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Modeling and simulation of Physical systems.ppt

Modeling and simulation of Physical systems.ppt

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Next Chapter: System and Control
Department of Mechanical and Electrical Engineering
Xiamen University
3. Laplace transforms
• What Are Laplace Transforms • Why to use Laplace • Calculation • How Do You Transform an Differential Equation • A few Examples
variables and to obtain a mathematical model. Because the systems are dynamic in nature, the descriptive equations are usually differential equations, if they can be linearized, then Laplace transform can be used to simplify the method of solution.
Xiamen University
Edited by Zhu QY
What is Mechatronics System? Why is the Model Must Needed? Laplace transforms Modeling of Mechatronics Simulation and Block Diagrams
Lcf (t) g(t)= cL f (t)+ Lg(t), L1 cF (s) G(s)= cL1 F (s)+ L-1 G(s)

基于PSR_模型的成都市土地生态安全评价

基于PSR_模型的成都市土地生态安全评价

Modeling and Simulation 建模与仿真, 2023, 12(5), 4449-4457 Published Online September 2023 in Hans. https:///journal/mos https:///10.12677/mos.2023.125405基于PSR 模型的成都市土地生态安全评价陈 涛,陈施越,樊玉茹西南民族大学公共管理学院,四川 成都收稿日期:2023年6月7日;录用日期:2023年9月1日;发布日期:2023年9月8日摘要研究目的:通过构建土地生态安全评价指标体系,对2011~2022年成都市土地生态安全做出综合评价,揭示成都市土地生态安全差异及其主要影响因素,指导土地合理利用。

研究方法:运用PSR 评价模型建立评价指标体系,运用熵值法计算权重,综合评价成都市土地生态安全指数。

研究结果:在2011~2020年间,成都市土地生态系统压力指数出现先降后升,然后趋于平稳;土地生态系统状态指数整体上呈现出上升的趋势;土地生态安全响应指数稳步上升。

研究结论:整体上看,成都市土地生态安全综合评价指数发展不稳定,成都市政府应该加大对生态环境方面的投入,制定土地生态发展策略,全面提高成都市土地生态安全等级。

关键词PSR 模型,土地生态安全评价,成都市Evaluation of Land Ecological Security in Chengdu Based on PSR ModelTao Chen, Shiyue Chen, Yuru FanSchool of Public Administration, Southwest Minzu University, Chengdu SichuanReceived: Jun. 7th , 2023; accepted: Sep. 1st , 2023; published: Sep. 8th , 2023AbstractThe paper is going to make a comprehensive evaluation of land ecological security in Chengdu dur-ing 2011~2022 by constructing an evaluation index system of land ecological security. Also the pa-per will reveal the differences and main influencing factors of land ecological security in Chengdu, and guide the rational use of land. PSR evaluation model was used to establish the evaluation index system and the entropy method was used to calculate the weight to comprehensively evaluate the陈涛等land ecological security index of Chengdu. The results showed that from 2011 to 2020, the land ecosystem pressure index in Chengdu decreased first, then increased, and then stabilized. The state index of land ecosystem showed an upward trend on the whole. The response index of land ecological security increased steadily. It can be concluded that the development of comprehensive evaluation index of land ecological security in Chengdu is not stable. Chengdu government should increase the investment in ecological environment, formulate land ecological development strat-egy, and comprehensively enhance the level of land ecological security in Chengdu.KeywordsPSR Model, Evaluation of Land Ecological Security, Chengdu Array Copyright © 2023 by author(s) and Hans Publishers Inc.This work is licensed under the Creative Commons Attribution International License (CC BY 4.0)./licenses/by/4.0/1. 引言随着都市化和现代化进程的加快,我国经济迈入快车道的同时,人口数量也逐步增多,土地资源作为基本生产要素,为经济社会发展提供重要支撑。

On the Modeling and Simulation of Friction

On the Modeling and Simulation of Friction

Abstract
Two new modeLs for "slp-stick ion are presented. One, called the "bristle model," is an apprxiation designed to apture the psical phenomenon of sticking. This model is relatively inefficent numericaly. The other model,called the "resetintegratormodel," does not Capture the details of the sticing phenomenon, but is numerically efficient and exhibits behavior similar to the model propoed by Karnopp in 1985. All threeof these modelsare preferable to thecassical model which poorly represents the friction force at zero velcdty. Simulation experiments show that the new models and the Karnopp model give simflar results in two examples In a dosed-loop example, the classical model predkts a mimit cycle which is not observed in the laboratory. The new modeis and the Karnopp model, on the other hand, agree with the experimental obserntio.

基于C-NCAP_某座椅鞭打试验仿真和优化

基于C-NCAP_某座椅鞭打试验仿真和优化

Modeling and Simulation 建模与仿真, 2023, 12(2), 1500-1511 Published Online March 2023 in Hans. https:///journal/mos https:///10.12677/mos.2023.122140基于C-NCAP 某座椅鞭打试验仿真和优化赵 军1,江圆迪1,毛晨曦2,刘会霞1*1江苏大学机械工程学院,江苏 镇江 2上海埃立曼科技有限公司,上海收稿日期:2023年2月17日;录用日期:2023年3月20日;发布日期:2023年3月28日摘要按照2021版C-NCAP 要求,对某车型座椅进行鞭打试验,通过鞭打试验结果分析发现挥鞭伤较为严重,鞭打得分较低。

针对这一问题采用Hypermesh 软件建立有限元模型,用LS-Dyna 显示非线性动力学求解器进行求解,并将有限元结果与试验结果进行对标分析。

在对标合格的有限元模型基础上分析鞭打损伤原因,并根据分析结果提出优化参数,优化参数如下:座椅靠背左右侧板厚度、座椅靠背支撑板厚度、座椅靠背上横杆厚度以及头枕杆直径。

根据优化后的结果可知,优化后的假人挥鞭伤减小,鞭打得分提高,大大增强了座椅防挥鞭伤的能力。

关键词座椅,鞭打试验,有限元分析,C-NCAPSimulation and Optimization of a Seat Whiplash Test Based on C-NCAPJun Zhao 1, Yuandi Jiang 1, Chenxi Mao 2, Huixia Liu 1*1School of Mechanical Engineering, Jiangsu University, Zhenjiang Jiangsu 2Shanghai Elliman Technology Co., Ltd., ShanghaiReceived: Feb. 17th , 2023; accepted: Mar. 20th , 2023; published: Mar. 28th , 2023AbstractAccording to the requirements of the 2021 version of C-NCAP, a whipping test was carried out on the seat of a certain model, and the whiplash injury was found to be more serious and the whip-lash score was lower through the analysis of the whiplash test results. To solve this problem, Hypermesh software is used to establish a finite element model, LS-Dyna is used to display the*通讯作者。

交通建模与仿真基本流程

交通建模与仿真基本流程

交通建模与仿真基本流程Traffic modeling and simulation is a critical tool in urban planning and transportation system design. 交通建模与仿真是城市规划和交通系统设计中至关重要的工具。

With the increasing complexity of transportation systems and the need for efficient and sustainable solutions, the demand for accurate and reliable traffic modeling and simulation continues to grow. 交通系统的复杂性不断增加,对于高效和可持续解决方案的需求也在增加,因此对准确可靠的交通建模与仿真的需求也在不断增加。

A basic flow process for traffic modeling and simulation involves several key steps, including data collection, model development, validation, and simulation. 交通建模与仿真的基本流程包括数据收集、模型开发、验证和仿真等几个关键步骤。

Each step is essential to ensuring the accuracy and reliability of the simulation results. 每一步都对确保仿真结果的准确性和可靠性至关重要。

Data collection is the first and crucial step in the traffic modeling and simulation process. 数据收集是交通建模与仿真过程中的第一步,也是至关重要的一步。

做新药研发管理你大概会觉得有用的几本入门书

做新药研发管理你大概会觉得有用的几本入门书

做新药研发管理你大概会觉得有用的几本入门书药物的整个开发途径大约需要8-15年左右,这一段时间可以大致分成几个阶段,每个阶段的研究内容、所需知识都不尽相同。

如果对每一个阶段都希望进行管理,那么至少应当理解这些工作的意义和价值,管理的基础是至少部分地了解事物的本质,所以对于医药研发人员来说,需要一些必备的常识。

今天我们将首先划分药物研发的阶段,粗浅地讨论一下每一阶段所必备的知识,主要介绍一下获得这些知识的最快途径。

希望这些内容对于即将开始药物研发的新人能够提供一些帮助,尽量避免一些浪费时间的著作。

临床阶段部分可能要到下次再做分享。

红色字体强烈推荐。

药物研发的第一个主要阶段是化合物的发现与早期开发阶段。

这一阶段的主要目的是找出可能有效安全而且成药性较好的药物,药物化学家和生物学研究员是这一阶段的主要两类工作人员,工作的中心是药物化学家的药物分子设计。

本次推送的这一部分主要内容有两个:药物化学案例学习和生物学基础。

药物化学的案例学习药物化学在过去的30年内,受到法规环境、技术进步等多方面的影响,在药物设计上的进步可谓日新月异。

技术迭代在未完成产品开发就已经完成,使得技术进步未能见证产品优势。

药物化学家的工具也变得越来多丰富,无法判断一些药物的设计方法能否禁得住考验。

药物设计工具未经验证,药物化学团队需要一些案例指导。

在之前的推送里,我分享了药物化学的入门书籍,感兴趣的读者可以回翻到当期。

这类书的质量往往主要来源于药物发现团队是否讲述了足够多的内容,就是一般所谓的“干货”,另外选题与选材也比较重要。

由于缺少强有力的编辑团队,这类书的文笔参差不齐,推荐其中的《Accounts in Drug Discovery: Case Studies in Medicinal Chemistry》,与《Casestudies in modern drug discovery and development》。

国内去年的《明星药物》也可以读一读,但对于药物开发缺少实质帮助。

World Journal of Modelling and Simulation

World Journal of Modelling and Simulation

Published by World Academic Press, World Modelling and Simulation, Vol. 3 (2007) No. 4, pp. 252-261
253
and computer science and so on. System Dynamics combines systems analysis and systems synthesis to study systemic complex questions[7] . The Grey System theory is based on the new cognition of the impersonal system. Though the information of some systems is deficient, it should have specific functions and be ordinal as a system. However, its internal rules are incompletedly exposed[11] . From the views of grey system theory, some random variables, ruleless disturbing elements and disordered and unsystematic date sequences can be found their internal developmental change rules. The main thought of grey system theory is to dig the hidden knowledge and rules by building a few dates in grey[8] . Since the grey system theory comes out, it applies in many kinds of practical fields. Its methods of analyzing,modeling, forecasting, deciding, controlling and evaluating uncertain system with few date are unique and special, proved to be with high application values[15] . The combined model of grey systems and system dynamics will scientifically forecast the whole system developmental tendency, both internal and external. The results will guide the developmental directions to gas industry and the scientific policies in this region. The rest of the paper is organized as follows. In section 2, the SD-GF model for cycle economy planning will be set up and the solution method will be stated after the system was forecasted by GF model. Next, in section 3, we select an example of economy system and simulate with software named VENSIM, then the analysis of region cycle economy planning is in what follows. The presentation of conclusion is in section 4.

