Africa focus–Adams & Adams(Bilingual)-非洲专题(双语)

合集下载

真 Megohmmeter 电阻抗抗测试仪说明书

Rev.00 03/2023 *************** 89CARACTERÍSTICAS• True Megohmmeter ®• Selección de tensiones de prueba de (50, 100, 250, 500 y 1000) V • Mediciones de aislamiento hasta 4000 GΩ (4 TΩ)• Medicion directa de los valores de relación DAR (absorción dieléctrica) y PI (índice de polarización)• Medición directa de la capacitancia de la muestra • Visualización de la tensión de prueba y tiempo de ejecución • Tiempos programables para la ejecución de pruebas y PI (índice de polarización)• Funciones SMOOTH (estabilización de la lectura) y alarma • Inhibición automática de prueba (si la tensión de la muestra es >25 V)• Descarga automática y visualización de la tensión de descarga • Pantalla grande de dos líneas con visualización de tiempo, tensión y medición • Retroiluminación electroluminiscente azul brillante • Apagado automático cuando no se usa• Funcionamiento remoto con sonda de prueba opcional • Estuche resistente al agua, firme, de doble pared con bolsaseparable para accesorios/cablesMODELO 1060Prueba de aislamiento en cables, transformadores, motores y cableado de instalaciones 2130.03absorción dieléctrica/índice de polarización (DAR/PI) automáticos, resistencia, continuidad, pendrive USB con software DataView ®, memoria de 128 kB)INCLUYEMedidor, bolsa para accesorios separable, dos cables de prueba de 1,5 m (5 pies) identificados por colores (rojo/negro), un cable de prueba (negro), tres pinzas tipo cocodrilo identificadas por colores (rojo/negro/azul), una punta de prueba (negra), cable cruzado RS-232 DB9 H/H de 2 m (6 pies), adaptador de RS-232 a USB, cable de ali-mentación de 115 V (EE. UU.), batería de NiMH recargable, fusibles de respuesto, pendrive USB con software DataView ® y manual de usuario.ACCESORIOSNº DE CATÁLOGO 2155.75Sonda de prueba remota para megóhmetro (600 V CAT IV)Consulte con fábrica sobre precios de calibración NIST.94 export@Rev.00 03/2023N o DE MODELO AEMCN o DE CATÁLOGO AEMCTENSIÓN DE PRUEBARANGO DE AISLAMIENTORANGO DE RESISTENCIARANGO DE CONTINUIDADRANGO DE CAPACITANCIADETECCIÓN DE TENSIÓNFUENTE DE ALIMENTACIÓNPANTALLASOFTWARE DATAVIEW ®65032126.52250 V 500 V 1000 V (1 a 500) MΩ (1 a 500) MΩ (10 a 5000) MΩ—600 V CAAccionamiento manual Analógica No65222155.51250 V 500 V 1000 V 50 kΩ a 10 GΩ100 kΩ a 20 GΩ200 kΩ a 40 GΩ—10 Ω—700 V CA /CC Seis baterías alcalinas AA Digital/ Analógica No65272126.53250 V 500 V 1000 V 1 kΩ a 4 GΩ1 kΩ a 4 GΩ1 kΩ a 4 GΩ(0 a 400) kΩ(0 a 400) Ω—600 V CA 1000 V CCSeis baterías alcalinas AA Digital/ Analógica No65292156.5250 V 100 V 250 V 500 V 1000 V (0,010 a 420) MΩ(0,020 a 420) MΩ(0,050 a 420) MΩ(0,100 a 4200) MΩ0,20 MΩ a 11 GΩ(0 a 420) kΩ(0 a 40) Ω(corriente deprueba 200 MA, ≤ 2 Ω)—700 V CA /CC Seis baterías alcalinas AADigital No65262155.5350 V 100 V 250 V 500 V 1000 V 10 kΩ a 10 GΩ20 kΩ a 20 GΩ50 kΩ a 50 GΩ100 kΩ a 100 GΩ200 kΩ a 200 GΩ1000 kΩ10 Ω, 100 Ω0,1 nF a 10 µF 700 V CA /CCSeis baterías alcalinas AA Digital/ AnalógicaSí65342155.5510 V 25 V 100 V 250 V 500 V 2 kΩ a 1 GΩ5 kΩ a 2 GΩ10 kΩ a 10 GΩ50 kΩ a 25 GΩ100 kΩ a 50 GΩ1000 kΩ10 Ω, 100 Ω—700 V CA /CCSeis baterías alcalinas AA Digital/ AnalógicaSí65362155.5610 V 100 V (variable en pasos de 1 V) 2 kΩ a 20 GΩ1000 kΩ10 Ω, 100 Ω—700 V CA /CCSeis baterías alcalinas AA Digital/ AnalógicaNo6536 Kit piso ESD2155.5710602130.0350 V 100 V 250 V 500 V 1000 V 2 kΩ a 200 GΩ 4 kΩ a 400 GΩ10 kΩ a 1 TΩ 20 kΩ a 2 TΩ 40 kΩ a 4 TΩ(0,01 a 400) kΩ(0,01 a 39,99) Ω(0,005 a 49,99)µF 1000 V CABatería de NiMH recargableDigital/ AnalógicaSí65052130.18500 V 1000 V 2500 V 5000 V 10 kΩ a 2 TΩ10 kΩ a 4 TΩ10 kΩ a 10 TΩ10 kΩ a 10 TΩ—(0,001 a 49,99)µF 2500 V CA 4000 V CCBatería de NiMH recargable Digital/ AnalógicaNo50502130.20500 V 1000 V 2500 V 5000 V 30 kΩ a 2 TΩ100kΩ a 4 TΩ 100 kΩ a 10 TΩ 300 kΩ a 10 TΩ—(0,001 a 49,99) μF 2500 V CA 4000 V CCBatería de NiMH recargable Digital/ AnalógicaNo50602130.21500 V 1000 V 2500 V 5000 V 10 kΩ a 2 TΩ 10 kΩ a 4 TΩ 10 kΩ a 10 TΩ 10 kΩ a 10 TΩ—(0,001 a 49,99) μF 2500 V CA 4000 V CCBatería de NiMH recargable Digital/ AnalógicaSí50702130.30500 V 1000 V 2500 V 5000 V 10 kΩ a 2 TΩ 10 kΩ a 4 TΩ 10 kΩ a 10 TΩ 10 kΩ a 10 TΩ—(0,001 a 49,99) μF 2500 V CA 4000 V CCBatería de NiMH recargable Gráfica/ DigitalSí65502130.31500 V 1000 V 2500 V 5000 V 10000 V 10 kΩ a 2000 GΩ 10 kΩ a 4000 GΩ 10 kΩ a 10000 GΩ 10 kΩ a 15000 GΩ 10 kΩ a 25000 GΩ—(0,001 a 19,99) μF 2500 V CA 4000 V CC Batería de NiMH recargableDigital/ AnalógicaSí65552130.32500 V 1000 V 2500 V 5000 V 10000 V 15000 V10 kΩ a 2000 GΩ 10 kΩ a 4000 GΩ10 kΩ a 10000 GΩ 10 kΩ a 15000 GΩ10 kΩ a 25000 GΩ 10 kΩ a 30000 GΩ—(0,001 a 19,99) μF 2500 V CA 4000 V CCBatería de NiMH recargableDigital/ AnalógicaSíConsulte con fábrica sobre precios de calibración NIST.TABLA DE SELECCIÓNMEGÓHMETROS。

