Models and Simulation for Analysis of a Computer Network
SIGNALINTEGRITY（信号完整性）外文翻译
SIGNAL INTEGRITYRaymond Y. Chen, Sigrid, Inc., Santa Clara, CaliforniaIntroductionIn the realm of high-speed digital design, signal integrity has become a critical issue, and is posing increasing challenges to the design engineers. Many signal integr ity problems are electromagnetic phenomena in nature and hence related to the EMI/EMC discussions in the previous sections of this book. In this chapter, we will discuss what the typical signal integrity problems are, where they come from, why it is important to understand them and how we can analyze and solve these issues. Several software tools available at present for signal integrity analysis and current trends in this area will also be introduced.The term Signal Integrity (SI) addresses two concerns in the electrical design aspects – the timing and the quality of the signal. Does the signal reach its destination when it is supposed to? And also, when it gets there, is it in good condition? The goal of signal integrity analysis is to ensure reliable high-speed data transmission. In a digital system, a signal is transmitted from one component to another in the form of logic 1 or 0, which is actually at certain reference voltage levels. At the input gate of a receiver, voltage above the reference value Vih is considered as logic high, while voltage below the reference value Vil is considered as logic low. Figure 14-1 shows the ideal voltage waveform in the perfect logic world, whereas Figure 14-2 shows how signal will look like in a real system. More complex data, composed of a string of bit 1 and 0s, are actually continuous voltage waveforms. The receiving component needs to sample the waveform in order to obtain the binary encoded information. The data sampling process is usually triggered by the rising edge or the falling edge of a clock signal as shown in the Figure 14-3. It is clear from the diagram that the data must arrive at the receiving gate on time and settle down to a non-ambiguous logic state when the receiving component starts to latch in. Any delay of the data or distortion of the data waveform will result in a failure of the data transmission. Imagine if the signal waveform in Figure 14-2 exhibits excessive ringing into the logic gray zone while the sampling occurs, then the logic level cannot be reliably detected.SI ProblemsT ypical SI Problems“Timing” is everything in a high-speed system. Signal timing depends on the delay caused by the physical length that the signal must propagate. It also depends on the shape of the waveform w hen the threshold is reached. Signal waveform distortions can be caused by different mechanisms. But there are three mostly concerned noise problems:•Reflection Noise Due to impedance mismatch, stubs, visa and other interconnect discontinuities. •Crosstalk Noise Due to electromagnetic coupling between signal traces and visa.•Power/Ground Noise Due to parasitic of the power/ground delivery system during drivers’ simultaneous switching output (SSO). It is sometimes also called Ground Bounce, Delta-I Noise or Simultaneous Switching Noise (SSN).Besides these three kinds of SI problems, there is other Electromagnetic Compatibility or Electromagnetic Interference (EMC/EMI) problems that may contribute to the signal waveform distortions. When SI problems happen and the system noise margin requirements are not satisfied – the input to a switching receiver makes an inflection below Vih minimum or above Vil maximum; the input to a quiet receiver rises above V il maximum or falls below Vih minimum; power/ground voltage fluctuations disturb the data in the latch, then logic error, data drop, false switching, or even system failure may occur. These types of noise faults are extremely difficult to diagnose and solve after the system is built or prototyped. Understanding and solving these problems before they occur will eliminate having to deal with them further into the project cycle,and will in turn cut down the development cycle and reduce the cost[1]. In the later part of thischapter, we will have further investigations on the physical behavior of these noise phenomena, their causes, their electrical models for analysis and simulation, and the ways to avoid them.1. Where SI Problems HappenSince the signals travel through all kinds of interconnections inside a system, any electrical impact happening at the source end, along the path, or at the receiving end, will have great effects on the signal timing and quality. In a typical digital system environment, signals originating from the off-chip drivers on the die (the chip) go through c4 or wire-bond connections to the chip package. The chip package could be single chip carrier or multi-chip module (MCM). Through the solder bumps of the chip package, signals go to the Printed Circuit Board (PCB) level. At this level, typical packaging structures include daughter card, motherboard or backplane. Then signals continue to go to another system component, such as an ASIC (Application Specific Integrated Circuit) chip, a memory module or a termination block. The chip packages, printed circuit boards, as well as the cables and connecters, form the so-called different levels of electronic packaging systems, as illustrated in Figure 14-4. In each level of the packaging structure, there are typical interconnects, such as metal traces, visa, and power/ground planes, which form electrical paths to conduct the signals. It is the packaging interconnection that ultimately influences the signal integrity of a system.2. SI In Electronic PackagingTechnology trends toward higher speed and higher density devices have pushed the package performance to its limits. The clock rate of present personal computers is approaching gigahertz range. As signal rise-time becomes less than 200ps, the significant frequency content of digital signals extends up to at least 10 GHz. This necessitates the fabrication of interconnects and packages to be capable of supporting very fast varying and broadband signals without degrading signal integrity to unacceptable levels. While the chip design and fabrication technology have undergone a tremendous evolution: gate lengths, having scaled from 50 µm in the 1960s to 0.18 µm today, are projected to reach 0.1 µm in the next few years; on-chip clock frequency is doubling every 18 months; and the intrinsic delay of the gate is decreasing exponentially with time to a few tens of Pico-seconds. However, the package design has lagged considerably. With current technology, the package interconnection delay dominates the system timing budget and becomes the bottleneck of the high-speed system design. It is generally accepted today that package performance is one of the major limiting factors of the overall system performance.Advances in high performance sub-micron microprocessors, the arrival of gigabit networks, and the need for broadband Internet access, necessitate the development of high performance packaging structures for reliable high-speed data transmission inside every electronics system.Signal integrity is one of the most important factors to be considered when designing these packages (chip carriers and PCBs) and integrating these packages together.3、SI Analysis3.1. SI Analysis in the Design FlowSignal integrity is not a new phenomenon and it did not always matter in the early days of the digital era. But with the explosion of the information technology and the arrival of Internet age, people need to be connected all the time through various high-speed digital communication/computing systems. In this enormous market, signal integrity analysis will play a more and more critical role to guarantee the reliable system operation of these electronics products. Without pre-layout SI guidelines, prototypes may never leave the bench; without post-layout SI verifications, products may fail in the field. Figure 14-5 shows the role of SI analysis in the high-speed design process. From this chart, we will notice that SI analysis is applied throughout the design flow and tightly integrated into each design stage. It is also very common to categorize SI analysis into two main stages: reroute analysis and post route analysis.In the reroute stage, SI analysis can be used to select technology for I/Os, clock distributions, chip package types, component types, board stickups, pin assignments, net topologies, and termination strategies. With various design parameters considered, batch SI simulations on different corner cases will progressively formulate a set of optimized guidelines for physical designs of later stage. SI analysis at this stage is also called constraint driven SI design because the guidelines developed will be used as constraints for component placement and routing. The objective of constraint driven SI design at the reroute stage is to ensure that the signal integrity of the physical layout, which follows the placement/routing constraints for noise and timing budget, will not exceed the maximum allowable noise levels. Comprehensive and in-depth reroute SI analysis will cut down the redesign efforts and place/route iterations, and eventually reduce design cycle.With an initial physical layout, post route SI analysis verifies the correctness of the SI design guidelines and constraints. It checks SI violations in the current design, such as reflection noise, ringing, crosstalk and ground bounce. It may also uncover SI problems that are overlooked in the reroute stage, because post route analysis works with physical layout data rather than estimated data or models, therefore it should produce more accurate simulation results.When SI analysis is thoroughly implemented throughout the whole design process, a reliable high performance system can be achieved with fast turn-around.In the past, physical designs generated by layout engineers were merely mechanical drawings when very little or no signal integrity issues were concerned. While the trend of higher-speed electronics system design continues, system engineers, responsible for developing a hardware system, are getting involved in SI and most likely employ design guidelines and routing constraints from signal integrity perspectives. Often, they simply do not know the answers to some of the SI problems because most of their knowledge is from the engineers doing previous generations of products. To face this challenge, nowadays, a design team (see Figure 14-6) needs to have SI engineers who are specialized in working in this emerging technology