2-建模仿真框架-建模仿真理论

2-建模仿真框架-建模仿真理论

建模与仿真理论Theory of Modeling andSimulation第二讲复习⏹重点内容⏹系统规范的层次性⏹难点内容⏹同态的概念第二讲建模仿真框架⏹2.1 建模仿真框架中的主要实体(重点)⏹源系统、实验框架、模型、仿真器⏹2.2 建模仿真框架中实体间的主要关系(重点)⏹建模关系:有效性⏹仿真关系:仿真正确性⏹2.3 其它重要的关系⏹建模和有效简化⏹实验框架:建模关系(难点)⏹2.4 时间Modeling and Simulation⏹Framework:⏹Entities and Relations⏹M&S Framework:⏹Entities and Relations in M&S⏹Framework的作用:⏹框架的理解可以使M&S人员(分析人员、程序设计人员、管理人员、用户等)更好地工作和交流⏹基于建模与仿真框架,可以更好地理解开展建模与仿真活动时遇到的基本观点和问题,并给出合理的解决方案Modeling and Simulation Ex pe实验框架M&S In A Nutshell源系统建模关系模型仿真器仿真关系P3:系统态射:抽象表示与近似C12:系统态射层次C13:抽象C14:VV与近似态势C15:DEVS的通用性与唯一性C16:系统的DEVS表示P2:MS形式体系C6:基本形式体系C7:耦合多组件系统C8:仿真器C9:多形式体系建模C10: 扩展DEVSC11: 并行分布离散事件仿真P1: 基础知识C1: 系统建模概念C2: 建模仿真框架C3:形式体系与仿真器C4:DEVS简介C5:系统规范层次P4:系统设计建模与仿真环境C17:DEVS设计方法论C18:系统实体结构/基于模型的框架实验框架M&S In A Nutshell源系统建模关系模型仿真器仿真关系P3:系统态射:抽象表示与近似C12:系统态射层次C13:抽象C14:VV与近似态势C15:DEVS的通用性与唯一性C16:系统的DEVS表示P2:MS形式体系C6:基本形式体系C7:耦合多组件系统C8:仿真器C9:多形式体系建模C10: 扩展DEVSC11: 并行分布离散事件仿真P1: 基础知识C1: 系统建模概念C2: 建模仿真框架C3:形式体系与仿真器C4:DEVS简介C5:系统规范层次P4:系统设计建模与仿真环境C17:DEVS设计方法论C18:系统实体结构Modeling and Simulation⏹Entities:⏹Source System⏹Model⏹Simulator⏹Experimental Framework⏹Relations:⏹Modeling⏹SimulationModeling and Simulation表1 建模仿真中的基本实体及通常所处的系统规范层次Basic entity Definition Related Systemspecification levels Source system Real or artificial source of data Known at level 0 BehaviordatabaseCollection of gathered data Observed at level 1Experimental frame Specifies the conditions underwhich system is observed orexperimented withConstructed atlevels 3 and 4Model Instructions for generation data Constructed atlevels 3 and 4Simulator Computational device forgenerating behavior of themodel Constructed at level 4第二讲建模仿真框架⏹2.1 建模仿真框架中的主要实体(重点)⏹源系统、实验框架、模型、仿真器⏹2.2 建模仿真框架中实体间的主要关系(重点)⏹建模关系:有效性⏹仿真关系:仿真正确性⏹2.3 其它重要的关系⏹建模和有效简化⏹实验框架:建模关系(难点)⏹2.4 时间2.1.1 Source System 2.1.1 Source System2.1.1 Source System 2.1.1 Source System2.1.1 Source System⏹Source System (源系统):符合建模需求的真实或虚拟的环境,是观测数据源。

纯电动汽车动力性匹配设计与模型仿真

纯电动汽车动力性匹配设计与模型仿真

Modeling and Simulation 建模与仿真, 2020, 9(3), 357-366Published Online August 2020 in Hans. /journal/moshttps:///10.12677/mos.2020.93036Dynamic Matching Design and ModelSimulation of Pure Electric VehicleWentao Zhang, Li Ye, Zhijun Zhang, Huan Ye, Mengya ZhangSchool of Power Engineering, University of Shanghai for Science and Technology, ShanghaiReceived: Aug. 6th, 2020; accepted: Aug. 20th, 2020; published: Aug. 27th, 2020AbstractBased on the selection of basic vehicle parameters and the determination of performance indica-tors, this paper carries out the design matching of dynamic performance parameters of pure elec-tric vehicles. Then, a pure electric vehicle dynamic simulation model is established by vehicle si-mulation software, and the vehicle dynamic performance index is simulated and analyzed by in-putting relevant parameters. Finally, the rationality of simulation model and parameter matching is verified by real car test. This study can provide theoretical basis for the matching design of var-ious systems in the initial stage of pure electric vehicles, carry out range and performance test evaluation of vehicle performance, and provide reference for the analysis of dynamic performance and economic index of pure electric vehicles.KeywordsPure Electric Vehicle, Parameter Design Matching, Vehicle Power Model, Simulation Analysis纯电动汽车动力性匹配设计与模型仿真张文韬,叶立,张志军,叶欢,张梦伢上海理工大学动力工程学院,上海收稿日期:2020年8月6日;录用日期:2020年8月20日;发布日期:2020年8月27日摘要本文基于对整车基本参数的选取与性能指标的确定,进行了纯电动汽车动力性能参数的设计匹配。

cimoc

cimoc

CIMOC简介CIMOC(CIty MOdeling and City simulation)是一种用于城市建模和城市模拟的软件工具。

它可以帮助城市规划师、建筑师和政策制定者理解和分析城市规划决策的影响,以及预测城市未来的发展趋势。

CIMOC注重于城市环境中的可持续发展、交通运输规划、土地利用规划、建筑设计、能源管理等关键领域。

本文将介绍CIMOC的主要功能、应用场景以及其在城市规划中的作用。

功能CIMOC是一个功能强大的软件工具,具有以下主要功能:1. 城市建模CIMOC可以帮助用户模拟和重建城市的三维模型。

用户可以使用该软件工具根据实际情况和数据进行城市建模,包括建筑物、道路、绿地等要素。

这些要素可以根据用户的需求进行编辑和组织,以便更好地展示城市的整体面貌。

2. 城市模拟CIMOC可以模拟城市内在的各种因素和过程,比如交通流量、能源消耗、人口迁移等。

用户可以通过调整参数和输入数据,模拟出不同情景下城市的发展趋势,并预测城市的未来发展。

3. 数据分析CIMOC提供了强大的数据分析功能,可以帮助用户对城市模型和模拟结果进行深入的分析。

用户可以通过CIMOC提供的工具和算法,对数据进行可视化和统计分析,以便更好地理解和解释城市规划决策的效果。

4. 决策支持CIMOC可以为城市规划师和政策制定者提供决策支持。

通过模拟和分析不同方案下的城市发展情况,用户可以评估不同规划决策对城市的影响,进而做出更明智的决策。

应用场景CIMOC适用于以下几个应用场景:1. 城市规划CIMOC可以帮助城市规划师进行城市规划。

通过构建城市模型和模拟不同规划方案的效果,规划师可以评估不同规划决策的优劣,为城市未来的发展提供科学依据。

2. 建筑设计CIMOC可以支持建筑师进行建筑设计。

通过在城市模型中添加建筑要素,建筑师可以模拟和评估不同建筑设计的效果,确保新建筑物与周围环境的和谐一致。

3. 交通规划CIMOC可以辅助交通规划师进行交通规划。

Abaqus_质量缩放及其结果准确性的评估

Abaqus_质量缩放及其结果准确性的评估

Modeling and Simulation 建模与仿真, 2023, 12(2), 1041-1047 Published Online March 2023 in Hans. https:///journal/mos https:///10.12677/mos.2023.122098Abaqus 质量缩放及其结果准确性的评估赵益兴上海工程技术大学机械与汽车工程学院,上海收稿日期:2023年2月7日;录用日期:2023年3月8日;发布日期:2023年3月15日摘要 Abaqus 质量缩放的目的是为了减少或者节约计算时间。

主要表现在,在显式动力学的求解过程中,实际中的一秒或者零点几秒就需要花费计算机更长的时间进行运算,无法满足实际工程或研究的需要。

有时可以适当扩大研究对象的质量,来达到减少计算机运行计算的时间。

通过简单例子来揭示质量缩放意义和对结果正确性进行评估。

最后对质量缩放的范围和相关设置进行介绍。

关键词Abaqus ,质量缩放,时间,评估Abaqus Mass Scaling and Evaluation of the Accuracy of Its ResultsYixing ZhaoSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai Received: Feb. 7th , 2023; accepted: Mar. 8th , 2023; published: Mar. 15th , 2023AbstractThe purpose of Abaqus mass scaling is to reduce or save calculation time. The main performance is that in the process of solving the explicit dynamics, it takes longer time for the computer to op-erate in one second or a few seconds in practice, which cannot meet the needs of actual engineer-ing or research. Sometimes the quality of the research object can be appropriately expanded to reduce the time of computer operation and calculation. This paper presents the significance of quality scaling and evaluates the correctness of the results through simple examples. The range of mass scaling and related settings is also introduced.赵益兴KeywordsAbaqus, Mass Scaling, Time, EvaluationCopyright © 2023 by author(s) and Hans Publishers Inc.This work is licensed under the Creative Commons Attribution International License (CC BY 4.0)./licenses/by/4.0/1. 引言在实际工程应用中,常常会遇到接触、碰撞等高度非线性情形,而工业仿真软件Abaqus中的显式动力学的优势正是解决此类问题。