adams知识点总结

adams知识点总结Adams is a multi-body dynamics simulation software used to analyze the motion and behavior of mechanical systems. It is widely used in the automotive, aerospace, and industrial machinery industries to test and validate designs before physical prototypes are built. Adams is known for its ability to accurately predict the performance of complex systems and its user-friendly interface.Key features of Adams include advanced modeling, flexible analysis, and robust post-processing capabilities. The software allows users to create detailed models of mechanical systems, define complex interactions between components, and simulate various operating conditions to predict the system's behavior. In this summary, we will explore the key knowledge points of Adams and how they are used in engineering design and analysis.1. ModelingOne of the key knowledge points in Adams is modeling, which refers to the creation of a digital representation of a mechanical system. Adams offers a wide range of modeling tools to help users build accurate and detailed models of their systems. These tools include parametric modeling, flexible bodies, and contact modeling.Parametric modeling allows users to define their systems using mathematical equations and parameters, making it easy to create complex and customizable models. Flexible bodies enable users to model the deformations and dynamic behavior of components, such as gears, springs, and rubber mounts. Contact modeling allows users to simulate the interactions between bodies in a system, such as collisions, friction, and wear.By using these modeling tools, engineers can create highly realistic digital models of their systems, which can be used to predict the behavior of the physical system under various conditions.2. AnalysisAnother key knowledge point in Adams is analysis, which refers to the process of simulating the behavior of a mechanical system using the digital model. Adams offers a wide range of analysis tools to help users simulate and analyze complex mechanical systems. These tools include dynamic analysis, kinematic analysis, and optimization.Dynamic analysis allows users to simulate the motion and behavior of a mechanical system under various operating conditions, such as acceleration, braking, and cornering. This type of analysis is essential for predicting the performance and safety of systems, such as vehicle suspensions, steering systems, and drivetrains. Kinematic analysis allows users to study the motion and interactions between components in a system, without considering forces and torques. This type of analysis is often used to study mechanisms, such as linkages, cams, and gears.Optimization allows users to find the best design parameters for a given system, such as the shape of a component, the material properties, or the operating conditions. This type of analysis is used to improve the performance, efficiency, and reliability of mechanical systems, such as gears, bearings, and structural components.By using these analysis tools, engineers can gain valuable insights into the behavior of their systems, which can be used to optimize designs and improve the performance and reliability of mechanical systems.3. Post-processingThe final key knowledge point in Adams is post-processing, which refers to the visualization and interpretation of the results from the simulation. Adams offers a wide range of post-processing tools to help users visualize and interpret the behavior of their systems. These tools include animation, plotting, and reporting.Animation allows users to visualize the motion and behavior of their systems in a dynamic and interactive way. This type of post-processing is essential for understanding the kinematics and dynamics of systems, such as vehicle suspensions, engine systems, and gearboxes. Plotting allows users to generate graphs and charts to visualize and interpret the results from the simulation, such as the motion, forces, and torques of components. Reporting allows users to generate detailed reports of the results from the simulation, such as the performance, safety, and reliability of the system. This type of post-processing is essential for communicating the results of the analysis to other stakeholders, such as managers, engineers, and customers.By using these post-processing tools, engineers can gain valuable insights into the behavior of their systems, which can be used to make informed decisions about design changes and improvements.In conclusion, Adams is a powerful multi-body dynamics simulation software used to analyze the motion and behavior of mechanical systems. It offers advanced modeling, flexible analysis, and robust post-processing capabilities to help engineers create detailed models, simulate the behavior, and interpret the results of complex systems. By using these knowledge points, engineers can optimize designs, improve the performance, and ensure the reliability of mechanical systems in various industries.。

基于双目视觉的智能驾驶三维场景的重建技术研究

基于双目视觉的智能驾驶三维场景的重建技术研究摘要三维重建作为计算机视觉技术中的一个重要分支，其研究一直处于火热状态，如今已在工业测量、影视娱乐、医疗科技以及文物重建等各方面得到广泛应用。

本文则主要对智能驾驶领域的双目视觉三维场景重建技术进行研究。

首先对针孔相机以及双目相机的成像原理进行讲解，介绍相机畸变产生及图像校正原理。

然后搭建双目相机三维重建系统，选取张正友标定法对相机进行标定，获取所需相机内外参数并对相机采集到的图片进行校正。

校正完成后通过立体匹配算法对图像进一步处理，获取视差图，再通过重投影矩阵由视差图计算出三维点坐标并重建三维点云模型。

最后对实验结果进行分析，总结实验结果及存在的不足。

关键词：双目视觉；相机标定；立体匹配；三维重建Research on 3D Reconstruction of Intelligent DrivingBased on Binocular VisionAbstractAs an important branch of computer vision technology, three-dimensional reconstruction has been in a hot state. Now it has been widely used in industrial measurement, studio entertainment, medical technology and cultural relic reconstruction. This paper mainly studies the 3D reconstruction technology based on binocular vision in the field of intelligent driving.Firstly, the paper explains the image-forming principle of pinhole camera and binocular camera, and introduces the generation of camera distortion and the principle of image correction. Secondly, a binocular camera 3D reconstruction system is built. Zhang Zhengyou calibration method is selected to calibrate the camera, required camera internal and external parameters are obtained and images collected by the camera are corrected. After the correction, stereo matching algorithm is used to further process the image to obtain the parallax map. 3D point coordinates is calculated via parallax map through the reprojection matrix and 3D point cloud model is reconstructed. Finally, the experimental results are analyzed, and the results and shortcomings are summarized.Keywords：Binocular Vision；Camera Calibration；Stereo Matching；3D Reconstruction目录第1章绪论............................................................................................. 错误!未定义书签。

adams问题补充（Adamsquestionsadded）

adams问题补充（Adams questions added）Research on dynamic performance and crash safety of electric bicycle under man vehicle road environmentAnalysis of bicycle frame based on ADAMS and ANSYSJoint simulation of self balancing dual wheel electric vehicle system based on ADAMS and MatlabApplication of ADAMS software in dynamic simulation analysis of micro electric vehicleSteering system design and ADAMS simulation of micro inverted three wheeled electric vehicleResearch on the control of running mechanism of two wheeled electric vehicleMaster's performance test and analysis system for electric vehiclesA review of two wheeled self balancing robots a review of the stability control of two wheeled self balancing robotsResearch on control method of two wheeled robot based on sliding mode strategyApplication of DSP in parallel dual wheeled electric vehicle control systemResearch on vehicle handling stability based on VirtualPrototyping TechnologyMajor: Vehicle EngineeringKeywords: virtual prototype, vehicle multi-body system dynamics, handling stability, electric power steering, PID controlClassification number: U469.4Research on vehicle handling stability based on Virtual Prototyping TechnologyResearch on joint simulation of EPS based on ADAMS and MATLABReduction Ratio is rack translation 1mm, steering wheel turn angle (in radians)So the Reduction Ratio unit should be rad/mm, not 1The rack ratio is used in vehicle simulation to define the variable (displacement, force, etc.) input of the steering column to control.Reduction ratio for steering input.For rack and pinion steering, it seems that the curve and the power curve of the template in the share can not be used to achieve the steering back and steering handiness. I see an article written before a steering column and a steering column with a three force, and I don't know how to write it... I don'tknow if you feel the same wayLeft_tire_forces inside the lateral, you can see the lateral force of the tireCornering force refers to the steering resistance of the tire, not the lateral forceAt the static load setup interface, the cornering force (steering force at the tire ground) includes the static friction torque,Blue for no power, red for power, you can observe, without power when the steering wheel required to enter a greater torque, this is clearly established.At the same time, some problems are found in the middle of the simulation results. For example, the input torque of the steering wheel is observed, and the maximum is 300N-mm, 0,3N-m,It's obviously small. Another angel=0 degrees, that is, the steering wheel in the middle, the steering wheel input torque is 0, in other words, with only a little force, steering wheelIt starts to rotate. This ignores the existence of static friction resistance between the tire and the road. Only when the input torque is greater than a certain value will the steering wheel startTurn.Analysis reason:Adams is actually a parameterized model, and the accuracy of the simulation results is related to the parameters that are filled. The mechanical parameters are better, fill in the wheelbase, load,The key is that the tire parameters on the static load settings interface are not filled. The tires are important components of the car, and its structural parameters andMechanical characteristics determine the main driving performance of a car.So the problem that needs to be solved now is..,How do you determine the correct tyre parameters on the static load settings interface, including back torque, steering force?,Brake force, traction force, vertical input, vertical input, overturning torque, rolling resistance, lateral force, and lateral force acting on the radius of the carcass.Since my simulation condition is a local steering on the plane, the speed is 0, so the braking force and traction force can not be filled. Due to the study of steering performance, rather thanRide comfort, so vertical input selection and vertical input can not be filled. Since the model is on the plane, there is no roll, so the overturning moment can not be filled.As the speed is 0, the wheel does not roll, so the rolling resistance and lateral force can not be filled. Therefore, the key is to measure the positive torque and steering force.As for the 2 parameters, I have considered them in 2 directions. One is to calculate the theoretical values from the point of view of the kinetic model and the existing parameter values. The other one isThe simulation results are obtained from the tool Tire Testrig, which comes with Adams, but the results are not satisfactory, and some even fail to be theoreticalCv_ is the prefix of the communicator variableSelect Pick Feature custom.-After studying for a few days, Adams people are talking about the simulation of vehicle dynamics. There is one of the most basic problems. Do you know what is the use of modeling?Very simple, right? My model is more accurate so that the leader will believe it! That makes sense! Blunder! What are you going to study based on the model? What are some of the useful information about the project, if you can not recognize this level, your behavior is not a rigorous engineering behavior, don't say the government always engage in performance face job, think of yourself first, you such a person as a civil servantis the same, if you really want to do for Chinese to improve your own heart to self cultivation. (it does not contradict or oppose or criticize some incorrect behavior.)If your vehicle model test has nothing else to do except that you do it, can you understand the design elements of no ground for blame, suspension K&C characteristics, understand what is anti brake nod, driving anti squat, understand why only the pitch center above the wheel center, drive shaft have good anti squat you, you may even even pitch center do not know, the more important the related concepts of the roll center and clear? It's not clear. It doesn't matter. You need to have a mind to think about, not something that has nothing to do with technology. It takes up most of your day. If you don't have a think of the heart, you want to rely on professors and experts will be taught, it is not the right attitude, the ultimate point is like a proud man took the money and said, you give me, I will look at the results, but the chassis performance design process in this field, the result is. For a vehicle, especially the reverse models, at the design stage, the most important is to understand the suspension parameters (size and stiffness) influence on the performance of suspension systems, such processes in the adjustment, how to adjust the You'll see. The value of whole vehicle simulation in modern chassis engineering (two years' new car cycle) lies in the contrast of different design schemes, which makes you find the problem,Verify the solution is correct, such as a car that the vertical stiffness is very large, uncomfortable, a look at the results through the analysis of the original design of the vertical bushing has a greater coupling to movement and lateral movement,so I this bush outwards from the axis of down about which direction to the right? Analysis - Design - vehicle simulation verification - adjustment, closed loop, problem solving.For the most simple example, you have seen the lotus, Porsche give you do not die simulation research. They can't check it, you can experience, we can also check, but one is limited to engineering practice is not necessary, on the other hand is in the world, a vehicle system research, now mainly used forin-depth study on the impact on the dynamics of the automobile chassis design, research of high speed characteristics, model testing needs have a certain basis, for example, to understand the tire model, such as the lateral stiffness in several large stiffness units, vehicle steering characteristics there will be some changes, because the test can not be absolutely accurate, so to be integrated according to the experience in the test data, the load status of the bushing test results, tire test results, comprehensive find a correction value. So, what do you think you need if you do that? How long will it take? So, go steady, one of the simplest, perhaps the most convincing reference, you see how the lotus do.Different plants have different cooperation in mind, we did find a group of like-minded partners, these people are friends as well as students or teachers, make people feel that the car China hope, mainly that the spirit of hard work. If you don't have the spirit to do this, we advise you to think more, the leaders of the country to call everyone should hold together, no matter from life or work on all should think how to benefit the development of things, learn about Scientific Outlook on Development!1., I built the vehicle model, after repeated debugging, and finally add tires and road to add success, but in the process of simulation, the following error occurred:ERROR:, The, simulation, stopped, at, time = 3.38783E-04. ADAMS, cannot, solveThe equations of motion.The, greatest, error, in, an, equation, is, 16.131, in, theEquation, for, GFORCE, JEEP.WHEEL4.forceThe, greatest, change, in, a, variable, is, 1.97857E-16, the, inVariable, for, PART, JEEP.Right_KnuckleTry, the, following, in, this, order:1., Use, the, MAXIT, argument, on, the, EQUILIBRIUM, IC, KINEMATICS,Or, INTEGRATOR, command, to, increase, the, maximum, number, ofIterations.The "Temporarily change the value of the PATTERN argument on the"EQUILIBRIUM, IC, KINEMATICS, or, INTEGRATOR, command, toPATTERN = T.The "Temporarily increase the value of the ERROR argument on the"EQUILIBRIUM, IC,运动学，或积分命令。

