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。     

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…     

浅谈Java 2D图象处理技术

本文介绍了Java 2D API的图象处理思想.通过以LookupOp查找表过滤器为例,分析了图象的即时模式模型,即对内存的缓冲区图象进行处理,再绘制到屏幕上的处理技术.同时介绍了利用Java 2D中提供的过滤器对图象数据的过滤操作.     

Maximal admissible mode decision delay in terminal guidance

Mode decision-maker is a critical component in the logic-based Integrated Estimation and Guidance(IEG) system. For the best possible estimation and guidance performance, the mode decision delay of the mode decision-maker should be limited to a range as small as possible. This paper presents a numerical method for computing the maximal admissible mode decision delay that varies with time-to-go. Particular attention has been paid to highly maneuvering target interception in terminal guidance. The results of this research offer useful guidelines for the design of the mode decision-maker in IEG systems.     

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.     

A drilling tool design and in situ identification of planetary regolith mechanical parameters

The physical and mechanical properties as well as the heat flux of regolith are critical evidence in the study of planetary origin and evolution. Moreover, the mechanical properties of planetary regolith have great value for guiding future human planetary activities. For planetary subsurface exploration, an inchworm boring robot (IBR) has been proposed to penetrate the regolith, and the mechanical properties of the regolith are expected to be simultaneously investigated during the penetration process using the drilling tool on the IBR. This paper provides a preliminary study of an in situ method for measuring planetary regolith mechanical parameters using a drilling tool on a test bed. A conical-screw drilling tool was designed, and its drilling load characteristics were experimentally analyzed. Based on the drilling tool-regolith interaction model, two identification methods for determining the planetary regolith bearing and shearing parameters are proposed. The bearing and shearing parameters of lunar regolith simulant were successfully determined according to the pressure-sinkage tests and shear tests conducted on the test bed. The effects of the operating parameters on the identification results were also analyzed. The results indicate a feasible scheme for future planetary subsurface exploration.     

月球表层采样样品智能确认方法

表层采样是月球采样探测的重要方式，样品智能确认有助于提升工作效率与复杂问题处理能力。结合月球表层采样铲挖工作过程，分析了铲挖过程中臂载相机图像的特点，模仿有人参与识别过程，提出了层次解耦的月球样品智能识别流程，利用深度学习方法构建了一类深度卷积识别网络，完整地描述了图像、特征、标记在网络中的正反传递关系，并在月球表层采样地面试验中进行了验证，结果表明该方法对不同光照、不同背景、不同过程、不同形态的样品，具有较好的泛化识别能力，误识别率优于8.1%，平均单幅识别时间约0.7 s。     

飞机大攻角非定常气动力建模与辨识

采用一阶微分方程描述由非线性非定常效应引起的气动力增量 ,从而建立了一种新的飞机大攻角非定常气动力数学模型。研究了模型在缓变运动下的线化形式和动导数表达式。讨论了模型参数的辨识问题。算例证实了所建立的气动力数学模型的正确性和所述模型参数辨识方法的有效性