Introduction_to_Modeling_and_Simulation[1]

Introduction_to_Modeling_and_Simulation[1]

INTRODUCTION TO MODELING AND SIMULATIONAnu MariaState University of New York at Binghamton Department of Systems Science and Industrial Engineering Binghamton, NY 13902-6000, U.S.A.ABSTRACTThis introductory tutorial is an overview of simulation modeling and analysis. Many critical questions are answered in the paper. What is modeling? What is simulation? What is simulation modeling and analysis? What types of problems are suitable for simulation? How to select simulation software? What are the benefits and pitfalls in modeling and simulation? The intended audience is those unfamiliar with the area of discrete event simulation as well as beginners looking for an overview of the area. This includes anyone who is involved in system design and modification - system analysts, management personnel, engineers, military planners, economists, banking analysts, and computer scientists. Familiarity with probability and statistics is assumed.1WHAT IS MODELING?Modeling is the process of producing a model; a model is a representation of the construction and working of some system of interest. A model is similar to but simpler than the system it represents. One purpose of a model is to enable the analyst to predict the effect of changes to the system. On the one hand, a model should be a close approximation to the real system and incorporate most of its salient features. On the other hand, it should not be so complex that it is impossible to understand and experiment with it. A good model is a judicious tradeoff between realism and simplicity. Simulation practitioners recommend increasing the complexity of a model iteratively. An important issue in modeling is model validity. Model validation techniques include simulating the model under known input conditions and comparing model output with system output.Generally, a model intended for a simulation study is a mathematical model developed with the help of simulation software. Mathematical model classifications include deterministic (input and output variables are fixed values) or stochastic (at least one of the input or output variables is probabilistic); static (time is not taken into account) or dynamic (time-varying interactions among variables are taken into account). Typically, simulation models are stochastic and dynamic.2WHAT IS SIMULATION?A simulation of a system is the operation of a model of the system. The model can be reconfigured and experimented with; usually, this is impossible, too expensive or impractical to do in the system it represents. The operation of the model can be studied, and hence, properties concerning the behavior of the actual system or its subsystem can be inferred. In its broadest sense, simulation is a tool to evaluate the performance of a system, existing or proposed, under different configurations of interest and over long periods of real time.Simulation is used before an existing system is altered or a new system built, to reduce the chances of failure to meet specifications, to eliminate unforeseen bottlenecks, to prevent under or over-utilization of resources, and to optimize system performance. For instance, simulation can be used to answer questions like: What is the best design for a new telecommunications network? What are the associated resource requirements? How will a telecommunication network perform when the traffic load increases by 50%? How will a new routing algorithm affect its performance? Which network protocol optimizes network performance? What will be the impact of a link failure?The subject of this tutorial is discrete event simulation in which the central assumption is that the system changes instantaneously in response to certain discrete events. For instance, in an M/M/1 queue - a single server queuing process in which time between arrivals and service time are exponential - an arrival causes the system to change instantaneously. On the other hand, continuous simulators, like flight simulators and weather simulators, attempt to quantify the changes in a system continuously over time in response toProceedings of the 1997 Winter Simulation Conferenceed. S. Andradóttir, K. J. Healy, D. H. Withers, and B. L. Nelson7controls. Discrete event simulation is less detailed (coarser in its smallest time unit) than continuous simulation but it is much simpler to implement, and hence, is used in a wide variety of situations.Figure 1 is a schematic of a simulation study. The iterative nature of the process is indicated by the system under study becoming the altered system which then becomes the system under study and the cycle repeats. In a simulation study, human decision making is required at all stages, namely, model development, experiment design, output analysis, conclusion formulation, and making decisions to alter the system under study. The only stage where human intervention is not required is the running of the simulations, which most simulation software packages perform efficiently. The important point is that powerful simulation software is merely a hygiene factor - its absence can hurt a simulation study but its presence will not ensure success. Experienced problem formulators and simulation modelers and analysts are indispensable for a successful simulation study.Figure 1: Simulation Study Schematic The steps involved in developing a simulation model, designing a simulation experiment, and performing simulation analysis are:Step 1.Identify the problem.Step 2.Formulate the problem.Step 3.Collect and process real system data.Step 4.Formulate and develop a model.Step 5.Validate the model.Step 6.Document model for future use.Step 7.Select appropriate experimental design.Step 8.Establish experimental conditions for runs.Step 9.Perform simulation runs.Step 10.Interpret and present results.Step 11.Recommend further course of action. Although this is a logical ordering of steps in a simulation study, many iterations at various sub-stages may be required before the objectives of a simulation study are achieved. Not all the steps may be possible and/or required. On the other hand, additional steps may have to be performed. The next three sections describe these steps in detail.3HOW TO DEVELOP A SIMULATION MODEL?Simulation models consist of the following components: system entities, input variables, performance measures, and functional relationships. For instance in a simulation model of an M/M/1 queue, the server and the queue are system entities, arrival rate and service rate are input variables, mean wait time and maximum queue length are performance measures, and 'time in system = wait time + service time' is an example of a functional relationship. Almost all simulation software packages provide constructs to model each of the above components. Modeling is arguably the most important part of a simulation study. Indeed, a simulation study is as good as the simulation model. Simulation modeling comprises the following steps:Step 1.Identify the problem. Enumerate problems with an existing system. Produce requirements for a proposed system.Step 2.Formulate the problem. Select the bounds of the system, the problem or a part thereof, to be studied. Define overall objective of the study and a few specific issues to be addressed. Define performance measures - quantitative criteria on the basis of which different system configurations will be compared and ranked. Identify, briefly at this stage, the configurations of interest and formulate hypotheses about system performance. Decide the time frame of the study, i.e., will the model be used for a one-time decision (e.g., capital expenditure) or over a period of time on a regular basis (e.g., air traffic scheduling). Identify the end user of the simulation model, e.g., corporate management versus a production supervisor. Problems must be formulated as precisely as possible.Step 3.Collect and process real system data. Collect data on system specifications (e.g., bandwidth for a communication network), input variables, as well as8Mariaperformance of the existing system. Identify sources of randomness in the system, i.e., the stochastic input variables. Select an appropriate input probability distribution for each stochastic input variable and estimate corresponding parameter(s).Software packages for distribution fitting and selection include ExpertFit, BestFit, and add-ons in some standard statistical packages. These aids combine goodness-of-fit tests, e.g., χ2 test, Kolmogorov-Smirnov test, and Anderson-Darling test, and parameter estimation in a user friendly format.Standard distributions, e.g., exponential, Poisson, normal, hyperexponential, etc., are easy to model and simulate. Although most simulation software packages include many distributions as a standard feature, issues relating to random number generators and generating random variates from various distributions are pertinent and should be looked into. Empirical distributions are used when standard distributions are not appropriate or do not fit the available system data. Triangular, uniform or normal distribution is used as a first guess when no data are available. For a detailed treatment of probability distributions see Maria and Zhang (1997).Step 4.Formulate and develop a model. Develop schematics and network diagrams of the system (How do entities flow through the system?). Translate these conceptual models to simulation software acceptable form. Verify that the simulation model executes as intended. Verification techniques include traces, varying input parameters over their acceptable range and checking the output, substituting constants for random variables and manually checking results, and animation.Step 5.Validate the model. Compare the model's performance under known conditions with the performance of the real system. Perform statistical inference tests and get the model examined by system experts. Assess the confidence that the end user places on the model and address problems if any. For major simulation studies, experienced consultants advocate a structured presentation of the model by the simulation analyst(s) before an audience of management and system experts. This not only ensures that the model assumptions are correct, complete and consistent, but also enhances confidence in the model.Step 6.Document model for future use. Document objectives, assumptions and input variables in detail.4 HOW TO DESIGN A SIMULATION EXPERIMENT?A simulation experiment is a test or a series of tests in which meaningful changes are made to the input variables of a simulation model so that we may observe and identify the reasons for changes in the performance measures. The number of experiments in a simulation study is greater than or equal to the number of questions being asked about the model (e.g., Is there a significant difference between the mean delay in communication networks A and B?, Which network has the least delay: A, B, or C? How will a new routing algorithm affect the performance of network B?). Design of a simulation experiment involves answering the question: what data need to be obtained, in what form, and how much? The following steps illustrate the process of designing a simulation experiment.Step 7.Select appropriate experimental design. Select a performance measure, a few input variables that are likely to influence it, and the levels of each input variable. When the number of possible configurations (product of the number of input variables and the levels of each input variable) is large and the simulation model is complex, common second-order design classes including central composite, Box-Behnken, and full-factorial should be considered. Document the experimental design.Step 8.Establish experimental conditions for runs. Address the question of obtaining accurate information and the most information from each run. Determine if the system is stationary (performance measure does not change over time) or non-stationary (performance measure changes over time). Generally, in stationary systems, steady-state behavior of the response variable is of interest. Ascertain whether a terminating or a non-terminating simulation run is appropriate. Select the run length. Select appropriate starting conditions (e.g., empty and idle, five customers in queue at time 0). Select the length of the warm-up period, if required. Decide the number of independent runs - each run uses a different random number stream and the same starting conditions -by considering output data sample size. Sample size must be large enough (at least 3-5 runs for each configuration) to provide the required confidence in the performance measure estimates. Alternately, use common random numbers to compare alternative configurations by using a separate random number stream for each sampling process in a configuration. Identify output data most likely to be correlated.Step 9.Perform simulation runs. Perform runs according to steps 7-8 above.5 HOW TO PERFORM SIMULATION ANALYSIS?Introduction to Modeling and Simulation 9Most simulation packages provide run statistics (mean,standard deviation, minimum value, maximum value) on the performance measures, e.g., wait time (non-time persistent statistic), inventory on hand (time persistent statistic). Let the mean wait time in an M/M/1 queue observed from n runs be n 21W ...,,W ,W . It is important to understand that the mean wait time W is a random variable and the objective of output analysis is to estimate the true mean of W and to quantify its variability.Notwithstanding the facts that there are no data collection errors in simulation, the underlying model is fully known, and replications and configurations are user controlled, simulation results are difficult to interpret. An observation may be due to system characteristics or just a random occurrence. Normally, statistical inference can assess the significance of an observed phenomenon, but