Adams详细介绍PPT学习教案

第34页/共46页
ISO移线行驶
ISO Lane Change 内容：
纵向控制器使车辆行驶速度保持在期望值，侧向控制器控制转向系统使车辆保持沿期望的 ISO指定路线行驶
第35页/共46页
第36页/共46页
驾驶员控制文件 Driver control file*.dcf
文件驱动
驾驶员控制数据文件
转向(steering)
侧倾与垂直力分析(roll & vertical force) 静态分析（static load) 外部文件分析（external file)
载荷分析（Loadcase) 车轮包络分析(Wheel envelope)
第20页/共46页
整车仿真内容
开环驾驶分析包括：
转向正弦扫频输入
Swept-Sine Steer 用以衡量车辆的频率响应特性。为评估车辆的瞬态特性，幅频及相频特性
提供依据。重点考查的参数有：方向盘转角、侧向加
速度、横摆角速度、侧倾角
第26页/共46页
第27页/共46页
稳态圆周试验
内容：
固定方向盘转角的稳态圆周车速、横摆角速度 Constant-Radius Cornering —— 定半径转弯亦称稳态回转试验，用以评定整车的不足转向
整车分析参数
转向比、转向齿条比-方向盘与齿条（只在输入力矩或位移的时候有效）、最大的前后制动力矩、前后制动力分配
第19页/共46页
悬架仿真内容
车轮同向跳动（parallel wheel analysis) 车轮反向跳动(opposite wheel travel) 单轮跳动(single wheel travel)
ENVIRONMENT MDI_ACAR_USERMODE standard

ADAMS简介

ADAMS，即机械系统动力学自动分析(Automatic Dynamic Analysis of Mechanical Systems)，该软件是美国MDI公司(Mechanical Dynamics Inc.)开发的虚拟样机分析软件。

目前，ADAMS已经被全世界各行各业的数百家主要制造商采用。

根据199 9年机械系统动态仿真分析软件国际市场份额的统计资料，ADAMS软件销售总额近八千万美元、占据了51%的份额,现已经并入美国MSC公司。

软件应用ADAMS软件使用交互式图形环境和零件库、约束库、力库，创建完全参数化的机械系统几何模型，其求解器采用多刚体系统动力学理论中的拉格郎日方程方法，建立系统动力学方程，对虚拟机械系统进行静力学、运动学和动力学分析，输出位移、速度、加速度和反作用力曲线。

ADAMS软件的仿真可用于预测机械系统的性能、运动范围、碰撞检测、峰值载荷以及计算有限元的输入载荷等。

ADAMS一方面是虚拟样机分析的应用软件，用户可以运用该软件非常方便地对虚拟机械系统进行静力学、运动学和动力学分析。

另一方面，又是虚拟样机分析开发工具，其开放性的程序结构和多种接口，可以成为特殊行业用户进行特殊类型虚拟样机分析的二次开发工具平台。

ADAMS软件有两种操作系统的版本：UNIX版和Wind ows NT/2000版。

在这里将以Windows 2000版的ADAMS l2.0为蓝本进行介绍。

ADAMS软件模块ADAMS软件由基本模块、扩展模块、接口模块、专业领域模块及工具箱5类模块组成，如表3-1所示。

用户不仅可以采用通用模块对一般的机械系统进行仿真，而且可以采用专用模块针对特定工业应用领域的问题进行快速有效的建模与仿真分析。

基本模块用户界面模块ADAMS／View求解器模块ADAMS／Solver后处理模块ADAMS／PostProcessor扩展模块液压系统模块ADAMS/Hydraulics振动分析模块ADAMS/Vibration线性化分析模块ADAMS/Linear高速动画模块ADAMS/Animation试验设计与分析模块ADAMS/Insight耐久性分析模块ADAMS/Durability数字化装配回放模块ADAMS/DMU Replay接口模块柔性分析模块ADAMS/Flex控制模块ADAMS/Controls图形接口模块ADAMS/ExchangeCATIA专业接口模块CAT/ADAMSPro/E接口模块Mechanical/Pro专业领域模块轿车模块ADAMS/Car悬架设计软件包Suspension Design概念化悬架模块CSM驾驶员模块ADAMS/Driver动力传动系统模块ADAMS/Driveline轮胎模块ADAMS/Tire柔性环轮胎模块FTire Module柔性体生成器模块ADAMS/FBG经验动力学模型EDM发动机设计模块ADAMS/Engine配气机构模块ADAMS/Engine Valvetrain正时链模块ADAMS/Engine Chain附件驱动模块Accessory Drive Module铁路车辆模块ADAMS/RailFORD汽车公司专用汽车模块ADAMS/Pre(现改名为Chassis)工具箱软件开发工具包ADAMS/SDK虚拟试验工具箱Virtual Test Lab虚拟试验模态分析工具箱Virtual Experiment Modal Analysis钢板弹簧工具箱Leafspring Toolkit飞机起落架工具箱ADAMS/Landing Gear履带/轮胎式车辆工具箱Tracked/Wheeled Vehicle齿轮传动工具箱ADAMS/Gear ToolAdamsAdams是全球运用最为广泛的机械系统仿真软件，用户可以利用Adams在计算机上建立和测试虚拟样机，实现事实再现仿真，了解复杂机械系统设计的运动性能。

Selfish routing in capacitated networks

Date : June 2003; revised February 2004.
2000 Mathematics Subject Classiﬁcation. Primary 90C35; 90B10, 90B20, 90C25, 90C27, 91A10, 91A13,
91A43.
Key words and phrases. Selﬁsh Routing, Price of Anarchy, Traﬃc Assignment, System Optimum, Nash
SELFISH ROUTING IN CAPACITATED NETWORKS
´ R. CORREA, ANDREAS S. SCHULZ, AND NICOLAS ´ E. STIER MOSES JOSE Sloan School of Management and Operations Research Center Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge, MA 02139-4307
Abstract. According to Wardrop’s ﬁrst principle, agents in a congested network choose their routes selﬁshly, a behavior that is captured by the Nash equilibrium of the underlying noncooperative game. A Nash equilibrium does not optimize any global criterion per se, and so there is no apparent reason why it should be close to a solution of minimal total travel time, i.e. the system optimum. In this paper, we oﬀer positive results on the eﬃciency of Nash equilibria in traﬃc networks. In contrast to prior work, we present results for networks with capacities and for latency functions that are nonconvex, nondiﬀerentiable, and even discontinuous. The inclusion of upper bounds on arc ﬂows has early been recognized as an important means to provide a more accurate description of traﬃc ﬂows. In this more general model, multiple Nash equilibria may exist and an arbitrary equilibrium does not need to be nearly eﬃcient. Nonetheless, our main result shows that the best equilibrium is as eﬃcient as in the model without capacities. Moreover, this holds true for broader classes of travel cost functions than considered hitherto.

