对电路网络树进行自动拓扑识别问题。     

EXPERIMENT RESEARCH ON DYNAMIC LOAD IDENTIFICATION TECHNOLOGY BASED ON GENERALIZED ORTHOGONAL POLYNOMIAL THEORY (GOPT)

Dynamicloadisoneoftheimportantoriginalparametersinstructuraldynamicanalysis.Ithasobviousinfluencestodynamicsdesign,vibrationcontrols,activecontroltechnologyandsoon.Sincel97O,manyresearchitemsinthisfieldhavebeenaccomplishedintheworld-Sometheoriesandexperimentalmethodsofdynamicloadidentificationhadbeencreatedinfrequencydomain[l'2J-Howeverftherearesomedisadvantagesthattheycouldbeadaptedonlytoidenti-fythesteady-stateloadandnottoidentifythetransientload,suchasshockloadwithshortsampling.Forvarious…     

2 AU以内的“渐进型”太阳高能粒子事件模拟

太阳高能粒子（Solar Energetic Particle,SEP）事件是影响地球空间以及深空辐射环境的主要因素之一。“渐进型”太阳高能粒子事件中的高能粒子主要来自于日冕物质抛射（Coronal Mass Ejection,CME）所驱动的激波扩散加速（Diffusive Shock Acceleration,DSA）过程。CME驱动的激波在行星际的传播过程中,其结构不断演化,进而影响到高能粒子的加速过程。本文利用二维太阳高能粒子加速和传播模型,对发生于2014年4月18日的太阳高能粒子事件实例进行了数值模拟。模型考察了黄道面上2 AU的距离以内包含地球所在位置的4个不同点,分别计算了每个点上高能粒子的通量。数值模拟的结果表明：黄道面内不同位置的观察点,与激波波前的磁力线连接不同,从而导致观察点处高能粒子的通量有着显著的差异。该模型的计算结果可以为深空探测计划开展辐射环境研究提供必要的输入。     

Modelling ignition probability with pairwise mixing-reaction model for flame particle tracking

Reduced order models for ignition analysis can offer insights into ignition processes and facilitate the combustor optimization. In this study, a Pairwise Mixing-Reaction (PMR) model is formulated to model the interaction between the flame particle and the surrounding cell mixture during Lagrangian flame particle tracking. Specifically, the model accounts for the two-way coupling of mass and energy between the flame particle and the surrounding shell layer by modelling the corresponding turbulent mixing, chemical reaction and evaporation process if present. The state of a flame particle, e.g., burnt, hot gas or extinguished, is determined based on particle temperature. This model can properly describe the ignition process with a spark kernel being initiated in a nonflammable region, which is of practical importance in certain turbine engines and has not been rigorously accounted for by the existing models based on the estimation of local Karlovitz number. The model is integrated into an ignition probability analysis platform and is demonstrated for a methane/air bluff-body flame with the flow and fuel/air mixing characteristics being extracted from a non-reacting simulation. The results show that for the spark location being at the extreme fuel-lean outer shear layer of the recirculation zone, PMR can yield ignition events with a significant number of active flame particles. The mechanisms for the survival of the initial flame particles and the entrainment of the survived flame particles into the recirculation zone are analyzed. The results also show that the ignition probability map from PMR agrees well with the experimental observation: a high ignition probability in the shear layer of the recirculation zone near the mean stoichiometric surface, and low ignition probabilities inside the recirculation zone and the top stagnation region of the recirculation zone. The parametric study shows that the predicted shape of the ignition progress factor and ignition probability is in general insensitive to the model parameters and the model is adequate for quantifying the regions with high ignition probabilities.     

Inverse identification of constitutive parameters of Ti2AlNb intermetallic alloys based on cooperative particle swarm optimization

Ti₂AlNb intermetallic alloy is a relatively newly developed high-temperature-resistant structural material, which is expected to replace nickel-based super alloys for thermally and mechanically stressed components in aeronautic and automotive engines due to its excellent mechanical properties and high strength retention at elevated temperature. The aim of this work is to present a fast and reliable methodology of inverse identification of constitutive model parameters directly from cutting experiments. FE-machining simulations implemented with a modified Johnson-Cook (TANH) constitutive model are performed to establish the robust link between observables and constitutive parameters. A series of orthogonal cutting experiments with varied cutting parameters is carried out to allow an exact comparison to the 2D FE-simulations. A cooperative particle swarm optimization algorithm is developed and implemented into the Matlab programs to identify the enormous constitutive parameters. Results show that the simulation observables (i.e., cutting forces, chip morphologies, cutting temperature) implemented with the identified optimal material constants have high consistency with those obtained from experiments, which illustrates that the FE-machining models using the identified parameters obtained from the proposed methodology could be predicted in a close agreement to the experiments. Considering the wide range of the applied unknown parameters number, the proposed inverse methodology of identifying constitutive equations shows excellent prospect, and it can be used for other newly developed metal materials.     

A new bearing fault diagnosis method based on modified convolutional neural networks

Fault diagnosis is vital in manufacturing system. However, the first step of the traditional fault diagnosis method is to process the signal, extract the features and then put the features into a selected classifier for classification. The process of feature extraction depends on the experimenters’ experience, and the classification rate of the shallow diagnostic model does not achieve satisfactory results. In view of these problems, this paper proposes a method of converting raw signals into two-dimensional images. This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process. And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network (CNN), which can automatically accomplish the process of the feature extraction and fault diagnosis. The effect of this method is verified by bearing data. The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed. The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.