most statistical inference techniques assume independent, identically distributed (iid) data. Most types of simulation data are autocorrelated, and hence, do not satisfy this assumption. Analysis of simulation output data consists of the following steps.Step 10.Interpret and present results. Compute numerical estimates (e.g., mean, confidence intervals) of the desired performance measure for each configuration of interest. To obtain confidence intervals for the mean of autocorrelated data, the technique of batch means can be used. In batch means, original contiguous data set from a run is replaced with a smaller data set containing the means of contiguous batches of original observations.The assumption that batch means are independent may not always be true; increasing total sample size and increasing the batch length may help.Test hypotheses about system performance.Construct graphical displays (e.g., pie charts, histograms)of the output data. Document results and conclusions.Step 11.Recommend further course of action. This may include further experiments to increase the precision and reduce the bias of estimators, to perform sensitivity analyses, etc.6AN EXAMPLEA machine shop contains two drills, one straightener, and one finishing operator. Figure 2 shows a schematic of the machine shop. Two types of parts enter the machine shop.in sequence. Type 2 parts require only drilling and finishing. The frequency of arrival and the time to be routed to the drilling area are deterministic for both types of parts.Step 1.Identify the problem. The utilization of drills, straightener, and finishing operator needs to be assessed. In addition, the following modification to the original system is of interest: the frequency of arrival of both parts is exponential with the same respective means as in the original system.Step 2.Formulate the problem. The objective is to obtain the utilization of drills, straightener, and finishing operator for the original system and the modification . The assumptions include:♦The two drills are identical♦There is no material handling time between the threeoperations.♦Machine availability implies operator availability.♦Parts are processed on a FIFO basis.♦All times are in minutes.Step 3.Collect and process real system data. At the job shop, a Type 1 part arrives every 30 minutes, and a Type 2 part arrives every 20 minutes. It takes 2 minutes to route a Type 1 part and 10 minutes to route a Type 2 part to the drilling area. Parts wait in a queue till one of the two drilling machines becomes available. After drilling, Type 1parts are routed to the straightener and Type 2 parts are10Mariarouted to the finishing operator. After straightening, Type 1 parts are routed to the finishing operator.The operation times for either part were determined to be as follows. Drilling time is normally distributed with mean 10.0 and standard deviation 1.0. Straightening time is exponentially distributed with a mean of 15.0. Finishing requires 5 minutes per part.Step 4.Formulate and develop a model. A model of the system and the modification was developed using a simulation package. A trace verified that the parts flowed through the job shop as expected.Step 5.Validate the model. The utilization for a sufficiently long run of the original system was judged to be reasonable by the machine shop operators.Step 6.Document model for future use. The models of the original system and the modification were documented as thoroughly as possible.Step 7.Select appropriate experimental design. The original system and the modification described above were studied.Step 8.Establish experimental conditions for runs. Each model was run three times for 4000 minutes and statistical registers were cleared at time 1000, so the statistics below were collected on the time interval [1000, 4000]. At the beginning of a simulation run, there were no parts in the machine shop.Step 9.Perform simulation runs. Runs were performed as specified in Step 8 above.Step 10.Interpret and present results. Table 1 contains the utilization statistics of the three operations for the original system and the modification (in parentheses).Table 1: Utilization StatisticsDrilling Straightening Finishing Mean Run #1 0.83 (0.78) 0.51 (0.58) 0.42 (0.39) Mean Run #2 0.82 (0.90) 0.52 (0.49) 0.41 (0.45) Mean Run #3 0.84 (0.81) 0.42 (0.56) 0.42 (0.40) Std. Dev. Run #1 0.69 (0.75) 0.50 (0.49) 0.49 (0.49) Std. Dev. Run #2 0.68 (0.78) 0.50 (0.50) 0.49 (0.50) Std. Dev. Run #3 0.69 (0.76) 0.49 (0.50) 0.49 (0.49) Mean utilization represents the fraction of time a server is busy, i.e., busy time/total time. Furthermore, the average utilization output for drilling must be divided by the number of drills in order to get the utilization per drill. Each drill is busy about 40% of the time and straightening and finishing operations are busy about half the time. This implies that for the given work load, the system is underutilized. Consequently, the average utilization did not change substantially between the original system and the modification; the standard deviation of the drilling operation seems to have increased because of the increased randomness in the modification. The statistical significance of these observations can be determined by computing confidence intervals on the mean utilization of the original and modified systems.Step 11.Recommend further course of action. Other performance measures of interest may be: throughput of parts for the system, mean time in system for both types of parts, average and maximum queue lengths for each operation. Other modifications of interest may be: the flow of parts to the machine shop doubles, the finishing operation will be repeated for 10% of the products on a probabilistic basis.7 WHAT MAKES A PROBLEM SUITABLE FOR SIMULATION MODELING AND ANALYSIS?In general, whenever there is a need to model and analyze randomness in a system, simulation is the tool of choice. More specifically, situations in which simulation modeling and analysis is used include the following:♦ It is impossible or extremely expensive to observe certain processes in the real world, e.g., next year's cancer statistics, performance of the next space shuttle, and the effect of Internet advertising on a company's sales.♦ Problems in which mathematical model can be formulated but analytic solutions are either impossible (e.g., job shop scheduling problem, high-order difference equations) or too complicated (e.g., complex systems like the stock market, and large scale queuing models).♦ It is impossible or extremely expensive to validate the mathematical model describing the system, e.g., due to insufficient data.Applications of simulation abound in the areas of government, defense, computer and communication systems, manufacturing, transportation (air traffic control), health care, ecology and environment, sociological and behavioral studies, biosciences, epidemiology, services (bank teller scheduling), economics and business analysis.8 HOW TO SELECT SIMULATION SOFTWARE?Although a simulation model can be built using general purpose programming languages which are familiar to the analyst, available over a wide variety of platforms, and less expensive, most simulation studies today are implemented using a simulation package. TheIntroduction to Modeling and Simulation 11advantages are reduced programming requirements; natural framework for simulation modeling; conceptual guidance; automated gathering of statistics; graphic symbolism for communication; animation; and increasingly, flexibility to change the model. There are hundreds of simulation products on the market, many with price tags of $15,000 or more. Naturally, the question of how to select the best simulation software for an application arises. Metrics for evaluation include modeling flexibility, ease of use, modeling structure (hierarchical v/s flat; object-oriented v/s nested), code reusability, graphic user interface, animation, dynamic business graphics, hardware and software requirements, statistical capabilities, output reports and graphical plots, customer support, and documentation.The two types of simulation packages are simulation languages and application-oriented simulators (Table 2). Simulation languages offer more flexibility than the application-oriented simulators. On the other hand, languages require varying amounts of programming expertise. Application-oriented simulators are easier to learn and have modeling constructs closely related to the application. Most simulation packages incorporate animation which is excellent for communication and can be used to debug the simulation program; a "correct looking" animation, however, is not a guarantee of a valid model. More importantly, animation is not a substitute for output analysis.Table 2: Simulation PackagesType OfSimulationPackageExamplesSimulation languages Arena (previously SIMAN), AweSim! (previously SLAM II), Extend, GPSS, Micro Saint,SIMSCRIPT, SLXObject-oriented software: MODSIM III, SIMPLE++ Animation software: Proof AnimationApplication -Oriented Simulators Manufacturing: AutoMod, Extend+MFG,FACTOR/AIM, ManSim/X, MP$IM,ProModel, QUEST, Taylor II, WITNESS Communications/computer: COMNET III,NETWORK II.5, OPNET Modeler, OPNETPlanner, SES/Strategizer, SES/workbench Business: BP$IM, Extend+BPR, ProcessModel, ServiceModel, SIMPROCESS, Time machine Health Care: MedModel9BENEFITS OF SIMULATION MODELING AND ANALYSISAccording to practitioners, simulation modeling and analysis is one of the most frequently used operations research techniques. When used judiciously, simulation modeling and analysis makes it possible to:♦Obtain a better understanding of the system by developing a mathematical model of a system ofinterest, and observing the system's operation in detail over long periods of time.♦Test hypotheses about the system for feasibility.♦Compress time to observe certain phenomena over long periods or expand time to observe a complex phenomenon in detail.♦Study the effects of certain informational, organizational, environmental and policy changes on the operation of a system by altering the system's model; this can be done without disrupting the real system and significantly reduces the risk of experimenting with the real system.♦Experiment with new or unknown situations about which only weak information is available.♦Identify the "driving" variables - ones that performance measures are most sensitive to - and the inter-relationships among them.♦Identify bottlenecks in the flow of entities (material, people, etc.) or information.♦Use multiple performance metrics for analyzing system configurations.♦Employ a systems approach to problem solving.♦Develop well designed and robust systems and reduce system development time.10WHAT ARE SOME PITFALLS TO GUARD AGAINST IN SIMULATION?Simulation can be a time consuming and complex exercise, from modeling through output analysis, that necessitates the involvement of resident experts and decision makers in the entire process. Following is a checklist of pitfalls to guard against.♦Unclear objective.♦Using simulation when an analytic solution is appropriate.♦Invalid model.♦Simulation model too complex or too simple.♦Erroneous assumptions.♦Undocumented assumptions. This is extremely important and it is strongly suggested that assumptions made at each stage of the simulation modeling and analysis exercise be documented thoroughly.♦Using the wrong input probability distribution.♦Replacing a distribution (stochastic) by its mean (deterministic).♦Using the wrong performance measure.♦Bugs in the simulation program.♦Using standard statistical formulas that assume independence in simulation output analysis.♦Initial bias in output data.♦Making one simulation run for a configuration.12MariaIntroduction to Modeling and Simulation 13♦ Poor schedule and budget planning.♦ Poor communication among the personnel involvedin the simulation study.REFERENCESBanks, J., J. S. Carson, II, and B. L. Nelson. 1996.Discrete-Event System Simulation, Second Edition,Prentice Hall.Bratley, P., B. L. Fox, and L. E. Schrage. 1987. A Guideto Simulation, Second Edition, Springer-Verlag.Fishwick, P. A. 1995. Simulation Model Design andExecution: Building Digital Worlds, Prentice-Hall.Freund, J. E. 1992. Mathematical Statistics, Fifth Edition,Prentice-Hall.Hogg, R. V., and A. T. Craig. 1995. Introduction toMathematical Statistics, Fifth Edition, Prentice-Hall.Kleijnen, J. P. C. 1987. Statistical Tools for SimulationPractitioners, Marcel Dekker, New York.Law, A. M., and W. D. Kelton. 1991. SimulationModeling and Analysis, Second Edition,McGraw-Hill.Law, A. M., and M. G. McComas. 1991. Secrets ofSuccessful Simulation Studies, Proceedings of the1991 Winter Simulation Conference, ed. J. M.Charnes, D. M. Morrice, D. T. Brunner, and J. J.Swain, 21-27. Institute of Electrical and ElectronicsEngineers, Piscataway, New Jersey.Maria, A., and L. Zhang. 1997. Probability Distributions,Version 1.0, July 1997, Monograph, Department ofSystems Science and Industrial Engineering, SUNYat Binghamton, Binghamton, NY 13902.Montgomery, D. C. 1997. Design and Analysis ofExperiments, Third Edition, John Wiley.Naylor, T. H., J. L. Balintfy, D. S. Burdick, and K. Chu.1966. Computer Simulation Techniques, John Wiley.Nelson, B. L. 1995. Stochastic Modeling: Analysis andSimulation, McGraw-Hill.AUTHOR BIOGRAPHYANU MARIA is an assistant professor in the departmentof Systems Science & Industrial Engineering at the StateUniversity of New York at Binghamton. She receivedher PhD in Industrial Engineering from the University ofOklahoma. Her research interests include optimizing theperformance of materials used in electronic packaging(including solder paste, conductive adhesives, andunderfills), simulation optimization techniques, geneticsbased algorithms for optimization of problems with alarge number of continuous variables, multi criteriaoptimization, simulation, and interior-point methods.。