基于ADAMS的微型摆式内燃机振动特性分析

任志勇周锋涛沈杰
（煤炭科学研究总院太原研究院，太原０００）３０６
ＡｎｌｓｓｏｉｒｔｎｃａａｔｒｔｓｏｃｏｆｅｉｔｎａｙｉｎｖｂａｉｈｒｃｅｉｉｆｍｉｒｒｅｐｓｏｏＳｃ
●
－
一
～
、
Ｊ
Ｏ
ＦｅｕｎｙＨｚｒｑｅｃ（）
图３运动学激振器
２建立微型摆式内燃机的振动模型
Ｔｍｅｓｃｉ（ｅ）
２．建立微型摆式内燃机的输入通道１
图１微型摆式内燃机在工作过程中受到在水平方向上的激励
－
将图１２中的力作为边彳振动分析的激振力，将激振力施和亍
Ｋｅｙｗｏｒ：ｂｒｔｏｃｒｃｅｉｔｃ；ｉｒｒｅｓｎｇｐｓｏｅｉ；ｄｓＶｉａｉｎｈａａｔｒｓｉｓＭｃｏｆｅｗｉｉｔｎｎｇｎｅＡＤＡＭＳ
中图分类号ｉＨ１文献标识码：Ｔ６Ａ
１引言
ｃｔｎｃａｉｈｃａａｅｏｒｍｃａｉ，ｈｃ０ｐｅｅｔｈｒｃｓｅｓｕｅｉ，ｕｉｍｈｓｗｉｉａｒｌｌａｍｈｓｗｉＣ１ｉｌｎｅｐｏｅｓｓ对ｑｅｚｎｔｇｅｎｍｈｓｐｌｇｅｎｍｈ＂ｍｍｔｔｇｓａｉｇｄｃｔｎｔｔｅＯｌｍ．ｈｕａｐｏｅｓｄｂｅｏ７ｒｓｏｏｌｇｏｐａａｃ．ｕ一ｈｐｎａｕｔｇａｈｌｅｔｅｅｇｒｒｃｓｅｔｆ，ｔｎｙｏｄｉａｅｒｅｂｔｎｉＳｚｉＴｓｙｈ７ｅｉｎｎｐｎ

adams在各领域的实际应用

adams在各领域的实际应用
ADAMS的应用十分广泛，它广泛应用于汽车、铁道、航空航天、兵器、船舶、风力、工程机械等领域。

具体来说，ADAMS在各领域的实际应用包括但不限于以下几个方面：
1. 汽车工业：用于车辆的乘坐平稳性、可操纵性和寿命研究，悬架系统和转向性能分析，驾驶训练动力学研究以及各种各样机构设计。