field. When a new technology is under consideration, such as a new device family or a new fabrication process for chip packages or boards, SI engineers will carry out the electrical characterization of the technology from SI perspectives, and develop layout guideline by running SI modeling and simulation software [2]. These SI tools must be accurate enough to model individual interconnections such as visa, traces, and plane stickups. And they also must be very efficient so what-if analysis with alternative driver/load models and termination schemes can be easily performed. In the end, SI engineers will determine a set of design rules and pass them to the design engineers and layout engineers. Then, the design engineers, who are responsible for the overall system design, need to ensure the design rules are successfully employed. They may run some SI simulations on a few critical nets once the board is initially placed and routed. And they may run post-layout verifications as well. The SI analysis they carry out involves many nets. Therefore, the simulation must be fast, though it may not require the kind of accuracy that SI engineers are looking for. Once the layout engineers get the placement and routing rules specified in SI terms, they need to generate an optimized physical design based on these constraints. And they will provide the report on any SI violations in a routed system using SI tools. If any violations are spotted, layout engineers will work closely with design engineers and SI engineers to solve these possible SI problems.3.2.Principles of SI AnalysisA digital system can be examined at three levels of abstraction: log ic, circuit theory, and electromagnetic (EM) fields. The logic level, which is the highest level of those three, is where SI problems can be easily identified. EM fields, located at the lowest level of abstraction, comprise the foundation that the other levels are built upon [3]. Most of the SI problems are EM problems in nature, such as the cases of reflection, crosstalk and ground bounce. Therefore, understanding the physical behavior of SI problems from EM perspective will be very helpful. For instance, in the following multi-layer packaging structure shown in Figure 14-7, a switching current in via a will generate EM waves propagating away from that via in the radial direction between metal planes. The fields developed between metal planes will cause voltage variations between planes (voltage is the integration of the E-field). When the waves reach other visa, they will induce currents in those visa. And the induced currents in that visa will in turn generate EM waves propagating between the planes. When the waves reach the edges of the package, part of them will radiate into the air and part of them will get reflected back. When the waves bounce back and forth inside the packaging structure and superimpose to each other, resonance will occur. Wave propagation, reflection, coupling and resonance are the typical EM phenomena happening inside a packaging structure during signal transients. Even though EM full wave analysis is much more accurate than the circuit analysis in the modeling of packaging structures, currently, common approaches of interconnect modeling are based on circuit theory, and SI analysis is carried out with circuit simulators. This is because field analysis usually requires much more complicated algorithms and much larger computing resources than circuit analysis, and circuit analysis provides good SI solutions at low frequency as an electrostatic approximation.Typical circuit simulators, such as different flavors of SPICE, employ nodal analysis and solve voltages and currents in lumped circuit elements like resistors, capacitors and inductors. In SI analysis, an interconnect sometimes will be modeled as a lumped circuit element. For instance, a piece of trace on the printed circuit board can be simply modeled as a resistor for its finite conductivity. With this lumped circuit model, the voltages along both ends of the trace are assumed to change instantaneously and the travel time for the signal to propagate between the two ends is neglected. However, if the signal propagation time along the trace has to be considered, a distributed circuit model, such as a cascaded R-L-C network, will be adopted to model the trace. To determine whether the distributed circuit model is necessary, the rule of thumb is – if the signal rise time is comparable to the round-trip propagation time, you need to consider using the distributed circuit model.For example, a 3cm long stripling trace in a FR-4 material based printed circuit board will exhibits 200ps propagation delay. For a 33 MHz system, assuming the signal rise time to be 5ns, the trace delay may be safely ignored; however, with a system of 500 MHz and 300ps rise time, the 200ps propagation delay on the trace becomes important and a distributed circuit model has to be used to model the trace. Through this example, it is easy to see that in the high-speed design, with ever-decreasing signal rise time, distributed circuit model must be used in SI analysis.Here is another example. Considering a pair of solid power and ground planes in a printed circuit board with the dimension of 15cm by 15cm, it is very natural to think the planes acting as a large, perfect, lumped capacitor, from the circuit theory point of view. The capacitor model C= erA/d, an electro-static solution, assumes anywhere on the plane the voltages are the same and all the charges stored are available instantaneously anywhere along the plane. This is true at DC and low frequency. However, when the logics switch with a rise time of 300ps, drawing a large amount of transient currents from the power/ground planes, they perceive the power/ground structure as a two-dimensional distributed network with significant delays. Only some portion of the plane charges located within a small radius of the switching logics will be able to supply the demand. And voltages between the power/ground planes will have variations at different locations. In this case, an ideal lumped capacitor model is obviously not going to account for the propagation effects. Two-dimensional distributed R-L-C circuit networks must be used to model the power/ground pair.In summary, as the current high-speed design trend continues, fast rise time reveals the distributed nature of package interconnects. Distributed circuit models need to be adopted to simulate the propagation delay in SI analysis. However, at higher frequencies, even the distributed circuit modeling techniques are not good enough, full wave electromagnetic field analysis based on solving Maxwell’s equations must come to play. As presen ted in later discussions, a trace will not be modeled as a lumped resistor, or a R-L-C ladder; it will be analyzed based upon transmission line theory; and a power/ground plane pair will be treated as a parallel-plate wave guide using radial transmission line theory.Transmission line theory is one of the most useful concepts in today’s SI analysis. And it is a basic topic in many introductory EM textbooks. For more information on the selective reading materials, please refer to the Resource Center in Chapter 16.In the above discussion, it can be noticed that signal rise time is a very important quantity in SI issues. So a little more expanded discussion on rise time will be given in the next section.信号完整性介绍在高速数字设计领域，信号完整性已经成为一个严重的问题，是造成越来越多的挑战的设计工程师。
Modeling,Simulat...
Book reviewModeling,Simulation,and Control of Flexible Manufacturing Systems ±A Petri Net Approach;Meng Chu Zhou;Kurapati Venkatesh;Yushun Fan;World Scienti®c,Singapore,19991.IntroductionA ¯exible manufacturing system (FMS)is an automated,mid-volume,mid-va-riety,central computer-controlled manufacturing system.It can be used to produce a variety of products with virtually no time lost for changeover from one product to the next.FMS is a capital-investment intensive and complex system.In order to get the best economic bene®ts,the design,implementation and operation of FMS should be carefully made.A lot of researches have been done regarding the modeling,simulation,scheduling and control of FMS [1±6].From time to time,Petri net (PN)method has also been used as a tool by di erent researcher in studying the problems regarding the modeling,simulation,scheduling and control of FMS.A lot of papers and books have been published in this area [7±14].``Modeling,Simulation,and Control of Flexible Manufacturing Systems ±A PN Approach''is a new book written by Zhou and Venkatesh which is focused on studying FMS using PN as a systematic method and integrated tool.The book's contents can be classi®ed into four parts.The four parts are introduction part (Chapter 1to Chapter 4),PNs application part (Chapter 5to Chapter 8),new research results part (Chapter 9to Chapter 13),and future development trend part (Chapter 14).In the introduction part,the background,motivation and objectives of the book are described in Chapter 1.The brief history of manufacturing systems and PNs is also presented in Chapter 1.The basic de®nitions and problems in FMS design and implementation are introduced in Chapter 2.The authors divide FMS related problems into two major areas ±managerial and technical.In Chapter 4,basic de®nitions,properties,and analysis techniques of PNs are presented,Chapter 4can be used as the fundamentals of PNs for those who are not familiar with PN method.In Chapter 3,the authors presented their approach to studying FMS related prob-lems,the approach uses PNs as an integrated tool and methodology in FMS design and implementation.In Chapter 3,various applications in modeling,analysis,sim-ulation,performance evaluation,discrete event control,planning and scheduling of FMS using PNs are presented.Through reading the introduction part,the readers can obtain basic concepts and methods about FMS and PNs.The readers can also get a clear picture about the relationshipbetween FMS and PNs.Mechatronics 11(2001)947±9500957-4158/01/$-see front matter Ó2001Elsevier Science Ltd.All rights reserved.PII:S 0957-4158(00)00057-X948Book review/Mechatronics11(2001)947±950The second part of the book is about PNs applications.In this part,various applications of using PNs in solving FMS related problems are introduced.FMS modeling is the basis for simulation,analysis,planning and scheduling.In Chapter5, after introduction of several kinds of PNs,a general modeling method of FMS using PNs is given.The systematic bottom-up and top-down modeling method is pre-sented.The presented