开关电源外文翻译(1)

开关电源外文翻译(1)

Modeling, Simulation, and Reduction of Conducted Electromagnetic Interference Due to a PWM Buck Type SwitchingPower Supply IA. FarhadiAbstract:Undesired generation of radiated or conducted energy in electrical systems is called Electromagnetic Interference (EMI). High speed switching frequency in power electronics converters especially in switching power supplies improves efficiency but leads to EMI. Different kind of conducted interference, EMI regulations and conducted EMI measurement are introduced in this paper. Compliancy with national or international regulation is called Electromagnetic Compatibility (EMC). Power electronic systems producers must regard EMC. Modeling and simulation is the first step of EMC evaluation. EMI simulation results due to a PWM Buck type switching power supply are presented in this paper. To improve EMC, some techniques are introduced and their effectiveness proved by simulation.Index Terms:Conducted, EMC, EMI, LISN, Switching SupplyI. INTRODUCTIONFAST semiconductors make it possible to have high speed and high frequency switching in power electronics []1. High speed switching causes weight and volume reduction of equipment, but some unwanted effects such as radio frequency interference appeared []2. Compliance with electromagnetic compatibility (EMC) regulations is necessary for producers to present their products to the markets. It is important to take EMC aspects already in design phase []3. Modeling and simulation is the most effective tool to analyze EMC consideration before developing the products. A lot of the previous studies concerned the low frequency analysis of power electronics components []4[]5. Different types of power electronics converters are capable to be considered as source of EMI. They could propagate the EMI in both radiated and conducted forms. Line Impedance Stabilization Network (LISN)is required for measurement and calculation of conducted interference level []6. Interference spectrum at the output of LISN is introduced as the EMC evaluation criterion []7[]8. National or international regulations are the references for the evaluation of equipment in point of view of EMC []7[]8.II. SOURCE, PATH AND VICTIM OF EMIUndesired voltage or current is called interference and their cause is called interference source. In this paper a high-speed switching power supply is the source of interference.Interference propagated by radiation in area around of an interference source or by conduction through common cabling or wiring connections. In this study conducted emission is considered only. Equipment such as computers, receivers, amplifiers, industrial controllers, etc that are exposed to interference corruption are called victims. The common connections of elements, source lines and cabling provide paths for conducted noise or interference. Electromagnetic conducted interference has two components as differential mode and common mode []9.A. Differential mode conducted interferenceThis mode is related to the noise that is imposed between different lines ofa test circuit by a noise source. Related current path is shown in Fig. 1 []9. The interference source, path impedances, differential mode current and load impedance are also shown in Fig. 1.B. Common mode conducted interferenceCommon mode noise or interference could appear and impose between the lines, cables or connections and common ground. Any leakage current between load and common ground could be modeled by interference voltage source.Fig. 2 demonstrates the common mode interference source, common mode currents I cm1 and I cm2 and the related current paths []9. The power electronics converters performas noise source between lines of the supply network. In this study differential mode of conducted interference is particularly important and discussion will be continued considering this mode only.III. ELECTROMAGNETIC COMPATIBILITY REGULATIONSApplication of electrical equipment especially static power electronic converters in different equipment is increasing more and more. As mentioned before, power electronics converters are considered as an important source of electromagnetic interference and have corrupting effects on the electric networks[]2. High level of pollution resulting from various disturbances reduces the quality of power in electric networks. On the other side some residential, commercial and especially medical consumers are so sensitive to power system disturbances including voltage and frequency variations. The best solution to reduce corruption and improve power quality is complying national or international EMC regulations. CISPR, IEC, FCC and VDE are among the most famous organizations from Europe, USA and Germany who are responsible for determining and publishing the most important EMC regulations. IEC and VDE requirement and limitations on conducted emission are shown in Fig. 3 and Fig. 4 []7[]9.For different groups of consumers different classes of regulations could be complied. Class A for common consumers and class B with more hard limitations for special consumers are separated in Fig. 3 and Fig. 4. Frequency range of limitation is different for IEC and VDE that are 150 kHz up to 30 MHz and 10 kHz up to 30 MHz respectively. Compliance of regulations is evaluated by comparison of measured or calculated conducted interference level in the mentioned frequency range with the stated requirements in regulations. In united European community compliance of regulation is mandatory and products must have certified label to show covering of requirements []8.IV. ELECTROMAGNETIC CONDUCTED INTERFERENCE MEASUREMENTA. Line Impedance Stabilization Network (LISN)1-Providing a low impedance path to transfer power from source to powerelectronics converter and load.2-Providing a low impedance path from interference source, here power electronics converter, to measurement port.Variation of LISN impedance versus frequency with the mentioned topology is presented in Fig. 7. LISN has stabilized impedance in the range of conducted EMI measurement []7.Variation of level of signal at the output of LISN versus frequency is the spectrum of interference. The electromagnetic compatibility of a system can be evaluated by comparison of its interference spectrum with the standard limitations. The level of signal at the output of LISN in frequency range 10 kHz up to 30 MHz or 150 kHz up to 30 MHz is criterion of compatibility and should be under the standard limitations. In practical situations, the LISN output is connected to a spectrum analyzer and interference measurement is carried out. But for modeling and simulation purposes, the LISN output spectrum is calculated using appropriate software.For a simple fixed frequency PWM controller that is applied to a Buck DC/DC) changes slow with respect converter, it is possible to assume the error voltage (veto the switching frequency, the pulse width and hence the duty cycle can be approximated by (1). Vp is the saw tooth waveform amplitude.A. PWM waveform spectral analysisThe normalized pulse train m (t) of Fig. 8 represents PWM switch current waveform. The nth pulse of PWM waveform consists of a fixed component D/fs , in which D is the steady state duty cycle, and a variable component dn/f sthat represents the variation of duty cycle due to variation of source, reference and load.As the PWM switch current waveform contains information concerning EMI due to power supply, it is required to do the spectrum analysis of this waveform in the frequency range of EMI studies. It is assumed that error voltage varies around Veas is shown in (2).with amplitude of Ve1fm represents the frequency of error voltage variation due to the variations of source, reference and load. The interception of the error voltage variation curve and the saw tooth waveform with switching frequency, leads to (3) for the computation of duty cycle coefficients[]10.Maximum variation of pulse width around its steady state value of D is limited to D1. In each period of Tm=1/fm , there will be r=fs/fm pulses with duty cycles of dn. Equation (4) presents the Fourier series coefficients Cn of the PWM waveform m (t). Which have the frequency spectrum of Fig.9.B-Equivalent noise circuit and EMI spectral analysisTo attain the equivalent circuit of Fig.6 the voltage source Vs is replaced by) as it short circuit and converter is replaced by PWM waveform switch current (Iexhas shown in Fig. 10.The transfer function is defined as the ratio of the LISN output voltage to the EMI current source as in (5).The coefficients di, ni (i = 1, 2, … , 4) correspond to the parameters of the equivalent circuit. Rc and Lc are respectively the effective series resistance (ESR) and inductance (ESL) of the filter capacitor Cf that model the non-ideality of this element. The LISN and filter parameters are as follows: CN = 100 nF, r = 5 Ω, l = 50 uH, RN =50 Ω, LN=250 uH, Lf = 0, Cf =0, Rc= 0, Lc= 0, fs =25 kHz The EMI spectrum is derived by multiplication of the transfer function and the source noise spectrum. Simulation results are shown in Fig. 11.VI. PARAMETERS AFFECTION ON EMIA. Duty CycleThe pulse width in PWM waveform varies around a steady state D=0.5. The output noise spectrum was simulated with values of D=0.25 and 0.75 that are shown in Fig.12 and Fig. 13. Even harmonics are increased and odd ones are decreased that isdesired in point of view of EMC. On the other hand the noise energy is distributed over a wider range of frequency and the level of EMI decreased []11.B. Amplitude of duty cycle variationThe maximum pulse width variation is determined by D 1. The EMI spectrum was simulated with D 1=0.05. Simulations are repeated with D 1=0.01 and 0.25 and the resultsare shown in Fig.14 and Fig.15.Increasing of D1 leads to frequency modulation of the EMI signal and reduction in level of conducted EMI. Zooming of Fig. 15 around 7th component of switching frequency in Fig. 16 shows the frequency modulation clearly.C. Error voltage frequencyThe main factor in the variation of duty cycle is the variation of source voltage. The fm=100 Hz ripple in source voltage is the inevitable consequence of the usage of rectifiers. The simulation is repeated in the frequency of fm=5000 Hz. It is shown in Fig. 17 that at a higher frequency for fm the noise spectrum expands in frequency domain and causes smaller level of conducted EMI. On the other hand it is desired to inject a high frequency signal to the reference voltage intentionally.D. Simultaneous effect of parametersSimulation results of simultaneous application of D=0.75, D1=0.25 and fm=5000Hz that lead to expansion of EMI spectrum over a wider frequencies and considerable reduction in EMI level is shown in Fig. 18.VII. CONCLUSIONAppearance of Electromagnetic Interference due to the fast switching semiconductor devices performance in power electronics converters is introduced in this paper. Radiated and conducted interference are two types of Electromagnetic Interference where conducted type is studied in this paper. Compatibility regulations and conducted interference measurement were explained. LISN as an important part of measuring process besides its topology, parameters and impedance were described. EMI spectrum due to a PWM Buck type DC/DC converter was considered and simulated. It is necessary to present mechanisms to reduce the level of Electromagnetic interference. It shown that EMI due to a PWM Buck type switching power supply could be reduced by controlling parameters such as duty cycle, duty cycle variation and reference voltage frequency.VIII. REFRENCES[1] Mohan, Undeland, and Robbins, “Power Electronics Converters, Applications an d Design” 3rd edition, John Wiley & Sons, 2003.[2] P. Moy, “EMC Related Issues for Power Electronics”, IEEE, Automotive Power Electronics, 1989, 28-29 Aug. 1989 pp. 46 – 53.[3] M. J. Nave, “Prediction of Conducted Interference in Switched Mode Power Su pplies”, Session 3B, IEEE International Symp. on EMC, 1986.[4] Henderson, R. D. and Rose, P. J., “Harmonics and their Effects on Power Quality and Transformers”, IEEE Trans. On Ind. App., 1994, pp. 528-532.[5] I. Kasikci, “A New Method for Power Facto r Correction and Harmonic Elimination in Power System”, Proceedings of IEEE Ninth International Conference on Harmonics and Quality of Power, Volume 3, pp. 810 – 815, Oct. 2000.[6] M. J. Nave, “Line Impedance Stabilization Networks: Theory and Applications”, RFI/EMI Corner, April 1985, pp. 54-56.[7] T. Williams, “EMC for Product Designers” 3rd edition 2001 Newnes.[8] B. Keisier, “Principles of Electromagnetic Compatibility”, 3rd edition ARTECH HOUSE 1987.[9] J. C. Fluke, “Controlling Conducted Emission by Design”, Vanhostrand Reinhold 1991.[10] M. Daniel,”DC/DC Switching Regulator Analysis”, McGrawhill 1988[11] M. J. Nave,” The Effect of Duty Cycle on SMPS Common Mode Emission: theory and experiment”, IEEE National Symposium on Electromagnetic Co mpatibility, Page(s): 211-216, 23-25 May 1989.作者:A. Farhadi国籍:伊朗出处:10.11.248.20:8000/rewriter/EI基于压降型PWM开关电源的建模、仿真和减少传导性电磁干扰IIA. Farhadi作者:A. Farhadi国籍:伊朗出处:10.11.248.20:8000/rewriter/EI摘要:电子设备之中杂乱的辐射或者能量叫做电磁干扰(EMI)。