2. 航天工业：用于卫星结构的展开、卫星轨道及其飞行姿态动力学研究。

3. 航空和国防工业：用于飞行稳定性和控制分析，驾驶员弹射模拟分析，装备分离、起落装置和载荷分析。

4. 工程机械行业：用于车辆越野机动性分析，操纵性和寿命分析，挖掘机、起重设备以及卡车的动力学分析仿真和研究。

5. 机电产品工业：用于激光唱机、VCR机构、照相机杆件机构和照片复制机的分析。

6. 生物力学和人机工程领域：用于乘员碰撞仿真分析和乘员姿态分析，以及人机界面的检验和事故重建。

此外，ADAMS软件还可以应用于各种动态系统分析和设计领域，例如行星齿轮箱的动态性能分析和优化设计等。

泛化迁移深度学习下的跨模态图像行人识别算法

第42卷　第1期吉林大学学报(信息科学版)Vol.42　No.12024年1月Journal of Jilin University (Information Science Edition)Jan.2024文章编号:1671⁃5896(2024)01⁃0137⁃06泛化迁移深度学习下的跨模态图像行人识别算法收稿日期:2022⁃10⁃13基金项目:西安明德理工学院科研基金资助项目(2021XY01L09)作者简介:蔡现龙(1976 ),男,陕西渭南人,西安明德理工学院讲师,主要从事计算机科学与技术研究,(Tel)86⁃189****7386(E⁃mail)2631069053@㊂蔡现龙,李　阳,陈　曦(西安明德理工学院信息工程学院,西安710124)摘要:针对由于受光照条件变化㊁行人身高差异等影响,致使监控视频图像在不同时刻的成像存在较大的跨模态差异问题,为准确识别跨模态图像中的行人,提出基于泛化迁移深度学习的跨模态图像行人识别算法㊂通过循环生成对抗网络(Cyele GAN:Cycle Generative Adversarial Network)形成跨模态图像,采用单目标图像处理对基准图分割处理,得到人体候选区域,在匹配图中搜索和其匹配的区域,得到人体区域的视差,通过视差提取人体区域的深度和透视特征㊂将注意力机制和跨模态行人识别相结合,分析两种不同类型图像的差异,将两个子空间映射到同一个特征空间,同时引入泛化迁移深度学习算法对损失函数度量学习,自动筛选跨模态图像的行人特征,最终通过模态融合模块将筛选的特征融合处理完成行人识别㊂实验结果表明,所提算法可以快速㊁准确地提取不同模态图像中的行人,识别效果较好㊂关键词:泛化迁移深度学习;跨模态图像;行人识别;特征提取中图分类号:TP311文献标志码:APedestrian Recognition Algorithm of Cross⁃Modal Image under Generalized Transfer Deep LearningCAI Xianlong,LI Yang,CHEN Xi(School of Information Engineering,Xi’an Mingde Institute of Technology,Xi’an 710124,China)Abstract :Due to the influence of changes in lighting conditions and pedestrian height differences,there are large cross modal differences in surveillance video images at different times.In order to accurately identify pedestrians in cross modal images,a pedestrian recognition algorithm based on generalized transfer depth learning is proposed.The cross modal image is formed through Cyele GAN(Cycle Generative Adversarial Network),and the reference map is segmented using single object image processing to obtain candidate human body regions.The matching regions are searched in the matching map to obtain the disparity of human body regions,and the depth and perspective features of human body regions are extracted through the disparity.The attention mechanism and cross modal pedestrian recognition are combined to analyze the differences between the two types of images.The two subspaces are mapped to the same feature space.And the generalized migration depth learning algorithm is introduced to learn the loss function measurement,automatically screen the pedestrian features of the cross modal images,and finally complete pedestrian recognition through the modal fusion module to fuse the filtered features.The experimental results show that the proposed algorithm can quickly and accurately extract pedestrians from different modal images,and the recognition effect is good.Key words :generalization transfer deep learning;cross⁃modal images;pedestrian recognition;feature extraction0　引　言由于在光照条件较差的环境中对单模态行人识别,无法满足相关领域对行人识别效果的预期要求,因此人们将深度学习技术应用于行人识别[1⁃2]中,并在对应的数据集中取得了较高的识别率㊂由于昼夜光照差异比较明显,导致跨模态的行人识别面临巨大挑战㊂目前人们针对跨模态行人识别方面的研究已有许多报道,如王留洋等[3]优先组建双模态特征提取网络,通过构建的网络对图像深度特征实行提取操作,增强处理全部特征后融合图像的全部像素信息,完成行人识别㊂Oh 等[4]利用多个图像区域(头部㊁身体等)的convnet 特征构建了行人识别框架,从时间和视点两方面分析了不同特征的重要性,利用人脸识别器实现了行人人脸识别㊂郑爱华等[5]采用双路模型提取不同模态下的全局特征,对其实行局部精细化处理,挖掘行人的结构化局部信息;通过标签和预测信息构建跨模态局部信息之间的关联,完成跨模态融合处理,确保各个特征之间相互补充,最终实现行人识别㊂为降低光照等因素引起的图像模态差异对行人识别效果的影响,笔者引入泛化迁移深度学习,提出一种跨模态图像行人识别算法㊂经实验测试结果表明,所提算法能有效降低行人识别时间,提升行人识别结果的准确性㊂1　跨模态图像行人识别模型设计1.1　跨模态图像行人特征提取由于受摄像机角度㊁外部环境等因素影响,使行人视频监控图像产生了较大的模态差异,为此需要将识别的行人视频设定为一个图像集,利用Cyele GAN 生成跨模态图像㊂由于人体的轮廓在图像集中近似为矩形,所以可借助矩形目标检测方法得到人体候选区域㊂优先采用Hough变换方法提取行人的主要图1　人体候选区域获取流程图Fig.1　Flow chart of human body candidate region acquisition 特征信息,通过视知觉分组的灰度分类器和共圆分类器将人体候选区域虚假信息剔除㊂图1给出了人体候选区域获取的详细操作流程图㊂为得到人体候选区域不同区域的特征信息,优先需要获取不同区域的视差㊂在实际操作过程中,采用基于局部约束的像素点区域匹配算法㊂以基准图中待匹配像素点为中心构建一个窗口,通过窗口内相邻像素的灰度值描述图像中的像素特征㊂将基准图中随机一个像素点设定为中心,同时创建多个大小完全一致的滑动窗口,引入搜索策略获取像点图在对准图中对应的像素点,两者之间的差值即为视差㊂块匹配方法[6⁃7]的核心是将基准图待匹配的窗口设定为模板图像,对准图像作为目标图像,对两者实行模板匹配㊂在匹配过程中,主要通过人体候选区域每个灰度间的相关测度描述不同视图间的相关性,如下:D p SSD (h )=∑(u ,w )∈R p R (u ,v )-I m R (u ,v ),(1)其中D p SSD (h )表示视图之间的相关性;R (u ,v )表示跨模态图像的水平偏移量;I m 表示基准图像;R p 表示随机像素对应的块状邻域㊂由于每个候选区域的相关性保持不变,所以需要将目标区域中区域相关性设定为式(1)的形式,进而获取目标区域对应的距离测度,如下:D T SAD (h )=∑(u ,w )∈R p 1R (u ,v )-I m R (u ,v ),(2)其中D T SAD (h )表示各个目标区域之间的距离测度㊂在实际应用过程中,需要消除左右两个视图之间由于光照亮度产生的差异,为此引入零均值方法,将其应用目标匹配过程中,进而获取零均值视图相关性D T ZSAD (h ),如下:831吉林大学学报(信息科学版)第42卷D T ZSAD (h )=∑(u ,w )∈R p R (u ,v )-I m -1R (u ,v )-I m R (u ,v )㊂(3) 通过候选人体区域取代式(2)和式(3)中的目标区域,而候选人体区域的视差可根据外极线约束在经过校正处理后的左右视图中,沿外极线方向搜索目标最小视图相关性D T ZSAD (h ),如下:[D T SAD (h )]min =arg min (u ,w )∈R p [D p SSD (h )-D T ZSAD (h )]㊂(4) 在跨模态图像中,人体和其他物体之间存在明显差异,则跨模态图像可能出现的行人身高最小值为h min ,如下:h min =H -b D T ZSAD (h ),(5)其中H 表示人体候选区域内的深度特征㊂设定人体区域在空间中的真实长度为l ,在采集人体图像的过程中,可通过小孔透视比例得到不同轮廓的特征提取结果:W (u ,v )=(z -h )h 1R (u ,v ),(6)其中z 表示人体候选区域的深度㊂由于跨模态图像中人体候选区域的视差半径和真实人体身高之间存在密切关联,而人体的真实身高可看做是行人的固有特征,设定行人身高的变化范围,则有h min ≤h ≤h max ㊂通过上述分析,利用图2给出跨模态图像行人特征提取流程图㊂图2　跨模态图像行人特征提取流程Fig.2　Flow chart of pedestrian feature extraction from cross⁃modal images 通过人体视觉[8⁃9]可得到人体区域的深度和透视特征,如下:S (u ,v )=1[D T SAD (h )]min R (u ,v )I m ,T (u ,v )={W (u ,v )(z -h )}2I m ìîíïïï,(7)其中S (u ,v )和T (u ,v )分别表示人体区域的深度特征和透视特征㊂1.2　泛化迁移深度学习下的跨模态图像行人识别深度学习中的注意力机制是指重点关注图像的细节信息,忽略没有利用价值的信息,使其在图像领域得到广泛应用,取得了十分显著的成果㊂将通道域思想应用于跨模态图像行人识别中,可以快速获取红绿蓝(RGB:Red,Green and Blue)和相对照度(RI:Relative Illumination)图像两者之间的差异性,进而准确区分不同类型的行人㊂通过SeNet 网络的思想全面引入压缩激活神经网络,其中压缩激活模块主要是利用每个通道之间的关系,学习特征权重,有效增强特征图关键信息的权重比例㊂设定输入特征为F ={f 1,f 2, ,f n },大小为F ∈E (h ,w ,c ),优先对1.1小节得到的特征压缩处理,通过全局池化的方式,将特征图转换为大小完全相同的向量,即全局通道描述符b (u ,v ),如下:b (u ,v )=F (sp )(u ,v ),1W (u ,v )∑m =1∑n =1f n (i ,j {),(8)931第1期蔡现龙,等:泛化迁移深度学习下的跨模态图像行人识别算法其中F (sp )(㊃)表示压缩操作;f n (i ,j )表示通道总数㊂通过两个全连接层得到特征向量u 的计算如下:u =H (u ,v )(i ,j ),β(g (u ,v {)),(9)其中H (u ,v )(㊃)表示激励操作;β表示激活函数;g (u ,v )表示两个全连接层对应的权值矩阵㊂将注意力机制应用于跨模态图像行人识别中,构建基于压缩激活机制的双路径跨模态模型,模型中融入了压缩激活模块,方便后续学习更加具有鲁棒性的特征㊂学习不同模态下的特征,将其映射到对应的子空间中㊂通过上述分析,优先计算行人各个特征之间的欧氏距离,并基于其再次计算即可获取三元组损失函数,如下:K chtri =1F (sp )(u ,v )∑m =1∑n =1f n (i ,j )[max(D (u ,v )-min D (u ,v ))+β],(10)其中K chtri 表示三元组损失函数;D (u ,v )表示相同跨模态图像之间的特征距离㊂将三元组损失函数和身份损失函数两者结合,最终获取综合损失函数如下:K tocal =K chtri +K id ,(11)其中K tocal 表示综合损失函数;K id 表示身份损失函数㊂经上述分析,引入泛化迁移深度学习算法对综合损失函数度量学习,则有:K tocal (u ,v )=(k a ,p -β)K chtri +K id ,(12)其中K tocal (u ,v )表示综合损失函数的度量学习结果;k a ,p 表示超参数㊂对输入的原始图像,通过测试集形成的跨模态图像集并没有得到充分应用,所以需要借助模态融合模块将两种筛选后的特征融合处理,同时将融合后的结果输入到全连接层中,采用SoftMax 损失展开有监督的训练㊂模态融合[10]模块的主要目的是将原始图像和跨模态图像两者有效融合,在设定条件下可利用RGB 图得到丰富的颜色特征,采用RI 图像可得到丰富的纹理特征,如下:L lsr =(1-β)lg{p (k )}-1/K chtri (k a ,p -β),(13)其中L lsr 表示跨模态图像的纹理特征;p (k )表示平滑参数㊂采用模态融合模块融合处理上述提取的特征和式(13)提取的纹理特征,以实现跨模态图像行人识别,如下:Q (u ,v )=1/(1-β){(k a ,p -β)K tocal (u ,v )}f n (i ,j ),(14)其中Q (u ,v )表示跨模态图像的行人识别结果㊂至此,实现跨模态图像行人识别㊂2　实验分析为验证所提泛化迁移深度学习下的跨模态图像行人识别算法的有效性,实验在INRIA Person Dataset 图像库(http:∥pascal.inrialpes.fr /data /human /)中随机选择200幅跨模态图像作为测试图像集,设定图像的大小为256×256像素,优先利用图3给出部分测试图像㊂图3　部分行人测试图像集Fig.3　Part of the pedestrian test image set 041吉林大学学报(信息科学版)第42卷将文献[3⁃4]算法作为所提方法的对比方法,从不同角度对图3所示的行人图像进行测试㊂2.1　实验流程实验计算机配备IntelXeon 6230(2.10GHz)CPU 和32GByte 视频内存的NVIDIA Tesla V100视频卡㊂实验中,文献[3⁃4]算法行人识别流程和参数设置依照其实验最佳参数进行设定㊂笔者算法具体的实验流程如图4所示㊂图4　所提算法识别流程Fig.4　Identification process of the proposed algorithm 2.2　实验结果分析在图3所示的测试图像集上进行实验测试,分析不同算法的识别效果,实验测试结果如图5所示㊂图5　不同算法的跨模态图像行人识别结果对比Fig.5　Comparison of pedestrian recognition results incross⁃modal images by different algorithms 从图5可看出,无论白天还是夜晚,采用所提算法均可准确识别行人,而另外两种算法在比较复杂的场景下只能识别出行人的局部特征信息,出现了漏识和误识现象㊂由此可见,所提算法利用模态融合模块能更好地完成行人识别,且受光照差异造成的模态差异影响较小㊂以相同数据集中不同光照强度的图像作为测试对象,将识别时间作为测试指标,表1给出了具体实验分析结果㊂表1　不同算法的跨模态图像行人识别时间测试结果对比　平均识别时间为1.732s,分别低于另外两种算法的1.79s 和1.85s,全面验证了笔者算法的优势,同时可以更快的速度完成行人识别,受光照影响较小㊂141第1期蔡现龙,等:泛化迁移深度学习下的跨模态图像行人识别算法图6　图像不同视差距离下峰值信噪比数值Fig.6　Peak signal to noise ratio values of images at different parallax distances 以峰值信噪比(PSNR:Peak Singal⁃Noise Ratio)为指标,测试在图像不同视差距离下行人识别的峰值信噪比数值,结果如图6所示㊂从图6可看出,随着视差距离的增大,行人识别图像峰值信噪比数值虽然呈现降低趋势,但降低幅度很小㊂其中笔者方法的峰值信噪比数值始终高于两种对比算法㊂上述结果说明笔者方法将泛化迁移深度学习引入到行人识别中,获取的行人识别结果较完整,表明识别能力较好㊂3　结　语针对行人识别方法受光照㊁视差距离影响产生的模态差异造成识别时间较长以及识别结果不准确的问题,笔者提出一种泛化迁移深度学习下的跨模态图像行人识别算法㊂通过和另外两种算法对比可知,笔者算法可以全面降低行人识别所用时间,同时还能增加识别结果准确性,为后续开展此方面研究提供了重要的策略和理论依据㊂参考文献:[1]祁磊,于沛泽,高阳.弱监督场景下的行人重识别研究综述[J].软件学报,2020,31(9):2883⁃2902.QI L,YU P Z,GAO Y.Research on Weak⁃Supervised Person Re⁃Identification [J].Journal of Software,2020,31(9):2883⁃2902.[2]韩光,葛亚鸣,张城玮.基于去相关高精度分类网络与重排序的行人再识别[J].计算机应用研究,2020,37(5):1587⁃1591,1596.HAN G,GE Y M,ZHANG C W.Person Re⁃Identification by Decorrelated High⁃Precision Classification Network and Re⁃Ranking [J].Application Research of Computers,2020,37(5):1587⁃1591,1596.[3]王留洋,芮挺,郑南,等.基于跨模态特征增强的RGB⁃T 行人检测算法研究[J].兵器装备工程学报,2022,43(5):254⁃260.WANG L Y,RUI T,ZHENG N,et al.Research on RGB⁃T Pedestrian Detection Algorithm Based on Cross⁃Modal Feature Enhancement [J].Journal of Ordnance Equipment Engineering,2022,43(5):254⁃260.[4]OH S J,BENENSON R,FRITZ M,et al.Person Recognition in Personal Photo Collections [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(1):203⁃220.[5]郑爱华,曾小强,江波,等.基于局部异质协同双路网络的跨模态行人重识别[J].模式识别与人工智能,2020,33(10):867⁃878.ZHENG A H,ZENG X Q,JIANG B,et al.Cross⁃Modal Person Re⁃Identification Based on Local Heterogeneous CollaborativeDual⁃Path Network [J].Pattern Recognition and Artificial Intelligence,2020,33(10):867⁃878.[6]AGARWAL R,VERMA O P.Robust Copy⁃Move Forgery Detection Using Modified Superpixel Based FCM Clustering withEmperor Penguin Optimization and Block Feature Matching [J].Evolving Systems,2022,13(1):27⁃41.[7]JAVDANI D,RAHMANI H,WEISS G.SeMBlock:A Semantic⁃Aware Meta⁃Blocking Approach for Entity Resolution [J].Intelligent Decision Technologies:An International Journal,2021,15(3):461⁃468.[8]WU J Y,LU C H,LO H H,et al.P⁃23:Image Adaptation to Human Vision (Eyeglasses Free):Full Visual⁃CorrectedFunction in Light⁃Field Near⁃to⁃Eye Displays [J].SID International Symposium:Digest of Technology Papers,2021,52(3):1143⁃1145.[9]ANNAMALAI R,DORNEICH M,TOKADLI G.Evaluating the Effect of Poor Contrast Ratio in Simulated Sensor⁃Based VisionSystems on Performance [J].IEEE Transactions on Human⁃Machine Systems,2021,51(6):632⁃640.[10]邓佳桐,程志江,叶浩劼.改进YOLOv3的多模态融合行人检测算法[J].中国测试,2022,48(5):108⁃115.DENG J T,CHENG Z J,YE H J.Multimodal Fusion Pedestrian Detection Algorithm Based on Improved YOLOv3[J].China Measurement &Testing Technology,2022,48(5):108⁃115.(责任编辑:刘东亮)241吉林大学学报(信息科学版)第42卷。