method is demonstrated by modeling a real FMS cell in New Jersey Institute of Technology.The application of PNs in FMS performance analysis is introduced in Chapter 6.The stochastic PNs and the time distributions are introduced in this Chapter. The analysis of a¯exible workstation performance using the PN tool called SPNP developed at Duke University is given in Section6.4.In Chapter7,the procedures and steps involved for discrete event simulation using PNs are discussed.The use of various modeling techniques such as queuing network models,state-transition models,high-level PNs,object-oriented models for simulations are brie¯y explained.A software package that is used to simulate PN models is introduced.Several CASE tools for PNs simulations are brie¯y intro-duced.In Chapter8,PNs application in studying the di erent e ects between push and pull paradigms is shown.The presented application method is useful for the selection of suitable management paradigm for manufacturing systems.A manufacturing system is modeled considering both push and pull paradigms in Section8.3which is used as a practical example.The general procedures for performance evaluation of FMS with pull paradigm are given in Section8.4.The third part of the book is mainly the research results of the authors in the area of PNs applications.In Chapter9,an augmented-timed PN is put forward. The proposed method is used to model the manufacturing systems with break-down handling.It is demonstrated using a¯exible assembly system in Section9.3. In Chapter10,a new class of PNs called Real-time PN is proposed.The pro-posed PN method is used to model and control the discrete event control sys-tems.The comparison of the proposed method and ladder logic diagrams is given in Chapter11.Due to the signi®cant advantages of Object-oriented method,it has been used in PNs to de®ne a new kind of PNs.In Chapter12,the authors propose an Object-oriented design methodology for the development of FMS control software.The OMT and PNs are integrated in order to developreusable, modi®able,and extendible control software.The proposed methodology is used in a FMS.The OMT is used to®nd the static relationshipamong di erent objects.The PN models are formulated to study the performance of the FMS.In Chapter12,the scheduling methods of FMS using PNs are introduced.Some examples are presented for automated manufacturing system and semiconductor test facility.In the last Chapter,the future research directions of PNs are pointed out.The contents include CASE tool environment,scheduling of large production system,su-pervisory control,multi-lifecycle engineering and benchmark studies.Book review/Mechatronics11(2001)947±950949 mentsAs a monograph in PNs and its applications in FMS,the book is abundant in contents.Besides the rich knowledge of PNs,the book covers almost every aspects regarding FMS design and analysis,such as modeling,simulation,performance evaluation,planning and scheduling,break down handling,real-time control,con-trol software development,etc.So,the reader can obtain much knowledge in PN, FMS,discrete event system control,system simulation,scheduling,as well as in software development.The book is a very good book in the combinations of PNs theory and prac-tical applications.Throughout the book,the integrated style is demonstrated.It is very well suited for the graduate students and beginners who are interested in using PN methods in studying their speci®c problems.The book is especially suited for the researchers working in the areas of FMS,CIMS,advanced man-ufacturing technologies.The feedback messages from our graduate students show that compared with other books about PNs,this book is more interested and easy to learn.It is easy to get a clear picture about what is PNs method and how it can be used in the FMS design and analysis.So,the book is a very good textbook for the graduate students whose majors are manufacturing systems, industrial engineering,factory automation,enterprise management,and computer applications.Both PNs and FMS are complex and research intensive areas.Due to the deep understanding for PNs,FMS,and the writing skills of the authors,the book has good advantages in describing complex problems and theories in a very easy read and understandable fashion.The easy understanding and abundant contents enable the book to be a good reference book both for the students and researchers. Through reading the book,the readers can also learn the new research results in PNs and its applications in FMS that do not contained in other books.Because the most new results given in the book are the study achievements of the authors,the readers can better know not only the results,but also the background,history,and research methodology of the related areas.This would helpthe researchers who are going to do the study to know the state-of-art of relevant areas,thus the researchers can begin the study in less preparing time and to get new results more earlier.As compared to other books,the organization of the book is very application oriented.The aims are to present new research results in FMS applications using PNs method,the organization of the book is cohesive to the topics.A lot of live examples have reinforced the presented methods.These advantages make the book to be a very good practical guide for the students and beginners to start their re-search in the related areas.The history and reference of related research given in this book provides the reader a good way to better know PNs methods and its applications in FMS.It is especially suited for the Ph.D.candidates who are determined to choose PNs as their thesis topics.950Book review/Mechatronics11(2001)947±9503.ConclusionsDue to the signi®cant importance of PNs and its applications,PNs have become a common background and basic method for the students and researchers to do re-search in modeling,planning and scheduling,performance analysis,discrete event system control,and shop-¯oor control software development.The book under re-view provides us a good approach to learn as well as to begin the research in PNs and its application in manufacturing systems.The integrated and application oriented style of book enables the book to be a very good book both for graduate students and researchers.The easy understanding and step-by-step deeper introduction of the contents makes it to be a good textbook for the graduate students.It is suited to the graduated students whose majors are manufacturing system,industrial engineering, enterprise management,computer application,and automation.References[1]Talavage J,Hannam RG.Flexible manufacturing systems in practice:application,design,andsimulation.New York:Marcel Dekker Inc.;1988.[2]Tetzla UAW.Optimal design of¯exible manufacturing systems.New York:Springer;1990.[3]Jha NK,editor.Handbook of¯exible manufacturing systems.San Diego:Academic Press,1991.[4]Carrie C.Simulation of manufacturing.New York:John Wiley&Sons;1988.[5]Gupta YP,Goyal S.Flexibility of manufacturing systems:concepts and measurements.EuropeanJournal of Operational Research1989;43:119±35.[6]Carter MF.Designing¯exibility into automated manufacturing systems.In:Stecke KE,Suri R,editors.Proceedings of the Second ORSA/TIMS Conference on FMS:Operations Research Models and Applications.New York:Elsevier;1986.p.107±18.[7]David R,Alla H.Petri nets and grafcet.New York:Prentice Hall;1992.[8]Zhou MC,DiCesare F.Petri net synthesis for discrete event control of manufacturing systems.Norwell,MA:Kluwer Academic Publishers;1993.[9]Desrochers AA,Al-Jaar RY.Applications of petri nets in manufacturing systems.New York:IEEEPress;1995.[10]Zhou MC,editor.Petri nets in¯exible and agile automation.Boston:Kluwer Academic Publishers,1995.[11]Lin C.Stochastic petri nets and system performance evaluations.Beijing:Tsinghua University Press;1999.[12]Peterson JL.Petri net theory and the modeling of systems.Englewood Cli s,NJ:Prentice-Hall;1981.[13]Resig W.Petri nets.New York:Springer;1985.[14]Jensen K.Coloured Petri Nets.Berlin:Springer;1992.Yushun FanDepartment of Automation,Tsinghua UniversityBeijing100084,People's Republic of ChinaE-mail address:*****************。
仿真算法知识点总结
仿真算法知识点总结一、简介仿真算法是一种通过生成模型和运行模拟来研究系统或过程的方法。
它是一种用计算机模拟真实世界事件的技术，可以用来解决各种问题，包括工程、商业和科学领域的问题。
仿真算法可以帮助研究人员更好地理解系统的行为，并预测系统未来的发展趋势。
本文将对仿真算法的基本原理、常用技术和应用领域进行总结，以期帮助读者更好地了解和应用仿真算法。
二、基本原理1. 离散事件仿真（DES）离散事件仿真是一种基于离散时间系统的仿真技术。
在离散事件仿真中，系统中的事件和状态都是离散的，而时间是连续变化的。
离散事件仿真通常用于建模和分析复杂系统，例如生产线、通信网络和交通系统等。
离散事件仿真模型可以用于分析系统的性能、验证系统的设计和决策支持等方面。
2. 连续仿真（CS）连续仿真是一种基于连续时间系统的仿真技术。
在连续仿真中，系统中的状态和事件都是连续的，而时间也是连续的。
连续仿真通常用于建模和分析动态系统，例如电力系统、控制系统和生态系统等。
连续仿真模型可以用于分析系统的稳定性、动态特性和系统参数的设计等方面。
3. 混合仿真（HS）混合仿真是一种同时兼具离散事件仿真和连续仿真特点的仿真技术。
混合仿真可以用于建模和分析同时包含离散和连续过程的系统，例如混合生产系统、供应链系统和环境系统等。
混合仿真模型可以用于分析系统的整体性能、协调离散和连续过程以及系统的优化设计等方面。
4. 随机仿真随机仿真是一种基于概率分布的仿真技术。
在随机仿真中，系统的状态和事件都是随机的，而时间也是随机的。
随机仿真通常用于建模和分析具有随机性质的系统，例如金融系统、天气系统和生物系统等。
随机仿真模型可以用于分析系统的风险、概率特性和对策选择等方面。
5. Agent-Based ModelingAgent-based modeling (ABM) is a simulation technique that focuses on simulating the actions and interactions of autonomous agents within a system. This approach is often used for modeling complex and decentralized systems, such as social networks, biologicalecosystems, and market economies. In ABM, individual agents are modeled with their own sets of rules, behaviors, and decision-making processes, and their interactions with other agents and the environment are simulated over time. ABM can be used to study the emergent behavior and dynamics of complex systems, and to explore the effects of different agent behaviors and interactions on system-level outcomes.三、常用技术1. Monte Carlo方法蒙特卡洛方法是一种基于随机模拟的数值计算技术。
给水管网可靠性评价研究进展
给水管网可靠性评价研究进展陈盛达;李树平;姜晓东【摘要】给水管网可靠性研究对于保证城市供水安全性及服务水平有积极的意义.本文主要论述了配水管网不同的可靠性评价指标及多种配水系统可靠性分析方法,主要包括解析法、模拟法和代理指标法,并结合不同分析方法的计算复杂程度和适用性,对它们的优缺点做出了评价.【期刊名称】《城镇供水》【年(卷),期】2017(000)005【总页数】5页(P91-95)【关键词】配水管网;可靠性;评价指标【作者】陈盛达;李树平;姜晓东【作者单位】同济大学环境科学与工程学院,上海 200092;同济大学环境科学与工程学院,上海 200092;同济大学环境科学与工程学院,上海 200092【正文语种】中文供水管网是城市重要的基础设施，负责从水源向用户不间断地输送保质保量的水。
输配水管网系统是否完成其预定的设计目标，可以用可靠性作为衡量指标。
配水系统的可靠性通常定义为系统在不同的运行条件下，包括正常和故障状态下向客户供给合理水量的能力[1]。
而配水系统的可靠性通常依赖于系统中的众多参数，使得其定量分析存在较大的限制。
早期对供水管网可靠性的研究主要集中在拓扑结构连通性上[2～3]，随着计算机和水力模拟器的发展和完善，部分学者开始利用模拟的手段描述实际配水管网故障发生的状况，并提出了分析水力可靠性的必要性[4～5]。
然而，实际管网中的运行状态受系统条件和配置等多种因素影响，计算传统的可靠性指标仍有很大的工作量[6]，同时对管网的优化设计带来很大的不便，为了克服这个不足，很多学者开始提出不同形式的代理指标从而避免多次对管网做直接的水力分析[7～9]，同时大大简化了管网多目标优化的流程，然而代理指标是否能完全代替配水管网的可靠性以及不同代理指标之间的性能优劣，至今仍存在较多的争论[10～11]。
本文主要论述了多种配水系统可靠性分析方法，主要包括解析法、模拟法和代理指标法，并结合不同分析方法的计算复杂程度和适用性，对它们的优缺点做出了评价，同时也比较了不同可靠性指标和优化算法模型特点和研究中的应用。
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.