M&S、VV&A、T&E三者的关系

M&S、VV&A、T&E三者的关系

建模与仿真、VV&A、T&E三者的关系刘丽贾荣珍王行仁詹文军摘要:随着建模与仿真(Modeling and Simulation,简称M&S)复杂程度的增加,M&S的正确性和置信度的问题显得非常重要。

M&S必须经过严密的校核、验证和确认(Verification, Validation and Accreditation,简称VV&A),以确保M&S达到预期的目的。

仿真系统还必须经过测试与评估(Test and Evaluation,简称T&E)来支持仿真系统的确认。

为了在M&S 全生命周期中有效地进行VV&A过程和T&E过程,探讨M&S、VV&A、T&E三者的关系是非常必要的。

关键词:建模与仿真;校核、验证与确认;测试与评估中图分类号:TP391.9 文献标识码:A文章编号1004-731x(2000)02-0091-04Relationship of M&S, VV&A and T&ELIU Li, JIA Rong-zhen, WANG Xing-ren, ZHAN Wen-jun(Dept. of Automatic Control, Beijing University of Aeronautics and Astronautics,Beijing 100083, China)Abstract: With the increasing of the complexity of the modeling and simulation (M&S), the correctness and credibility of the M&S is very important. M&S must be subjected to a rigorous verification, validation and accreditation (VV&A) process to ensure that the M&S is suitable for use in its intended purpose. Simulation system must be subjected to test and evaluation (T&E) process to support the accreditation to the simulation system, too. In order to conduct VV&A process and T&E process effectively during M&S full life cycle, it is very necessary to discuss the relationship of M&S, VV&A and T&E.Keywords: modeling and simulation; verification, validation and accreditation; test and evaluation引言建模与仿真技术广泛地应用于各个领域的规划制订、方案论证、设计分析、运行维护、训练及管理等各个阶段。

AMESim Proportional Reversing Valve模型与仿真分析说明书

AMESim Proportional Reversing Valve模型与仿真分析说明书

The Modeling and Simulation of Proportional Reversing Valve Basedon AMESimLin Chuang 1, a , Fei Ye 2,b1-2 School of Mechanical Engineering, Shenyang Jianzhu University, No.9, Hunnan East Road,Hunnan New District, Shenyang City, Liaoning, P.R. China, 110168a *********************,b ***************Keywords: AMESim ;Proportional Reversing Valve ;Modeling and SimulationAbstract . In some models of proportional reversing valve as an example, by Ansoft software andAMESim software respectively establishes the finite element analysis model of proportional solenoidand the proportional reversing valve with simulation model, the output characteristic parameterswhich are obtained by Ansoft software import AMESim proportional solenoid model, settingsimulation parameters, comparing theoretical characteristic curve and the sample parameter, todetermine the proportional solenoid model is correct.By analyzing the proportional reversing valvemodel simulation of the proceeds of the pilot valve to control pressure curve and the main valve coredisplacement curve, known pilot valve for the main valve has good controllability, proportionalreversing valve model to meet the corresponding functional requirement, for it can be used in liftinghydraulic circuit simulation model provides an important reference.1 IntroductionAt present, it is an important means of analysis of the hydraulic system operating characteristicswith the help of AMESim simulation, when the software simulates the truck crane hoisting circuitcontaining the proportional reversing valve,it need the help of HCD function to model the simulationof the proportional reversing valve[1]. Single using HCD to set up the simulation model of theproportional reversing valve,it usually simplifys the proportional electromagnet,uses piecewisefunction simulation of its drive on valve core according to the sample provided parameters,and ishard to ensure the simulation accuracy.The author attempts to use the finite element analysis softwareAnsoft Maxwell to model the proportional electromagnet, through the simulation input/outputcharacteristic of proportional electromagnet, as a proportional directional valve AMESim simulationmodeling of the input signal,to ensure the accuracy of hydraulic system simulation containingproportional control valve.Fig.1 Pilot proportional direction valve structure diagramThis paper is based on the structure and working principle of proportional directional valve, usesAMESim software for modeling and simulation, analysis of the simulation of pilot valve to controlInternational Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2015)pressure curve and the main valve core displacement curve, knowing pilot valve for the main valve has good controllability,proportional directional valve model meeting the corresponding functional requirement, is an important reference for it can be used in lifting hydraulic circuit simulation model provides.2 The working principle of the proportional directional valveFig.1 is the structure diagram of the guide type proportional directional valve, this valve is mainly consisting of two parts, proportion of pilot valve and main valve ,the pilot valve's internal structure includes integrated proportional amplifier, proportional electromagnet and the centring spring, etc.Proportional amplifier amplifys the power of the command signal,inputs proportional current to the proportional electromagnet, proportional electromagnetic outputs electromagnetic force and promotes the forerunner in proportion valve core, at this point, generating a control pressure at the outlet of the pilot valve,it pressd on the one end of both sides of the main valve core, under the action of the pressure,main valve core gradually overcomes the force of the reset spring and begins to move, and forming a valve mouth opening, and the oil flow rate can be changed proportionally and the flow direction can be changed,so to realize the control of the position and speed of the actuator.3 The proportional electromagnet modeling and simulationIn order to study the dynamic output characteristics of proportional electromagnet working alone,it is built in the AMESim simulation model as shown in Fig.2, the main part of its proportional electromagnetic valve is composed of signal input, and the quality of block and the reset spring, the quality of block M is according to the proportional electromagnet armature putting total quality to set, and design of the friction coefficient and reset spring pre-tightening force and stiffness reasonably.Fig.2 Proportional electromagnet AMESim simulation model3.1 AMESim proportional electromagnetic valve is created in the output fileProportional electromagnet GH263-060 as sample, the rated current of 1.11[A] and rated travel 4[mm], suction 145[N] [2], proportional electromagnetic valve is built by using Ansoft software model, when the input rated current is 1.11[A], steady-state output proportional electromagnet force changes between 137 ~ 161[N], the mean value of 148.4[N], 145[N] sample value and the error is only 2.3%, the model correctly reflects the proportional electromagnet output characteristic[3].Proportional electromagnetic valve is set up in the AMESim simulation model, need the electromagnetic force and inductance output characteristics as the data support, through AMESim table edit module will Ansoft Maxwell 2D analysis of the proceeds of the proportional electromagnet electromagnetic force and inductance related data, in the form of a 2D table in AMESim are stored for Diancitie. The data and Dianganxin data format file, so that the proportional electromagnet simulation parameters when imported.3.2 The simulation parameters settingselectromagnet coil inside an electrical current, electromagnetic loop formation on the armature make its output electromagnetic force, after reaching reset spring pre-tightening force, under the impetus of the armature push-rod spring began to shrink.In AMESim environment parameter settings, set parameters for the model on the basis of the above conditions, the main parameter such as Table 1.3.3 Run the simulationAfter setting simulation parameters, operation simulation, get proportional electromagnet simulation results are as follows:(1) The input voltage and current curvesCan be seen from the Fig.3, the input voltage coil is the input voltage proportional solenoid, that is, between 0 and 1 seconds, a linear growth trend, the voltage change range is 0 ~13[V].At this point, as the input voltage, current also increases gradually, in the 1[s] current peak of 1.104[A].The numerical samples with proportional electromagnet rated current numerical 1.11[A] very close.Fig.3 Input voltage and current curve over time(2) The armature current push rod - force change curvesFig.4 Putting armature current - force characteristic curve Fig.5 Theoretical curve Current - power output characteristics of proportional electromagnetic valve is an important index of evaluating its control performance, can be seen in Fig.4 armature putter output force changing with the current, before 0.58[A], electromagnetic force approximation to grow by a certain slope, in 0.58 [A] place, putting electromagnetism appeared inflection point, 0.58[A] and 0.93[A] stage, and electromagnetic force in another slope increase slowly, in 1.1[A] output reach maximum electromagnetic force 144.932[N].Electromagnetic force in the middle stage of slow increase of the reason is that when the armature inductance increases after putting a displacement, the obstacles of thrust increases have played a role, with the increase of current, push rod after a certain stage in electromagnetic force increases rapidly, in the end when the current is 1.1[A], putting the output force is 144.932[N], the sample value and the proportional electromagnet suction numerical rating 145[N] almost unanimously. Armature putter output force rapid rise, slow increase, rapid rise in three stages, and the current proportional electromagnetic valve is shown in Fig.5 - theory of power output characteristic curve, in contrast, the trend and numerical difference is not big, in the range of allowable error.Appear afore-mentioned difference possible factors is: in the process of proportional electromagnet modeling and simulation, to the simplified model, the parameters of the individual module default assumptions, will also introduce a small error[4].Simulation results in view of the above analysis, the proportional solenoid current - force characteristic curve is close to the theoretical analysis, the curves in its value and sample parameter is very close, so after the proportional electromagnet model can be applied to the study.4 The proportional directional valve with the modeling and simulationAs shown in ing AMESim software model pilot proportional directional valve.Fig.6 Pilot proportional directional valve with the simulation model4.1 The simulation parameters settingsPilot valve as the premise of proportional directional valve, the manual input signals accurately convert proportional electromagnet force output signal, and then passed to the control valve core, with the help of drive valve core movement to achieve the goal of controlling the oil is loaded into the main valve core on each control cavity.As drive carrier output proportional electromagnetic valve isthe whole process, the electromagnetic force, putting through the armature effect on pilot valve core, when the output of the electromagnetic force is greater than the reset spring pre-tightening force, valve core began to move and generate the opening of valve port, control the oil into the left side spring cavity of main valve core, when pressure is enough to overcome the right after the spring pre-tightening force and the valve core friction, the main valve core movement to the right, at the same time in the main valve spool valve mouth opening, realize the main valve reversing throttling.In AMESim environment parameter settings, according to the proportional electromagnet simulation model and guiding the operation condition of the proportional directional valve set parameters for the model, main parameter such as Table 2.Table 2 Setting the main parameter of Pilot proportional directional valve Control pressure Constant Source 30[bar]Directional valvespool Piston diameter 15[mm],Rod diameter 2[mm],The rest take a defaultvalueThe main valvemass Mass 0.02[kg],Coefficient of viscous friction 15[ N/(m/s)],Higher displacement limit 15.2[mm],The rest take a default valueThe main valvespring cavityPre-tightening force 15[N],Spring rate 10000[N/m] Traffic sources Constant flow rate 2[L/min]Set the solver Simulation time 1[s],Time interval 0.001[s]4.2 Run the simulationRun the simulation, the curve can be obtained as follows:Fig.7 Pilot valve to control pressure curveFig.7 is pilot valve to control pressure output curve.Pilot valve control output by the pressure on both sides of the main valve core, under the action of the control pressure, the valve core gradually overcome the role of the reset spring and fluid dynamics, and finally formed the movement of the main valve core, forming a valve mouth opening, the main valve to realize reversing the throttle.By figure, output pressure is 0[bar] before 0.13[s], 0.13[s] control pressure output delay, between 0.13[s] to 0.7[s] time, control the pressure gradually increased, until 0.7[s], the output value of the maximum 30[bar].Fig.8 Main valve core displacement curveThe Fig.8 shows that the main valve core displacement curve and pilot control pressure curvetrend is consistent, the main valve core did not produce displacement before 0.13[s], 0.13[s] to 0.7 [s]in the main valve core control pressure, the maximum displacement of the 15.2[mm], curve reflectsthe pilot valve for the main valve with good controllability[5,6].5 SummaryIn AMESim environment, the proportional electromagnet about the working current and the clearance between the output force and the inductance data respectively by 2D table format is converted to the corresponding format file, proportional electromagnetic valve is set up in the AMESim simulation model of the 2D table data import magnet linear converter, the simulation analysis of the dynamic output characteristics in AMESim software, the result of the proceeds of thecurrent - force curve and theoretical curve contrast, verify the validity of the model, for furtherin-depth theoretical research to provide adequate basis.Set in AMESim model based on proportional electromagnet HCD, pilot proportional directional valve with HCD model, through the analysis of the simulation of the pilot valve to control pressure curve and the main valve core displacement curve,can be the guide valve for the main valve has good controllability, can be used as a directional controlvalve is used for lifting hydraulic circuit simulation model.References[1] BideauxE, SeavardaS. Pneumatic library for AMESim. Fluid Power system and technology,(1998),p.185-195.[2] GH263-060 proportional electromagnet samples. /.[3] Roccatelloa, Mancos, Nervegnan. Modeling a variable displacement axial piston pump in amultibody simulation environment [C]. American Society of Mechanical Engineers(ASME), Torino,(2006),p.456-468.[4] Wong, JY. Theory of Ground Vehicles[M].John Wiley&Sons,New York,(2001),p.169-174.[5] Stringer, John. Hydraulic system analysis [J].The Macmillan Pr.Ltd ,1976.[6] Ying Sun, Ping He,Yun qing Zhang, Li ping Chen. Modeling and Co-simulation of HydraulicPower Steering System[C]. 2011 Third International Conference on Measuring Technology andMechatronics Automation. 2011 IEEE:p.595-600.。