基于虚拟试验场的牵引车动态载荷研究

2024年第1期27doi:10.3969/j.issn.1005-2550.2024.01.005 收稿日期：2023-10-27基于虚拟试验场的牵引车动态载荷研究王庆华1，王丽荣2，陈小华2，李蒙然1，黄刚1（1.国家汽车质量检验检测中心（襄阳），襄阳441004；2. 北京福田戴姆勒汽车有限公司，北京 101400）摘要：基于Adams软件的虚拟试验场动态载荷分解技术在乘用车耐久性能开发领域广泛应用。

对于重卡车型，由于车辆模型复杂、参数有限且测试难度大，虚拟试验场技术的应用推广受到限制。

搭建某牵引车整车多体动力学模型及虚拟试验场仿真环境，同时采集试验场工况下的实车载荷谱数据并与虚拟试验场动力学仿真分析提取的动态载荷进行对比。

使用相对伪损伤比值、频谱分析等评估比利时、扭曲路、搓板路等典型路面工况下仿真与实测载荷谱数据的差异。

结果表明：基于虚拟试验场的动态载荷提取技术可应用于牵引车车型且可实现较高的精度，是一种获取试验场耐久工况载荷谱的有效方法。

关键词：虚拟试验场；载荷分解；路面模型；牵引车中图分类号：U467 文献标识码：A 文章编号：1005-2550（2024）01-0027-07Research on Dynamic Load of Tractor Based on VPGWANG Qing-hua1, WANG Li-rong2, CHEN Xiao-hua2, LI Meng-ran1, HUANG Gang1(1.National Automobile Quality Inspection and T est Center (Xiangyang), Xiangyang 441004,China; 2. Beijing Foton Daimler Automobile Co., Ltd, Beijing 101400, China)Abstract: The dynamic load decomposition technology of VPG based on Adams is widely applied in the field of passenger car durability performance development. For heavytruck, the application and promotion of VPG are limited due to the complexity of vehiclemodels, limited parameters, and high RLDA testing difficulty. The complete vehicle multi-body dynamics model of a tractor and virtual proving ground simulation environment arebuilt based on Adams. The real vehicle load data acquisition of the proving ground eventswas carried out and compared with the dynamic loads extracted from dynamic simulationanalysis of the virtual proving ground to verify the model accuracy and load accuracy.Relative pseudo damage ratio, RMS value ratio, and spectrum analysis were used to evaluatethe differences between simulated and measured load data under typical road conditionssuch as Belgium, twisted roads, and washboard roads. It is proved that The dynamic loadextraction technology based on virtual proving ground can be applied to tractor models andachieve high accuracy, which is an effective method for obtaining the load data of provingground durability events.Key Words: Virtual Proving Ground; Load Extraction; Road Model; Tractor随着高精度路面扫描和轮胎力学模型建模等技术快速发展，基于虚拟试验场（V i r t u a l Proving Ground）的动态载荷提取技术在车型开发早期阶段即可开展，可有效缩短开发周期和试验成本[1-4]。