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。
Autodesk CFD、Simulation Mechanical和Moldflow软件分析文档说
SIM20752 -Making Models Ready for Analysis An Introduction to SimStudio ToolsJim SwainApplications ConsultantSynergis Technologies LLCYour Instructor▪Applications Consultant with Synergis Technologies LLC.▪Over 30 years of engineering and CAD experience.▪Last 19 years with Synergis.▪AutoCAD and Inventor Certified Professional.Class summaryIn this class, we will use SimStudio Tools to change models from fully detailed, production-ready components to models that are suitable for analysis in Autodesk CFD software, Simulation Mechanical software, or Moldflow software. Production models are more finely detailed than needed for simulation analysis. This leads to either long analysis run times due to high element counts, large effort put into remodeling designs into simpler forms, or often both. And that is if they run at all!In other words…▪Guided tour of SimStudio Tools▪In context of taking a concept into CFD for early analysisKey learning objectivesBy the end of this class, you learn how to:▪Simplify a model by removing small features.▪Simplify a system by replacing components with simple primitives.▪Adjust a model by direct-editing existing features.▪Repair a model by healing damaged surfaces.What are SimStudio Tools andWhy Use Them?▪Set of tools for:▪Modeling▪Repairing▪Simplifying▪Idealizing▪Assembling▪Inspecting“SimStudio Tools, focuses on CAD model simplification, cleanup, and editing capabilities that help you create higher quality meshes and run through design iterations fasterin Autodesk Simulation Mechanical, CFD, and Moldflow.”Jon den HartogProduct ManagerAutodeskPosting to the SimStudio forum dated 03-20-2015▪Importing and Healing Models ▪Simplifying Models▪Modeling and Direct EditingImporting and Healing ModelsImporting Files▪Many file types supported.▪Files are fixed on import by default.My model…S ome general observations…▪No QAT▪But tweaking regular toolbar just as good.▪Like the default mouse behavior.▪Don’t like the default “up”.▪So….▪Tweak the Preferences.Repairing ModelsRepair Tools▪Repair Browser▪Identify potential issues▪Recommend actions ▪Find and Fix▪Auto Fix▪Adjustable toleranceManual Repair▪Direct Editing Solids▪Press Pull Faces▪Surfaces▪Create New▪Unstitch & Stitch and MergePress Pull▪Select the face▪Enter the offset▪Offset tools includes MeasureManual Repair▪I tried a Patch ▪It didn’t work.▪Gap FillGap Fill▪Part of the Idealize tools.▪Can be used to adjust surfaces toconnect.▪Used here to fix missing faces.Simplifying ModelsTools for Simplifying Models ▪Remove Features▪Remove Faces▪Suppress/Unsuppress▪Replace Bodies▪Remove InterferencesRemove Features▪Checks for features that may be removed.▪Size filter can be adjusted.▪May select multiplecomponents.Remove Faces▪Select faces to be removed.▪Be careful –Select Through maybe turned on!▪Fast method to simplify model.▪Box, Cylinder and sphere primitives are available.▪Primitive body may be resized during creation.▪Extend the primitive into the PCB ▪Replaces all instances ofcomponent in model.▪Original bodies are hidden.a fterwards…▪Use the Interference tool.Interference Inspection Tool ▪Select the components, then Compute.▪Decide whichcomponent loosesvolume.Create Fluid VolumesCreate Fluid Volumes▪Create external or internal volumes.▪CFD already does this, why bother?▪You can’t edit the volume inCFD!▪Internal volumes: Cap any openings.▪Surface patch tool handy here.▪Start the Fluid Volume tool.▪Select everything.▪Choose External or Internal VolumeFinal thoughts…▪Meshed in CFD▪Without using SimStudio Tools:▪6,153,016 Total Elements▪3,853,144 Fluid Elements▪2,299,872 Solid Elements▪Simplified with SimStudio Tools:▪276,849 Total Elements▪221,484 Fluid Elements▪55,365 Solid ElementsQuestions?Resources –SimHub▪Simulation TV –Features and What’s New videos ▪Resources –White papers, validation document Simulation TV Feature demos andWhat’s New videosResources White papers andvalidation documentsDiscussions / Idea Station Ask questions,shareyour knowledge andideasBlog Feature stories, tipsand tricks, latest newsLearning Archive of AU-onlinepresentationsAsk a question of the SimSquadJoin The Discussion!▪Autodesk customers and industry partners ask questions and share information about Autodesk products.▪Regularly monitored by Autodesk employeesAutodesk Nastran Forum Autodesk Nastran Idea StationCan befound via theKnowledgeNetwork or theSimHubResources –Build Your Simulation IQ WebinarsRegister for live webinars, orwatch them on-demand on YouTube.Autodesk is a registered trademark of Autodesk, Inc., and/or its subsidiaries and/or affiliates in the USA and/or other countries. All other brand names, product names, or trademarks belong to their respective holders. Autodesk reserves the right to alter product and services offerings, and。
外文文献文献列表
- disruption ，: Global convergence vs nationalSustainable-，practices and dynamic capabilities in the food industry: A critical analysis of the literature5 Mesoscopic- simulation6 Firm size and sustainable performance in food -s: Insights from Greek SMEs7 An analytical method for cost analysis in multi-stage -s: A stochastic / model approach8 A Roadmap to Green - System through Enterprise Resource Planning (ERP) Implementation9 Unidirectional transshipment policies in a dual-channel -10 Decentralized and centralized model predictive control to reduce the bullwhip effect in -，11 An agent-based distributed computational experiment framework for virtual -/ development12 Biomass-to-bioenergy and biofuel - optimization: Overview, key issues and challenges13 The benefits of - visibility: A value assessment model14 An Institutional Theory perspective on sustainable practices across the dairy -15 Two-stage stochastic programming - model for biodiesel production via wastewater treatment16 Technology scale and -s in a secure, affordable and low carbon energy transition17 Multi-period design and planning of closed-loop -s with uncertain supply and demand18 Quality control in food -，: An analytical model and case study of the adulterated milk incident in China19 - information capabilities and performance outcomes: An empirical study of Korean steel suppliers20 A game-based approach towards facilitating decision making for perishable products: An example of blood -21 - design under quality disruptions and tainted materials delivery22 A two-level replenishment frequency model for TOC - replenishment systems under capacity constraint23 - dynamics and the ―cross-border effect‖: The U.S.–Mexican border’s case24 Designing a new - for competition against an existing -25 Universal supplier selection via multi-dimensional auction mechanisms for two-way competition in oligopoly market of -26 Using TODIM to evaluate green - practices under uncertainty27 - downsizing under bankruptcy: A robust optimization approach28 Coordination mechanism for a deteriorating item in a two-level - system29 An accelerated Benders decomposition algorithm for sustainable -/ design under uncertainty: A case study of medical needle and syringe -30 Bullwhip Effect Study in a Constrained -31 Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable -/ of perishable food32 Research on pricing and coordination strategy of green - under hybrid production mode33 Agent-system co-development in - research: Propositions and demonstrative findings34 Tactical ，for coordinated -s35 Photovoltaic - coordination with strategic consumers in China36 Coordinating supplier׳s reorder point: A coordination mechanism for -s with long supplier lead time37 Assessment and optimization of forest biomass -s from economic, social and environmental perspectives – A review of literature38 The effects of a trust mechanism on a dynamic -/39 Economic and environmental assessment of reusable plastic containers: A food catering - case study40 Competitive pricing and ordering decisions in a multiple-channel -41 Pricing in a - for auction bidding under information asymmetry42 Dynamic analysis of feasibility in ethanol - for biofuel production in Mexico43 The impact of partial information sharing in a two-echelon -44 Choice of - governance: Self-managing or outsourcing?45 Joint production and delivery lot sizing for a make-to-order producer–buyer - with transportation cost46 Hybrid algorithm for a vendor managed inventory system in a two-echelon -47 Traceability in a food -: Safety and quality perspectives48 Transferring and sharing exchange-rate risk in a risk-averse - of a multinational firm49 Analyzing the impacts of carbon regulatory mechanisms on supplier and mode selection decisions: An application to a biofuel -50 Product quality and return policy in a - under risk aversion of a supplier51 Mining logistics data to assure the quality in a sustainable food -: A case in the red wine industry52 Biomass - optimisation for Organosolv-based biorefineries53 Exact solutions to the - equations for arbitrary, time-dependent demands54 Designing a sustainable closed-loop -/ based on triple bottom line approach: A comparison of metaheuristics hybridization techniques55 A study of the LCA based biofuel - multi-objective optimization model with multi-conversion paths in China56 A hybrid two-stock inventory control model for a reverse -57 Dynamics of judicial service -s58 Optimizing an integrated vendor-managed inventory system for a single-vendor two-buyer - with determining weighting factor for vendor׳s ordering59 Measuring - Resilience Using a Deterministic Modeling Approach60 A LCA Based Biofuel - Analysis Framework61 A neo-institutional perspective of -s and energy security: Bioenergy in the UK62 Modified penalty function method for optimal social welfare of electric power - with transmission constraints63 Optimization of blood - with shortened shelf lives and ABO compatibility64 Diversified firms on dynamical - cope with financial crisis better65 Securitization of energy -s in China66 Optimal design of the auto parts - for JIT operations: Sequential bifurcation factor screening and multi-response surface methodology67 Achieving sustainable -s through energy justice68 - agility: Securing performance for Chinese manufacturers69 Energy price risk and the sustainability of demand