the journal of defense modeling and simulation -回复

the journal of defense modeling and simulation -回复

the journal of defense modeling andsimulation -回复题目:《军事建模与仿真的综述》摘要:本文旨在综述"the journal of defense modeling and simulation"(以下简称JDMS)的研究领域、特点以及对军事建模和仿真发展的贡献。

文章从定义、历史背景和分类入手,探讨了军事建模与仿真的重要性以及在军事领域中的广泛应用。

接着,笔者总结了JDMS在相关领域的文章发表情况,并提炼出一些重要的研究主题,如军事作战模拟、军事指挥与控制系统的仿真等。

最后,本文简要介绍了近年来军事建模与仿真领域的发展趋势,并对未来的研究方向提出了展望。

一、引言军事建模与仿真作为一个跨学科领域,涉及多个学科,如计算机科学、数学、工业工程、军事科学等。

它通过数学建模、仿真技术和计算机系统,模拟与预测战场环境和作战结果,为军队提供支持决策的工具和平台。

二、军事建模与仿真的定义与历史背景军事建模与仿真是指通过建立模型和运行仿真程序,代替实际情况发生的模拟活动。

它包括战术、战略、资源规划、军事系统行为等方面的研究。

三、军事建模与仿真的分类1. 物理仿真:对物理实体进行实时仿真,如陆地战斗平台、武器系统等;2. 人类行为仿真:模拟军事人员的行为、意识和决策过程;3. 战术和战役仿真:模拟战术级指挥过程、兵力调度等;4. 作战仿真:模拟战斗过程和结果。