ADAMS 基本介绍

1.2、虚拟样机技术的起源及发展
虚拟样机技术是一项新生的工程技术。借
助于这项技术，工程师们可以在计算机上建立机械系统的模型，伴之以三维可视化处理，模拟在现实环境下系统的运动和动力特性，并根据仿真结果精化和优化系统的设计与过程。
1.3、虚拟样机技术应用领域虚拟样机技术已经广泛地应用在各个领域里： 1、汽车制造业 2、工程机械 3、航天航空业 4、国防工业 5、通用机械制造业
第3章、ADAMS/View建模仿真步骤
Virtual Prototyping 整合系統
1. 2. 3. 4. 5. 6. 7. 8. 9.
I-DEAS UG CATIA Pro/E CADDS 5 Solidworks CoCreate AutoCAD Microstation
1. MATRI Xx 2. MATLA B 3. Easy5
Merge
合并两个不相交的实体
取相交形体实体切割实体还原
把第一个实体并入到第二个实体中
Intersect Cut Split
保留相交部分的实体，所形成的实体特性与第一个实体相同用第一个实体去切第二个实体，删除第一个实体和相交部分的第二个实体将经过以上布尔操作的实体还原
4.5、实体的特征操作
名称图标说明铰接副revolute允许2个零件相对转动去除2个旋转和3个移动自由度移动副translational允许2个零件相对移动去除3个旋转和2个移动自由度圆柱副cylindrical允许2个零件相对移动或转动去除2个旋转和2个移动自由度运动副约球副spherical允许2个零件相对转动去除3个移动自由度平面副planar允许2件在一平面内运动去除2个旋转和3个移动自由度束名称图标说明恒速副constantvelocity限定两个旋转运动等速去除1个旋转和3个移动自由度万向节hookeuniversal旋转运动在两个零件间传递去除1个旋转和3个移动自由度螺旋副允许2个零件作相对螺旋运动去除2个旋运动副screw转和3个移动自由度齿轮副gear复杂运动副把3个零件和2个简单运动副连接起来形成齿轮运动和其它类型的运动关联副coupler把2个或3个简单运动副连接起来实现运动或能量的传递固定副fixed把2个零件固连在一起约束名称图标说明平行约束parallelaxes限定两个零件的零件坐标系z轴始终平行去除2个旋转自由度垂直约束perpendicular限定两个零件的零件坐标系z轴垂直去除1个旋转自由度52基本约束jointprimitivesar方向约束orientation限定两个零件的零件坐标系坐标轴同向不能相对旋转去除3个旋转自由度点面约束inplane限定一个零件在另一个零件的某个平面上运动去除1个移动自由度点线约束inline限定第一个零件沿第二个零件上的某条直线运动去除两个移动自由度53运动约束?运动约束通过对模型施加运动来实现对模型的约束一旦定义好运动后模型就会按照所定义的运动规律进行运动而不考虑实现这种运动需要多大的力或力矩

Novel type of phase transition in a system of self-driven particles

Abstract
A simple model with a novel type of dynamics is introduced in order to investigate the emergence of self-ordered motion in systems of particles with biologically motivated interaction. In our model particles are driven with a constant absolute velocity and at each time step assume the average direction of motion of the particles in their neighborhood with some random perturbation (η) added. We present numerical evidence that this model results in a kinetic phase transition from no tran |va| = 0) to ﬁnite net transport through spontaneous symmetry breaking of the rotational symmetry. The transition is continuous since |va| is found to scale as (ηc − η)β with β ≃ 0.45.
——————–
One of the most interesting aspects of many particle systems is that they exhibit a complex cooperative behavior during phase transition [1]. This remarkable feature of equilibrium systems has been studied in great detail for the last couple of decades leading to a deeper understanding of processes which may take place in an assembly

Adams教程第二章

第二章建模总论在本章，你将建立和连接挂锁的各个部件，并同时验证各个部件的建立和连接是否正确。

有了正确的模型，你就可以在第三章中在仿真环境下对其进行测试。

建造挂锁模型可分为两个基本部分：建造曲柄（pivot）和手柄（handle）建造钩子（hook）和连杆（slider）完成后的图形如图 4 所示。

建造曲柄（pivot）和手柄（handle）作为建造模型的初始步骤，你需完成以下操作：1、启动ADAMS/View并建立一个新的数据文件2、熟识ADAMS/View的界面3、设置工作环境4、创建设计点5、建造曲柄（pivot）6、重新命名曲柄（pivot）7、建造手柄（handle）8、用转动副连接各个部件9、模拟模型的运动10、观察参数化的效果启动ADAMS/View并建立一个新的数据文件在本部分，你需要启动ADAMS/View并建立一个模型数据文件，其中包含一个名为Latch的模型。

模型数据文件记录了你在ADAMS/View当前时段所做的所有工作，包括你建立的所有模型、模型的属性、仿真的结果和图表、定制菜单和对话框，以及你所做的所有参考标识。

在UNIX环境下，你可以从ADAMS Product Menu菜单中启动ADAMS/View。

关于ADAMS Product Menu 菜单更多的信息请参阅指导手册《Running and Configuring ADAMS on Your UNIX System》。

在Windows环境下，你可以从开始菜单启动ADAMS/View。

关于更多的信息请参阅指导手册《Running ADAMS on Windows》。

在UNIX环境下启动ADAMS/View:1、从ADAMS Product Menu菜单中，选择ADAMS/View，则运行ADAMS/View的对话框出现。

2、用鼠标点击OK,则欢迎用户使用的对话框出现。

在Windows环境下启动ADAMS/View:1、点击开始按钮。

ADAMS__虚拟样机技术入门与提高

1.3、虚拟样机(yànɡ jī)技术运用范围
虚拟(xūnǐ)样机技术曾经普遍地运用在各个范围里： 1、汽车制造业 2、工程机械 3、航天航空业 4、国防工业 5、通用机械制造业
第八页，共183页。
第2章基本知识
1、自在度机械系统的自在度是指机械系统中各零件相关于
空中所具有的独立运动的数量。欲使机构具有确定的运动，那么其原动件的数目必需等于该机构的自在度。
ADAMS/View主窗口局部功用如下：主工具箱—展现各种常用命令的快捷键；命令菜单栏—包括了ADAMS/View顺序的全部命
令；快捷工具栏—设置了一些最基本的文件和编辑
命令的快捷按钮；义务屏幕区—显示样机模型的区域；义务栅格—在义务区显示栅格的目的是利于(lìyú)
建模；外形栏—显示操作进程中的各种信息和提示；
ADAMS/View支持参数化建模，以便能很容易地修正模型并用于实验研讨。
用户在仿真进程中止中或许当仿真完成后，都可以观察主要的数据变化以及模型的运动。这些就像做实际的物理实验一样。
第十七页，共183页。
ADAMS/Solver : 一个自动树立并解算用于机械系统运动仿真方程
(fāngchéng)的，快速、坚定的数值剖析工具。提供一种用于解算复杂机械系统复杂运动的数值方法。可以对以机械部件、控制系统和柔性部件组成的多域效
第四页，共183页。
ADAMS软件运用交互式图形环境(huánjìng) 和零件库、约束库、力库，创立完全参数化的机械系统几何模型，其求解器采用多刚体系统动力学实践中的拉格郎日方程方法，树立系统动力学方程，对虚拟机械系统中止静力学、运动学和动力学剖析，输入位移、速度、减速度和反作用力曲线。ADAMS软件的仿真可用于预测机械系统的功用、运动范围、碰撞检测、峰值载荷以及计算有限元的输入载荷等。

adams介绍

－具有一个强大的函数库供用户自定义力和运动发生器。
－具有开放式结构，允许用户集成自己的子程序。
－自动输出位移、速度、加速度和反作用力曲线，仿真结果显示为动画和曲线图形。
－可预测机械系统的性能、运动范围、碰撞、包装、峰值载荷以及计算有限元的输入载荷。
－支持同大多数CAD、FEA和控制设计软件包之间的双向通讯。
ADAMS软件的特点如下：
－利用交互式图形环境和零件库、约束库、力库建立机械系统三维参数化模型。
－分析类型包括运动学、静力学和准静力学分析，以及线性和非线性动力学分析，包含刚体和柔性体分析。
－具有先进的数值分析技术和强有力的求解器，使求解快速、准确。
－具有组装、分析和动态显示不同模型或同一个模型在某一个过程变化的能力，提供多种“虚拟样机”方案。
关于多学科 - MD ห้องสมุดไป่ตู้案
MD 方案涵盖了MD Nastran， MD Adams，MD Patran，提供了一个真正集成的多学科仿真系统，是为制造业的设计师、工程师随时随地可以使用的协同的多学科的分析更为复杂模型的平台，MD方案大大提升了生产力，驱动产品更新。
MSC.Adams 是集建模、求解、可视化技术于一体的虚拟样机软件，是世界上目前使用范围最广、最负盛名的机械系统仿真分析软件。ADAMS全仿真软件包是一个功能强大的建模和仿真环境，它可以对任何机械系统进行建模、仿真、细化及优化设计，应用范围从汽车、火车、航空航天器一直到盒式录像机等。使用这套软件可以产生复杂机械系统的虚拟样机，真实地仿真其运动过程，并且可以迅速地分析和比较多种参数方案，直至获得优化的工作性能，从而大大减少了昂贵的物理样机制造及试验次数，提高了产品设计质量，大幅度地缩短产品研制周期和费用。ADAMS软件将强大的分析求解功能与使用方便的用户界面相结合，使该软件使用起来既直观又方便，还可用户专门化。