side -s70 Strategic and tactical mathematical programming models within the crude oil - context - A review71 An analysis of the structural complexity of -/s72 Business process re-design methodology to support - integration73 Could - technology improve food operators’ innovativeness? A developing country’s perspective74 RFID-enabled process reengineering of closed-loop -s in the healthcare industry of Singapore75 Order-Up-To policies in Information Exchange -s76 Robust design and operations of hydrocarbon biofuel - integrating with existing petroleum refineries considering unit cost objective77 Trade-offs in - transparency: the case of Nudie Jeans78 Healthcare - operations: Why are doctors reluctant to consolidate?79 Impact on the optimal design of bioethanol -s by a new European Commission proposal80 Managerial research on the pharmaceutical - – A critical review and some insights for future directions81 - performance evaluation with data envelopment analysis and balanced scorecard approach82 Integrated - design for commodity chemicals production via woody biomass fast pyrolysis and upgrading83 Governance of sustainable -s in the fast fashion industry84 Temperature ，for the quality assurance of a perishable food -85 Modeling of biomass-to-energy - operations: Applications, challenges and research directions86 Assessing Risk Factors in Collaborative - with the Analytic Hierarchy Process (AHP)87 Random / models and sensitivity algorithms for the analysis of ordering time and inventory state in multi-stage -s88 Information sharing and collaborative behaviors in enabling - performance: A social exchange perspective89 The coordinating contracts for a fuzzy - with effort and price dependent demand90 Criticality analysis and the -: Leveraging representational assurance91 Economic model predictive control for inventory ，in -s92 -，ontology from an ontology engineering perspective93 Surplus division and investment incentives in -s: A biform-game analysis94 Biofuels for road transport: Analysing evolving -s in Sweden from an energy security perspective95 -，executives in corporate upper echelons Original Research Article96 Sustainable -，in the fast fashion industry: An analysis of corporate reports97 An improved method for managing catastrophic - disruptions98 The equilibrium of closed-loop - super/ with time-dependent parameters99 A bi-objective stochastic programming model for a centralized green - with deteriorating products100 Simultaneous control of vehicle routing and inventory for dynamic inbound -101 Environmental impacts of roundwood - options in Michigan: life-cycle assessment of harvest and transport stages102 A recovery mechanism for a two echelon - system under supply disruption103 Challenges and Competitiveness Indicators for the Sustainable Development of the - in Food Industry104 Is doing more doing better? The relationship between responsible -，and corporate reputation105 Connecting product design, process and - decisions to strengthen global - capabilities106 A computational study for common / design in multi-commodity -s107 Optimal production and procurement decisions in a - with an option contract and partial backordering under uncertainties108 Methods to optimise the design and ，of biomass-for-bioenergy -s: A review109 Reverse - coordination by revenue sharing contract: A case for the personal computers industry110 SCOlog: A logic-based approach to analysing - operation dynamics111 Removing the blinders: A literature review on the potential of nanoscale technologies for the ，of -s112 Transition inertia due to competition in -s with remanufacturing and recycling: A systems dynamics mode113 Optimal design of advanced drop-in hydrocarbon biofuel - integrating with existing petroleum refineries under uncertainty114 Revenue-sharing contracts across an extended -115 An integrated revenue sharing and quantity discounts contract for coordinating a - dealing with short life-cycle products116 Total JIT (T-JIT) and its impact on - competency and organizational performance117 Logistical - design for bioeconomy applications118 A note on ―Quality investment and inspection policy in a supplier-manufacturer -‖119 Developing a Resilient -120 Cyber - risk ，: Revolutionizing the strategic control of critical IT systems121 Defining value chain architectures: Linking strategic value creation to operational - design122 Aligning the sustainable - to green marketing needs: A case study123 Decision support and intelligent systems in the textile and apparel -: An academic review of research articles124 -，capability of small and medium sized family businesses in India: A multiple case study approach125 - collaboration: Impact of success in long-term partnerships126 Collaboration capacity for sustainable -，: small and medium-sized enterprises in Mexico127 Advanced traceability system in aquaculture -128 - information systems strategy: Impacts on - performance and firm performance129 Performance of - collaboration – A simulation study130 Coordinating a three-level - with delay in payments and a discounted interest rate131 An integrated framework for agent basedinventory–production–transportation modeling and distributed simulation of -s132 Optimal - design and ，over a multi-period horizon under demand uncertainty. Part I: MINLP and MILP models133 The impact of knowledge transfer and complexity on - flexibility: A knowledge-based view134 An innovative - performance measurement system incorporating Research and Development (R&D) and marketing policy135 Robust decision making for hybrid process - systems via model predictive control136 Combined pricing and - operations under price-dependent stochastic demand137 Balancing - competitiveness and robustness through ―virtual dual sourcing‖: Lessons from the Great East Japan Earthquake138 Solving a tri-objective - problem with modified NSGA-II algorithm 139 Sustaining long-term - partnerships using price-only contracts 140 On the impact of advertising initiatives in -s141 A typology of the situations of cooperation in -s142 A structured analysis of operations and -，research in healthcare (1982–2011143 - practice and information quality: A - strategy study144 Manufacturer's pricing strategy in a two-level - with competing retailers and advertising cost dependent demand145 Closed-loop -/ design under a fuzzy environment146 Timing and eco(nomic) efficiency of climate-friendly investments in -s147 Post-seismic - risk ，: A system dynamics disruption analysis approach for inventory and logistics planning148 The relationship between legitimacy, reputation, sustainability and branding for companies and their -s149 Linking - configuration to - perfrmance: A discrete event simulation model150 An integrated multi-objective model for allocating the limited sources in a multiple multi-stage lean -151 Price and leadtime competition, and coordination for make-to-order -s152 A model of resilient -/ design: A two-stage programming with fuzzy shortest path153 Lead time variation control using reliable shipment equipment: An incentive scheme for - coordination154 Interpreting - dynamics: A quasi-chaos perspective155 A production-inventory model for a two-echelon - when demand is dependent on sales teams׳ initiatives156 Coordinating a dual-channel - with risk-averse under a two-way revenue sharing contract157 Energy supply planning and - optimization under uncertainty158 A hierarchical model of the impact of RFID practices on retail - performance159 An optimal solution to a three echelon -/ with multi-product and multi-period160 A multi-echelon - model for municipal solid waste ，system 161 A multi-objective approach to - visibility and risk162 An integrated - model with errors in quality inspection and learning in production163 A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge ，adoption in - to overcome its barriers164 A relational study of - agility, competitiveness and business performance in the oil and gas industry165 Cyber - security practices DNA – Filling in the puzzle using a diverse set of disciplines166 A three layer - model with multiple suppliers, manufacturers and retailers for multiple items167 Innovations in low input and organic dairy -s—What is acceptable in Europe168 Risk Variables in Wind Power -169 An analysis of - strategies in the regenerative medicine industry—Implications for future development170 A note on - coordination for joint determination of order quantity and reorder point using a credit option171 Implementation of a responsive - strategy in global complexity: The case of manufacturing firms172 - scheduling at the manufacturer to minimize inventory holding and delivery costs173 GBOM-oriented ，of production disruption risk and optimization of - construction175 Alliance or no alliance—Bargaining power in competing reverse -s174 Climate change risks and adaptation options across Australian seafood -s – A preliminary assessment176 Designing contracts for a closed-loop - under information asymmetry 177 Chemical - modeling for analysis of homeland security178 Chain liability in multitier -s? Responsibility attributions for unsustainable supplier behavior179 Quantifying the efficiency of price-only contracts in push -s over demand distributions of known supports180 Closed-loop -/ design: A financial approach181 An integrated -/ design problem for bidirectional flows182 Integrating multimodal transport into cellulosic biofuel- design under feedstock seasonality with a case study based on California183 - dynamic configuration as a result of new product development184 A genetic algorithm for optimizing defective goods - costs using JIT logistics and each-cycle lengths185 A -/ design model for biomass co-firing in coal-fired power plants 186 Finance sourcing in a -187 Data quality for data science, predictive analytics, and big data in -，: An introduction to the problem and suggestions for research and applications188 Consumer returns in a decentralized -189 Cost-based pricing model with value-added tax and corporate income tax for a -/190 A hard nut to crack! Implementing - sustainability in an emerging economy191 Optimal location of spelling yards for the northern Australian beef -192 Coordination of a socially responsible - using revenue sharing contract193 Multi-criteria decision making based on trust and reputation in -194 Hydrogen - architecture for bottom-up energy systems models. Part 1: Developing pathways195 Financialization across the Pacific: Manufacturing cost ratios, -s and power196 Integrating deterioration and lifetime constraints in production and - planning: A survey197 Joint economic lot sizing problem for a three—Layer - with stochastic demand198 Mean-risk analysis of radio frequency identification technology in - with inventory misplacement: Risk-sharing and coordination199 Dynamic impact on global -s performance of disruptions propagation produced by terrorist acts。