四、JDMS的研究领域与重要性近年来,JDMS在军事建模与仿真领域的研究取得了显著的进展。

它涵盖了从理论研究到实践应用的方方面面,包括作战仿真、指挥与控制系统、战术级决策等。

五、JDMS在军事建模与仿真领域的研究贡献1. 军事作战模拟:JDMS发表了大量关于军事作战模拟的研究论文,包括对实体、环境、战术行为等方面的建模与仿真技术。

2. 军事指挥与控制系统的仿真:该领域是JDMS的研究重点之一,涉及到指挥系统的建模、仿真和评估方法,以及基于仿真的指挥决策支持系统等。

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SPEEDAM2006International Symposium on Power Electronics,Electrical Drives, Automation and MotionModeling and Simulation of Sensorless Controlof PMBLDC MotorUsing Zero-Crossing Back E.M.F DetectionR. Somanatham, P. V. N. Prasad, A. D. RajkumarDepartment of Electrical Engineering, University College of EngineeringOsmania University, Hyderabad – 500 007, A.P. (India)Abstract--In the present scheme, modeling and simulation of sensorless control of Permanent Magnet Brushless DC (PMBLDC) motor is carried out using zero-crossing back e.m.f technique. Since the neutral point of star connected machine is floating and not accessible to detect zero-crossing points, line back e.m.f information is considered. The motor is commutated at zero-crossing point of back e.m.f at 00 delay (no delay), 18.50 delay and 280 delay instants of commutation from zero-crossing point of line back e.m.f signals. The various waveforms like line back e.m.f, phase currents, rotor position, speed, torque with respect to time at no-load, half-load and full-load are obtained. From the results it is observed that the torque developed by the motor at larger delay angles is more pulsating due to more peak to peak currents.I ndex Terms--Brushless machines, Inverters, Permanent magnet motors, Zero-crossingI.INTRODUCTIONThe latest advances in permanent magnet materials, solid-state devices and micro-electronics have contributed to new energy efficient electric drives which use Permanent Magnet Brushless DC motors. These motors have higher efficiency, better power factor, better dynamic performance and better output power per unit mass & volume than cage Induction motors without sacrificing the reliability [1]. Because of these reasons PMBLDC motors are extensively used in wide range of applications including information technology equipment such as, computers, printers & scanners, household appliances, aerospace, electric vehicles, robotics etc.There are two methods of controlling PMBLDC motors namely sensor control and sensorless control. The later can be preferred due to advantages like cost reduction, reliability improvement, elimination of difficulty in maintaining the sensors etc. The sensorless mode of control is highly advantageous if the motor is operating in dusty or oily environments, where occasional cleaning of Hall sensors is required for proper sensing of rotor position [2]. The sensorless method can also be adopted if the motor is mounted in a less accessible location. Further, in the motors rated below one-watt This work was supported by Technical Education & Quality Improvement Programme (TEQIP) of Ministry of Human Resources & Development, Govt. of India. rating, the power consumption by the position sensorscan substantially reduce the motor efficiency and in compact units such as computer hard disk drives it may not be possible to accommodate position sensors.A sensorless operation of a PMBLDC motor using zero-crossing back e.m.f technique is modeled and simulated using Simulink. The responses of line backe.m.fs, phase currents, speed, rotor position and torqueare obtained and analyzed for different delay angles at different load torques from no-load to full-load.II. SENSORLESS ROTOR POSITION DETECTIONThe rotor position information of the PMBLDC motorcan be obtained using anyone of the four techniques outlined in [1,3,4]. They are: (a) Detection of the backe.m.f (zero-crossing approach, phase locked loop technique, e.m.f integration approach) (b) Detection of stator third harmonic voltage (c) Detection of conducting interval of free-wheeling diodes connected in anti-parallelwith the solid-state switches (d) Monitoring the inductance variation in the d-q axes.The magnitude and polarity of line back e.m.fs changeat the instant of commutation. The zero-crossing of thesee.m.fs give specific instants of commutation and can be used in the same way as Hall-effect sensor signals to switch the devices in a particular sequence in power inverter. Since the neutral point of the machine is floatingand not accessible, it is better to use the line back e.m.f information to switch the devices. Each transition occursat every 60 electrical degrees and thus there are six commutation instants in one cycle.The reliability of this method depends on the measured signals. Since the back e.m.f varies with the speed, the method is suitable only when a minimum speed of about5% of the rated speed is achieved. Hence, during startingthe motor operates in open loop mode and is driven bythe inverter which operates in 1200 conduction mode. When the motor reaches the minimum speed to facilitate zero-crossing detection of back e.m.f, the control is shifted to zero-crossing detection circuit and the drive operates in the closed loop mode.The sequence of operation of the motor during startingand running periods can be seen in the block schematic diagram as shown in Fig. 1.III. MODELING AND OPERATIONA .AssumptionsThe assumptions made for the development ofPMBLDC motor model are: (a) Induced currents in therotor due to stator are neglected (b) Iron and stray lossesare neglected (c) Three phase star connected motor isconsidered.B .Simulink ModelSimulink model [3,5,6] of s ensorless controlPMBLDC motor is shown in the Fig.2. The important blocks for the sensorless operation are: (a) PMBLDC motor (b) Zero-crossing detection circuit (c) Logical Inverter (d) 1200 Inverter block (e) Changer block (f) Timer (g) Load and (h) Reset controllerThe PMBLDC motor model is developed based on the state-space form [2]Bu Ax x x(1)where,>@T C B A I I I x T Z (2) >@TL C B A T V V V u (3) and A & B are state-space matrices. V A,V B & V C are the impressed voltages on the motor windings and T L is the external load torque on the motor. The voltages are generally derived from the Simulink sources. The state-space model calculates the line currents I A,I B & I C,the speed Ȧ (radians/sec) and the rotor position ș (radians), the phase back e.m.fs E A,E B & E C , the friction faced by the motor and the neutral voltage. The Reset controller is generally used to reset the elements of the matrices A and B in the state-space model.Zero-crossing detection block is used to determine the instants of line back e.m.fs, which can be used for commutating the currents in the PMBLDC motor. It is also used for developing the delays in the instants ofcommutations. Based on these signals the Logicalinverter is operated. The Logical inverter operates onlywhen it is possible to detect the back e.m.fs. Since, backe.m.f is a function of speed of the motor, at the instant ofstarting and during starting period its value is very less. Hence the motor is fed from 1200Inverter block during starting period. The 1200Inverter block operates for a given threshold time by the end of which the developed back e.m.f can be easily sensed. This threshold time is set by the Timer block. After the threshold time, the Changer block feeds the phase voltages developed by the Logicalinverter through the driver circuits to the motor. A step load torque is applied to the motor and its value dependson load applied to the motor. C .SimulationThe PMBLDC motor is simulated for different instants of commutation. The instants of commutation are no-delay, 18.50 delay and 280delay from the instant of zero-crossing points. At no-load, half-load and full-load of themotor, simulation is carried out for the above delays andthe various plots are obtained. A step load torque is applied to the motor at 0.05 sec. The specifications of motor and inverter are given in Appendix.The waveforms of line back e.m.f’s (E) with zero-crossing detection and commutation signals, speed(N rpm), line currents (I), rotor position and torque at no-load for 00 delay and 280delay angles are shown in Fig.3 and Fig.4 respectively. The respective waveforms at full-load for the same delay angles are shown in Fig.5 andFig.6. Fig.1: Block Schematic Diagram of Sensorless Operation of PMBLDC MotorF i g .2: S i m u l i n k M o d e l o f S e n s o r l e s s C o n t r o l P M B L D C M o t o rI AI B (A) I C ș (rad.)Fig.3b: Phase currents & Rotor position plotsat No-Load & No DelayI A I B (A) (rad.)T o r q u e ( N -m )Time (sec)Fig.4c. Torque at No-Load &28 Deg. DelayE AB E BC(V)E CAN (rpm)E AB E BC (V) E CAN (rpm)Fig.4a: Line back e.m.fs & Speed plotsat No-Load & 28 Deg. DelayTime (sec)Time (sec)Fig.3c. Torque at No-Load &No DelayTime (sec)T o r q u e ( N -m )T o r q u e (N .m )Fig.5c: Torque at Full-Load & No delayo r q u e (N .m )Time (sec)Fig.6c: Torque at Full-Load & 28 Deg. delayI AI B (A) I C ș (rad.)Fig.5b: Phase currents & Rotor position atFull-Load & No delayTime (sec)I A I B (A) I Cș (rad.)Fig.6b: Phase currents & Rotor position atFull-Load & 28 Deg. delayTime (sec)E ABE BC (V) E CA N (rpm)Fig.6a: Line back e.m.fs & Speed plots atFull-Load & 28 Deg. delayTime (sec)E ABE BC (V)E CA N (rpm)Fig.5a: Line back e.m.fs & Speed plots atFull-Load& No delayTime (sec)IV. RESULTS AND ANALYSIS At no-load, a marginal increase in speed ripple (N p ) is observed as the delay angle is increased from 00 to 280.But the steady-state speed is reached in nearly half of the time taken for 00 delay operation. At larger delay angles, the decrease in frequency (f) is characterized by decrease in line back e.m.f and speed. These are evident from Fig.3a and Fig.4a. Also, the peak current (I p ) is increased by more than 1.8 times as shown in Fig.3b and Fig.4b. The torque ripple (T rp ) is found to be increased by more than four times, though the initial peak torque is same in both cases of delay angles. This can be observed in Fig.3c and Fig.4c.At full-load, there is an increase in speed ripple and no change in time of steady-state speed (t SS ) for the same change in delay angle. These responses can be seen from Fig.5a and Fig.6a. The change in frequencies of phase currents and rotor position can be observed when a step load is applied to the motor. This is in agreement with drop in speed and fall in slope of rotor position with load. The peak current is increased by more than 1.4 times. Fig.5a & Fig.5b and Fig.6a & Fig.6b match with these conclusions. The torque ripple is found to be non-uniform and increased by more than four times, when the delay angle is increased as shown in Fig.5c and Fig.6c. At any load, the high frequency pulsating torques are due to the presence of sixth harmonic torque components.The peak values of back e.mfs (E p ) are also obtained. These variations are in same proportion with the variations of frequencies. The performance of the motor at different loads is summarized as shown in Table I.TABLE IPERFORMANCE ANALYSISNo-LoadS.No. 00 Delay 18.50 Delay 280 Delay1. t SS (sec.) 0.10 0.08 0.05 2. N p (rpm) 0 17.0 34.03. f (Hz.) 80.0 60.0 53.34. N (rpm) 2400 1800 1600 5. E p (V) 25.0 18.5 17.0 6. I p (A) 14.0 20.5 25.5 7. T rp (N.m) 0.18 0.620.79 Half-Load1. t SS (sec.) 0.15 0.15 0.152. N p (rpm) 0 17.0 35.03. f (Hz.) 67.4 50.0 42.94. N (rpm) 2022 1500 1286 5. E p (V) 20.5 16.0 13.5 6. I p (A) 17.5 25.0 27.5 7. T rp (N.m) 0.19 0.660.98 Full-Load1. t SS (sec.) 0.15 0.15 0.15 2. N p (rpm) 0 39.0 77.03. f (Hz.) 53.5 41.9 36.04. N (rpm) 1603 1257 1081 5. E p (V) 17.0 13.0 11.5 6. I p (A) 20.5 27.5 29.5 7. T rp (N.m) 0.22 0.771.03V. CONCLUSIONSModeling and simulation of a sensorless controlled Permanent Magnet Brushless DC motor is carried out at different loads. Initially the stator windings of the motor are excited with an inverter which operates in 1200 mode conduction for a threshold period. When the motor reaches the minimum speed to facilitate zero-crossing detection of back e.m.f, the control is transferred to zero-crossing detection circuit. Then, a closed loop operation is carried out where a pair of stator windings of the motor is excited by the logical inverter. The main advantage of the present scheme is that the sensorless operation can be easily implemented without the neutral point. At any load, as the delay angle increases, the speed, frequency & back e.m.f decease and speed ripple, current & torqueripple increase. At any delay angle, as the load increases,the response of the motor is similar. The torque pulsations are uniform for no-delay and become non-uniform and more at higher delay angles.APPENDIXA. PMBLDC Motor Specifications:Voltage: V = 40.0 V Current: I = 17.4 A Torque: T = 0.90 N.m Self inductance of the winding: l = 2.72 mH Mutual inductance of the winding: M = 1.5 mH Back e.m.f constant: ke = 0.5128V/rad/sec Torque constant : kt = 0.049N-m/A Motor inertia: j = 0.0002 kg-m 2Winding resistance/phase: r = 0.7 :Motor damping constant: d = 0 N.m/rad/sec Number of poles: p = 4 Initial angular displacement: T = 0 rad. B. Three-Phase Inverter:Input DC Voltage: 160 V, No. of switches: 6ACKNOWLEDGEMENTThe authors express their sincere thanks to the Principal, University College of Engineering, Osmania University for continuous encouragement.REFERENCES[1] Jacek F. Gieras, Mitchell Wing: Permanent MagnetMotor Technology Design and Applications , Marcel Decker, Inc, 2002[2] 2003 Microchip Technology Inc., pp13[3] R.Krishnan: Electric Motor Drives Modeling,Analysisand Control, Prentice Hall of India Private Limited, New Delhi, 2002, pp 578-580. [4] G.K.Dubey: Power Semiconductor Control Drives ,Prentice-Hall, Eaglewood Cliffs, 1989 [5] Chee-Mun Ong: Dynamics of Electrical Machinery UsingMATLAB/ SIMULINK , Prentice Hall PTR, 1998 [6] MATLAB 6.5, The Mathworks Inc., 2002。

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