ADAMS各种材料碰撞参数

Unit s
k N/mm c N-sec/mm d mm v mm/sec
Aluminum (Greasy) Steel (Dry) Aluminum (Greasy) Steel (Greasy) Aluminum (Greasy) Aluminum (Dry)
Acrylic Acrylic Acrylic Acrylic Acrylic Nylon Nylon Nylon Nylon Nylon Nylon Rubber (Dry) Rubber (Dry) Rubber (Dry) Rubber (Dry) Rubber (Dry) Rubber (Dry) Rubber (Dry) Rubber (Greasy) Rubber (Greasy) Rubber (Greasy) Rubber (Greasy) Rubber (Greasy) Rubber (Greasy) Rubber (Greasy) Rubber (Greasy)
Page 1
materials
e
d
vs vd mus mud R
1.5 0.1 0.1
10 0.3 0.25 0.15
1.5 0.1 0.1
10 0.08 0.05 0.15
1.5 0.1 0.1
10 0.08 0.05 0.15
1.5 0.1 0.1
10 0.25 0.2 0.20
1.5 0.1 0.1
0.1 0.1
10 0.08 0.05 0.20
1.5 0.1 0.1
10 0.05 0.03 0.20
1.5 0.1 0.1 1.5 0.1 0.1 1.5 0.1 0.1
10 0.05 0.03 0.20 10 0.05 0.03 0.20 10 0.05 0.03 0.20

AMESim与ADAMS联合仿真操作说明

AMESim与ADAMS联合仿真操作说明摘要：物理系统可能由各种元件组成，例如气动的，机械的，液压的，电子的以及控制系统等，所有的元件协同工作。

多学科领域系统和复杂多体系统之间的相互作用很难在单一的软件平台中来仿真。

解决的方案就是通过AMESim和专用的多体动力学软件ADAMS之间的接口，使得两者在仿真中协同工作。

本文结合天线的简单实例介绍AMESim与ADAMS联合仿真的操作过程。

关键词：AMESim ADAMS 联合仿真1.引言AMESim（Advanced Modeling Environment for Simulation of engineering systems）软件是由法国IMAGINE公司于1995年推出的多学科复杂领域系统工程高级建模和仿真平台，该软件不要求用户具备完备的仿真专业知识，采用面向系统原理图建模的方法，便于工程技术人员掌握和使用。

机构动力学分析软件ADAMS (automatic dynamic of mechanical system)集建模、求解和可视化技术于一体,能有效分析和比较多种参数方案。

运用AMESim与ADAMS的联合仿真，可以有效的对设备的动态过程进行分析，根据交互分析产生的结果来评价设备的性能，为了更加真实的符合实际情况，理论分析用来完成检验产生的数值结果。

这种虚拟产品开发方法与得出的结论将对设计人员提供一定帮助。

通过AMESim/ADAMS之间的接口，有两种方式实现联合仿真：（1）将模型从一个平台中输入到另一个平台中，采用单一的积分器进行计算。

（2）各个平台分别利用自己的积分器计算自己的模型，通过预先统一的通讯间隔进行信息交换。

2.软件环境要求首先AMESim软件需要4.2级以上版本； ADAMS需要2003级以上版本（含A/Control模块）。

其次必须要有Microsoft Visual C++ 编译器。

如果需要从ADAMS环境中使用接口，那么还强烈推荐Fortran编译器，这样可以将AMESim的模型编译成为ADAMS的子函数（Subroutine）。

基于ADAMS的摩托车虚拟样机仿真平台开发

基于ADAMS的摩托车虚拟样机仿真平台开发杨冬香;王建生;康献民【摘要】针对摩托车虚拟样机建模过程中装配关系复杂、而利用成型的CAD软件建模装配后导入又不能实现参数化，从而难以进行优化设计的缺点，基于ADAMS 软件，对摩托车虚拟样机仿真分析平台的建立进行了研究。

此平台不仅可实现摩托车各零部件间以及摩托车与路面等的自动装配，同时也实现了参数化的建模。

最后通过某款150型摩托车的虚拟样机模型的创建，验证了本平台的有效性。

此平台的创建可促进虚拟样机技术在摩托车行业的应用，从而提高国内摩托车的动力学性能。

%It is very complicated of the assembly during the virtual prototype modeling of the motorcycle, and the optimization design can’t execution when importing the model from other CAD software because it is not a parameterized model. Aiming at these shortages, the dynamic simulation analysis platform for the motorcycle virtual prototype based on ADAMS is studied in this paper. By this platform, the things that can be achieved is not only the automatic assembly of the simulation model, but also the parametric modeling. The effectiveness of the platform is validated by the building of a certain 150-type motorcycle virtual prototype model in the end of the paper. This platform can push the application of the virtual prototype technology in the motorcycle industry, and improve the dynamics performance of the internal motorcycle.【期刊名称】《机电工程技术》【年(卷),期】2014(000)009【总页数】4页(P27-30)【关键词】摩托车;虚拟样机平台;ADAMS【作者】杨冬香;王建生;康献民【作者单位】五邑大学机电工程学院，广东江门529020;五邑大学机电工程学院，广东江门 529020;五邑大学机电工程学院，广东江门 529020【正文语种】中文【中图分类】TP391.9摩托车行业是我国支柱产业之一，据统计，我国摩托车行业从业人员达400万，连续15年蝉联世界第一大摩托车生产和消费国[1]。

基于虚拟样机ADAMS的钢丝绳建模及仿真_郑世山

用虚拟样机软件ＡＤＡＭＳ提供的宏命令就可以轻松地解决此问题。宏命令是ＡＤＡＭＳ／Ｖｉｅｗ的命令集。
由ＡＤＡＭＳ／Ｖｉｅｗ软件绘制目标模型，以模型为模板通过宏命令复制、循环产生钢丝绳模型。在
ＡＤＡＭＳ／Ｖｉｅｗ软件中建一段２０ｍ长的钢丝绳，一共平均分成２００段小圆柱体，通过参数编辑宏命令完成钢丝绳建模，程序代码如下：
ｉｎｔｅｇｅｒ＿ｖａｂｌｅ＝ｌｅｖａｌ（ｉｐ＋１）ｅｎｄｖａｒｉａｂｌｅｄｅｌｅｔｅｖａｒｉａｂｌｅ＿ｎａｍｅ＝ｉｐｄｅｆａｕｌｔｓｍｏｄｅｌｐａｒｔ＿ｎａｍｅ＝．ｇａｎｇｓｈｅｎｇｚｉ＿１．ｐａｒｔ＿２ｆｏｒｖａｒ＝ｔｈｅ＿ｐａｒｔｏｂｊ＝．ｇａｎｇｓｈｅｎｇｚｉ＿１．＂ｐａｒｔ＿［＾２］＊＂ｔｙｐｅ＝ＰＡＲＴｍｏｖｅｏｂｊｅｃｔｐａｒｔ＿ｎａｍｅ＝（ｔｈｅ＿ｐａｒｔ）＆ｃ１＝０ｃ２＝－１００ｃ３＝０．０＆ｃｓｏａｒｔ＿ｎａｍｅ＝＆（ｅｖａｌ（ＤＢ＿ＤＥＦＡＵＬＴ（．ＳＹＳＴＥＭ＿ＤＥＦＡＵＬＴＳ，“ｐａｒｔ”）））ｄｅｆａｕｌｔｓｍｏｄｅｌｐａｒｔ＿ｎａｍｅ＝（ｅｖａｌ（ｔｈｅ＿ｐａｒｔ））ｅｎｄ
ｄｅｆａｕｌｔｓｍｏｄｅｌｍｏｄｅｌ＿ｎａｍｅ＝．ｇａｎｇｓｈｅｎｇｚｉ＿１ｖａｒｉａｂｌｅｃｒｅａｔｅｖａｒｉａｂｌｅ＿ｎａｍｅ＝ｉｐｉｎｔｅｇｅｒ＿ｖａｌｕｅ＝０ｗｈｉｌｅｃｏｎｄｉｔｉｏｎ＝（ｉｐ＜９９）ｐａｒｔｃｏｐｙｐｏｒｔ＝．ｇａｎｇｓｈｅｎｇｚｉ＿ｌ．ｐａｒｔ＿２ｎｅｗ＿ｐａｒｔ＝（ＵＮＩＱＵＥ＿ＮＡＭＥ（＂ｐａｒｔ＂））ｖａｒｉａｂｌｅｍｏｄｉｆｙｖａｒｉａｂｌｅ＿ｎａｍｅ＝ｉｐ