人体下肢运动力学分析与建模
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杭州电子科技大学硕士学位论文
第1章
1.1 课题背景及研究意义
绪论
从古至今，因为战争、工伤、疾病、交通事故和意外伤害等原因而产生的下 肢截肢者随着工业、交通的迅速发展而迅速增加。一项调查显示仅美国每年就大 约有 11 万人失去下肢，而我国目前下肢残疾者更是高达 600 万人，其中下肢截肢 者约 137 万多人[1]。这些下肢截肢者由于失去了人类最基本的功能之一——行走， 生活难以自理，被安置在一些脱离社会的特定角落，致使他们在身体心理都充满 着常人无法体会的痛苦。但是现在医疗水平还不能使肢体再生，为这些截肢者安 装人工假肢就成了恢复其一些日常活动的唯一手段。 随着科学的进步，人们生活水平的提高，不仅要求假肢要具有很好的装饰性， 而且对其运动性能的要求也越来越高。智能下肢假肢通过检测穿戴者的运动状态， 来控制假肢运动，从而提高步态的灵活性、协调性和安全性。现代运动生物力学 对人体腿部运动信息的采集与分析在机器人和假肢研究方面有着重大的作用。 二十世纪中期以来，把生物力学同体育科学理论研究相结合条件日趋成熟， 运动生物力学逐渐形成为了一门独立的学科。运动生物力学研究的内容是人体运 动中的机械运动规律，以生物学和力学的理论、方法研究人体从事各种活动、运 动和劳动的动作技术，使复杂的人体动作技术奠基于最基本的生物学和力学规律 之上，并以数学、力学、生物学以及动作技术原理的形式加以定量描述。随着计 算机、传感器、测速器、高速摄影、测力台和电子解析系统技术的应用，使准确 地测量与分析人体运动的参数成为现实，科学技术的发展为运动生物力学的研究 奠定了坚实的物质基础，而生物学、力学理论的发展与完善则为它建立了坚实的 理论基础。人体运动、活动和劳动中的各种动作技术，可以通过生物力学方法进 行测试研究，提高动作技术效率，提高运动技术水平[2]。 本论文依据获取的下肢的运动力学信息，通过建立人体下肢动力学模型和进 行运动力学分析，研究表面肌电信号与运动参数，关节力矩相互之间的关系，从 而对下肢运动进行建模。为深入地研究假肢的设计和控制建立基础，使假肢的运 动自然协调，快速灵活。为国内下肢假肢事业的发展打下基础，缩小和国外智能 假肢发展水平的差距，改善残疾人的生活质量，提高他们的社会活动参与能力， 促进社会的和谐发展，对康复医学也具有重要的意义。
VERIFICATION, VALIDATION, AND ACCREDITATION IN THE LIFE CYCLE OF MODELS AND SIMULATIONS
Proceedings of the 2000 Winter Simulation ConferenceJ. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick, eds.VERIFICATION, VALIDATION, AND ACCREDITATION IN THELIFE CYCLE OF MODELS AND SIMULATIONSJennifer ChewHQ, U.S. Army Developmental Test CommandATTN: CSTE-DTC-TT-M Aberdeen Proving Ground, MD 21005-5055, U.S.A.Cindy SullivanU.S. Army Yuma Proving Ground ATTN: CSTE-DTC-YP-CD, Building 2105 Yuma, AZ 85365, U.S.A.ABSTRACTVerification, validation, and accreditation (VV&A) activities should be an on-going process throughout the life cycle of models and simulations (M&S). It is important to note that there is no single set of VV&A tasks, events, or methods that would apply every time to every situation. The VV&A emphasis and methods used vary depending on the particular life cycle phase it is in, previous VV&A and use, the risks and uncertainty, its size and complexity, and of course, the resources available. For simplification, this paper discusses the activities and tasks during the early stages of model development and addresses each of the VV&A efforts separately, along with its associated activities. It outlines the specific VV&A activities and products that are appropriate to each phase of model development.1INTRODUCTIONIn recent years, the Department of Defense (DoD) has aggressively applied M&S in wargaming, analysis, design, testing, etc., to support acquisition decisions. One caveat is that if the model is intended to be used by DoD, then the model must be verified and validated to ensure that the simulation outputs are sufficiently credible for its intended use(s). While the DoD is responsible for its own M&S, M&S that are developed and/or used by industry and academia in support of DoD acquisition activities must also comply with the DoD VV&A policy. The information presented herein has been compiled from a wide variety of sources, including DoD directives and instructions related to M&S management and VV&A, software industry standards and practices, and academic text and professional literature.The VV&A activities contained herein are broadly applicable to all stand-alone models and federates which are used for supporting DoD acquisition decisions. Federates are individual M&S products that are capable of joining High Level Architecture—based federations. This paper does not cover the VV&A on a federation of models. VV&A of a federation must be completed after doing VV&A on each of its federates. The activities described in this paper are intended to be used for planning, producing, and documenting proper evidence to support the VV&A of M&S. This paper is also intended to help the reader to plan for and develop structured and organized VV&A activities; provide a systematic approach for preparing VV&A documentation; and give a better understanding of how VV&A can be an integral part of the M&S life cycle. It emphasizes activities that are crucial during each phase of M&S development and use.Too often Verification and Validation (V&V) are considered separately from development and documen-tation. The V&V plans and process should begin on the first day of development and continue in such a manner that the same documentation used for requirements, design, development, and configuration control also serves to support V&V activities. Finding and resolving problems early via application of V&V can significantly reduce the subsequent cost of M&S design, development, and testing. There are many V&V tasks that the M&S developer should be doing before and during model development. As a matter of fact, VV&A activities should begin as soon as there is a decision to apply M&S to a problem. The planning effort for VV&A is as important as implementing it. The earlier we start the V&V planning, the easier it is to implement. It is always good practice to ensure that all pertinent information is documented along the way.It is important to note that all the VV&A activities are tailorable to the specific requirements. Unless there is high impact given a failure (e.g., cost or safety) or it is a very large and/or complex developmental effort, we probably do not need to accomplish every task or method mentioned in this paper. There is no single set of VV&A tasks, events, or methods that applies exclusively every time to every situation. VV&A emphasis and methods used vary depending on the particular life cycle phase it is in, previous VV&A and use, the risks and uncertainty, its size and complexity, and resources available. The depth of analysis involved with the V&V of an established legacy model would be different from the development of a new M&S. Likewise, the available information for the accreditation of legacy model might be based more onhistorical performance than results from the detailed tasks outlined in this paper for a new M&S.There are many ways and techniques to accomplish VV&A. Although there is an abundance of literature on VV&A advocating diverse methods, this paper compresses the information to provide a simplified process that focuses on the activities and tasks during each phase of the development. For simplification, this paper addresses the VV&A activities and products that apply to each M&S development phase.2VV&A IN THE LIFE CYCLE OF M&SFigure 1 shows a typical life cycle of an M&S and its associated VV&A activities. These activities or tasks may be tailored and applied differently based on the depth of analysis, as required by the user or established acceptability criteria. The authoritative data source (ADS) library, as shown in Figure 1, contains DoD data sources used for supporting M&S which are cataloged through the available through the Defense Modeling and Simulation Office website at <www.dmso.mi l>.The remainder of this paper examines each of the VV&A phase and discusses the activities associated with them.2.1Requirements Verification and ValidationThe M&S development should begin with a clear and unambiguous statement of the problem that the M&S are intended to address. A good definition of the problem makes it easier to define M&S requirements such as simulation outputs, functions, and interactions. It is also important to specify, at least in general terms, how much like the real world the user needs these outputs, functions, and interactions to be. We believe that the most critical piece of the M&S development and V&V activities falls in the very beginning of the life cycle. If the requirements do not make sense or not well understood, then the M&S will not do what was originally intended.Basically, this phase of the process is primarily involved in reviewing the requirement documentation and in documenting all findings. The review focuses on the intended use, acceptability criteria for model fidelity, traceability, quality, configuration management, and fidelity of the M&S to be developed. This is done to ensure that all the requirements are clearly defined, consistent, testable, and complete.The first step is to gather information. Any informa-tion related to the M&S and its requirements increases understandability of the requirements and making the right decisions. It may not be obvious that one of the most criti-cal V&V effort is to review all the information gathered and document all the findings. This could include:•Requirements•Interface requirements•Developmental plans•Previous V&V plans and results•Configuration Management Plan•Quality Assurance Plans•Studies and AnalysesDocumenting all the findings, assumptions, limitations, etc., from reviewing every piece of related information about the M&S, is extremely important. We review the requirement documentation, determine the risk areas, and assess the criticality of specific factors that need the most attention. Again, we document the assessment and highlight the areas that may need further analysis. We report all the findings to the sponsor/user and have all the discrepancies resolved before continuing with any further major efforts.The following should be considered when tailoring. If the intended use is not adequately documented, the V&V team may need to talk to the users and document the intended use themselves. If the model has interfaces, these need to be verified to determine if the interface structure is adequate. User interfaces need to be analyzed to determine how accurately the interface is integrated into the overall M&S and for human factors engineering, for example, requirements to accommodate the number, skill levels, duty cycles, training needs, or other information about the personnel who will use or support the model. If this is a developmental effort or the developers are available, the V&V team may be able to participate in requirements review and ask the developers questions face-to-face. The following system engineering factors may be important to assess for adequacy:•adaptation of installation independent data•safety (prevent/minimize hazards to personnel, property, and physical environment)•security and privacy•for software, the computer hardware and oper-ating system•for hardware, the environment during transpor-tation, storage, and operation, e.g., wind, rain,temperature, geographical location, motion,shock, noise, and electromagnetic radiation •computer resources used by the software or incorporated into the hardware•design and construction constraints•logistics•packagingThe requirements V&V phase culminates with the documentation of the intended use, requirements traceability matrix, unsupported requirements, acceptability criteria for model fidelity, risk assessment, and model fidelity.2.2Conceptual Model Verification & ValidationA conceptual model is a preliminary or proposed design framework that is based on the outputs, functions, and interactions defined during the requirements V&V described in Section 2.1. A conceptual model typically consists of a description of how the M&S requirements are broken down into component pieces, how those pieces fit together and interact, and how they work together to meet the requirements specified. It should also include a description of the equations and algorithms that are used to meet the requirements, as well as an explicit description of any assumptions or limitations made or associated with the theories, concepts, fidelity, derivatives, logic, interfaces, or solution approaches. The process of determining the adequacy of the conceptual model and ensuring that it meets the specified requirements and intended use(s) is called conceptual model V&V.One of the initial tasks for conceptual model V&V is to come to finalize and agree with the acceptability criteria for model fidelity and to define the criticality of data inputs and outputs. The importance of data is discussed in Section 2.6. Acceptability criteria and data requirements are used to ensure that each step of the conceptual model framework is traceable to the requirements, and ultimately to these criteria. These criteria are established by the accreditation approval authority defining the terms and conditions of the M&S that will be considered acceptable for the application. Therefore, a set of test cases must be defined to ensure that all the simulation scenarios and trials will adequately address the requirements and satisfy the acceptability criteria. It is crucial that we verify and validate the conceptual model adequately from which the code is generated and/or hardware is built.The products of conceptual model V&V are model characteristics, input/output data items, interface issues, measure of model fidelity, potential weaknesses and limitations, perceived strengths, and traceability between conceptual model and requirements.2.3Design VerificationAfter the conceptual model is verified and validated, the developer produces a detailed design that describes exactly how the conceptual model will be coded or fabricated. It defines the components, elements, functions, and specifications that will be used to produce the simulation based on the conceptual model. Before a single line of software code is written or hardware is fabricated, we should review the detailed design to ensure it conforms to the conceptual model. This step is called Design Verification. It involves a mapping of the proposed design elements back to the conceptual model and requirements to ensure that there is traceability between those requirements and the proposed design. We should also develop test cases that can be traced back to the design and requirements.Although traceability is the main focus during the design verification, other activities such as participating in design reviews, audits, walkthroughs, and inspections are important. For software, it is also important to verify input data; determine computer-aided software engineering tools and design methodology; conduct internal software testing; and perform software metrics analysis. For hardware, it is important for subject matter experts to review the adequacy of drawings (e.g., schematic drawings), interface control drawings, and, as appropriate, the adequacy of the electrical design, mechanical design, power generation and grounding, electrical and mechanical interface compatibility, and mass properties.This phase culminates with the traceability matrix (detailed design to requirements, to conceptual model, and to test cases), design and requirement cross reference matrix, design walkthrough or inspection report, input data verifica-tion, software metric and test reports, and CASE tools.2.4Code Verification and Hardware CheckoutAfter the design is verified, the conceptual model and its associated design are converted into code or hardware by the developer. Code verification and hardware checkout ensure that the detailed design is being implemented correctly in the code or hardware respectively.Code verification normally entails detailed desk checking and software testing of the code, comparing it to the detailed design, documenting any discrepancies and fixing any problems discovered. Other important activities include participating in code testing, audits, walkthroughs, and inspections; validating input data; preparing complexity report; conducting code analysis; and verifying code structure.Hardware checkout entails reviews, audits and inspec-tions, comparing the hardware to its design, documenting any discrepancies and fixing any problems.This phase culminates with the design functionality, code walkthrough or inspection report, complexity metric report, input data validation, coding/interface/logic errors, and syntax and semantics.2.5Code and/or Hardware TestingAfter the design and the initial implementation are com-pleted, the developer integrates the code and/or hardware together and tests it. These tests are intended to verify and validate the M&S. Verification tests the correctness of the M&S to ensure that it accurately represents the developer’s requirements, conceptual description, and design. Validation tests the extent to which an M&S accurately represents the real world from the perspective of the intended use of the M&S.Verification tests that the M&S requirement, concep-tual model and design are implemented as documented in the previous phases. Acceptance testing determines whether all requirements are satisfied. Compliance testing determines if the simulation meets required security and performance standards. Test cases should be traceable to the documented requirements and design to ensure that all were met. Metrics that may be used, if this is a large software development, include breadth and depth of testing, fault profiles, and reliability metrics. The breadth of testing metric (% requirements tested and % requirements addressed) address the degree to which required functionality has been successfully demonstrated as well as the amount of testing that has been performed. The depth of testing metric (% tested and passed testing) measures the amount of testing achieved on the software architecture, that is, the extent and success of testing the possible control and data paths and conditions within the software. Automated tools may be used to compute this measure. Fault profiles (open versus closed anomalies) provides insight into the number and type of deficiencies in the current baseline, as well, as the developer’s ability to fix known faults. The reliability metric (mean time between failures) expresses the contribution to reliability.The two issues that must be addressed during valida-tion testing are to identify the real world being modeled and to identify the key structural characteristics and output parameters that are to be used for comparisons. In other words, validation has to do with the fidelity of the M&S. Fidelity is normally defined by the sponsor/user and is judged by several factors, one of which is its ability to predict the known behavior, or best estimate, of the real system when subjected to the same stimuli. The fidelity level is actually defined when the sponsor/user establishes the acceptability criteria for model fidelity. If the M&S is designed with these criteria in mind, then very likely the M&S will fall within the defined fidelity boundary and be acceptable by the sponsor/user. Otherwise, there is achance of going back to the drawing board. Defining the acceptability criteria up-front is crucially important.In those cases where there is no user or the user simply cannot come up with a set of criteria, we should make sure that all pertinent information about the M&S and the assumptions are documented every step of the way. As a user, validation, by far, is the most important phase of the M&S life cycle. Validation gives solid evidence to help analyze the extent to which the M&S are representing the real world. It is also critical that we assess the degree of detail that must be represented in the simulation to provide acceptable results and the degree of correspondence with real world phenomena that will be sufficient for use with high confidence. If the significant parameters of a real system have been properly incorporated into a model, a simulation experiment should reflect the behavior of a real system down to some level of detail commensurate with that description.Many validation techniques such as using subject matter experts, comparison techniques, and face validation to just name a few. Validation based upon direct com-parison of model results to the real world provides more credibility than other validation methods. Selection of techniques is based on the user’s needs, M&S types, intended uses, and other factors.Despite of the techniques used, the following products should be generated as part of the testing: model fidelity assessment; traceability between requirements, design, and test cases; subject matter expert opinions; M&S and real world comparison; model limitation and impact statement; sensitivity analysis report; test results; and metric report. 2.6AccreditationAccreditation is the official determination by the user that the capabilities of the M&S fit the intended use and that the limitations of the M&S will not interfere in drawing the correct conclusions. Accreditation planning should not wait until after the development is completed. It should begin when the requirements were being verified and validated because the first task, when preparing the accreditation plan, is to develop the acceptability criteria. Acceptability criteria established in the accreditation plan are what the user has identified as key characteristics for use in deciding whether or not to grant an accreditation for the particular M&S. Accreditation occurs at two levels: Class of Applications and Application-specific.Accreditation at the Class of Applications level ac-credits an M&S for a generic set of purposes or applications and includes reviewing a complete audit trail of the development and use of the M&S. The audit trail includes reviews of M&S documentation, V&V documentation, configuration control, M&S assumptions, previous successful uses, and recognition of users’ acceptances.Accreditation of Application-specific level M&S in-cludes data certification, scenarios, and the qualification/ training of the operator-analysts who will use the M&S.All M&S are driven by data, either as direct inputs or as embedded values that drive simulation characteristics. As perfect as the equations, algorithms, and software design of an M&S may be after conceptual model validation and design verification, it will probably fail results validation if the data that drive the simulation are inaccurate or inappropriate for the task at hand. A relationship clearly exists between producer data V&V activities and user data V&V requirements throughout the M&S life cycle. However, there is a distinction between data V&V activities performed by the producer and by the user. Producer data V&V determine data quality in terms of correctness, timeliness, accuracy, completeness, relevance, and accessibility that make data appropriate for the purpose intended and values are within the stated criteria and assumptions. User data V&V ensure that the data are transformed and formatted correctly and that the data meet user specified constraints. Data accreditation is an integral part of the M&S accreditation procedures to ensure that M&S data are verified as correct, and validated as appropriate and reasonable for the intended application.3CONCLUSIONSVV&A may sound challenging or even impossible. This should not be the case if proper VV&A activities are conducted throughout the M&S life cycle, especially during the early stages. Early VV&A planning can reduce or even eliminate many concerns that may arise at later stages. In fact, early planning can also allow you more flexibility in selecting the right V&V techniques and activities to fit the specific needs. However, many situations exist during the M&S planning stage. For example,•Model acceptability criteria and V&V requirements/planning must be established andagreed upon by all parties concerned before anyactivities are defined.•V&V activities can be very labor-intensive and must be focused and carefully scoped according tospecific accreditation requirements.•V&V plan changes as the M&S project matures.V&V planning should not be considered finaluntil after V&V has actually been accomplished.•Validation depends on the intended use and fidelity of the M&S, and it will likely change asnew users are identified.•V&V should begin on day one of the M&S development, should be an integral part of theM&S development, and should be a continuousprocess.•When planning for V&V activities, alternate methods should be included to facilitate scheduledriven events and to adjust as new techniques aredeveloped.•V&V efforts require an experienced and well-trained team.ACKNOWLEDGMENTSThe authors would like to recognize Mr. Bob Lewis, Tecmaster, Inc., for his support to the development of the VV&A activities in the Life Cycle of M&S. His significant contributions have made this paper possible.REFERENCESKnepell, P.L. 1999. VV&A of Models and Simulations (A Five-Day Workshop) Participant Guide. Peak Quality Services, Colorado Springs, CO.Department of Defense. 1996. Department of Defense Verification, Validation and Accreditation (VV&A) Recommended Practices Guide. Defense Modeling and Simulation Office, Alexandria, VA. (Co-authored by: O. Balci, P.A. Glasow, P. Muessig, E. H. Page, J.Sikora, S. Solick, and S. Youngblood).Department of the Army. Army Regulation 5-11. 1997.Management of Army Models and Simulations, Washington, DC.U.S. Army Developmental Test Command (DTC). 1998.Developmental Test Command Verification, Validation, and Accreditation (VV&A) Methodology.DTC Pamphlet 73-4, Aberdeen Proving Ground, MD. Department of the Army. 1999. Verification, Validation, and Accreditation of Army Models and Simulations.Pamphlet 5-11, Army Modeling and Simulation Office, Cystal City, VA.AUTHOR BIOGRAPHIESJENNIFER CHEW is an Electronics Engineer in the Technology Management Division, HQ U.S. Army Developmental Test Command (DTC), Aberdeen Proving Ground, Maryland. She supports the development of the DTC Virtual Proving Ground program and has the lead in developing the DTC VV&A process and methodology. She received her B.S. in Chemical Engineering from University of Maryland and M.S. in Electrical Engineering Science from Loyola College. She is a graduate of the Army Management Staff College and Quality and Reliability Engineering program. Her email address is <chewj@>.CINDY L. SULLIVAN is an Operations Research Analyst and manages the Yuma Proving Ground Virtual Proving Ground Program. She received her B.S. in Computer Science from Freed-Hardeman College and M.S. in Industrial Engineering from the University of Missouri–Columbia. She has 14 years of experience working with Army M&S and earned two Army Achievement Medals. She was the primary author of DTC Pam 73-4 M&S VV&A Methodology. Her email address is <Cindy.Sullivan@>.。
【Robot】Simulation and Analyses模拟与分析
Simulation and Analyses模拟与分析Wind load simulation and automatic generation of wind loads风荷载模拟与风荷载自动生成You can either run the simulation directly in Robot, or you can export your structure to Autodesk Simulation CFD if the program is installed on your machine.可以直接在Robot中运行仿真，也可以将结构导出到Autodesk simulation CFD （如果计算机上安装了程序）。
The Wind loads simulation feature allows you to simulate a wind flow around your structure, and to generate wind loads automatically.“风荷载模拟”功能允许您模拟结构周围的风流，并自动生成风荷载。
This feature is especially useful with structures that have a complicated geometry, and for which it is usually difficult to define the right wind loads. The wind simulation acts as a wind tunnel, and displays colored pressure maps on the model in order to visualize and understand the effects of the wind.对于几何结构复杂且通常难以定义正确风荷载的结构，此功能尤其有用。
Theory of modeling and simulation
THEORY OF MODELING AND SIMULATIONby Bernard P. Zeigler, Herbert Praehofer, Tag Gon Kim2nd Edition, Academic Press, 2000, ISBN: 0127784551Given the many advances in modeling and simulation in the last decades, the need for a widely accepted framework and theoretical foundation is becoming increasingly necessary. Methods of modeling and simulation are fragmented across disciplines making it difficult to re-use ideas from other disciplines and work collaboratively in multidisciplinary teams. Model building and simulation is becoming easier and faster through implementation of advances in software and hardware. However, difficult and fundamental issues such as model credibility and interoperation have received less attention. These issues are now addressed under the impetus of the High Level Architecture (HLA) standard mandated by the U.S. DoD for all contractors and agencies.This book concentrates on integrating the continuous and discrete paradigms for modeling and simulation. A second major theme is that of distributed simulation and its potential to support the co-existence of multiple formalisms in multiple model components. Prominent throughout are the fundamental concepts of modular and hierarchical model composition. These key ideas underlie a sound methodology for construction of complex system models.The book presents a rigorous mathematical foundation for modeling and simulation. It provides a comprehensive framework for integrating various simulation approaches employed in practice, including such popular modeling methods as cellular automata, chaotic systems, hierarchical block diagrams, and Petri Nets. A unifying concept, called the DEVS Bus, enables models to be transparently mapped into the Discrete Event System Specification (DEVS). The book shows how to construct computationally efficient, object-oriented simulations of DEVS models on parallel and distributed environments. In designing integrative simulations, whether or not they are HLA compliant, this book provides the foundation to understand, simplify and successfully accomplish the task.MODELING HUMAN AND ORGANIZATIONAL BEHAVIOR: APPLICATION TO MILITARY SIMULATIONSEditors: Anne S. Mavor, Richard W. PewNational Academy Press, 1999, ISBN: 0309060966. Hardcover - 432 pages.This book presents a comprehensive treatment of the role of the human and the organization in military simulations. The issue of representing human behavior is treated from the perspective of the psychological and organizational sciences. After a thorough examination of the current military models, simulations and requirements, the book focuses on integrative architectures for modeling theindividual combatant, followed by separate chapters on attention and multitasking, memory and learning, human decision making in the framework of utility theory, models of situation awareness and enabling technologies for their implementation, the role of planning in tactical decision making, and the issue of modeling internal and external moderators of human behavior.The focus of the tenth chapter is on modeling of behavior at the unit level, examining prior work, organizational unit-level modeling, languages and frameworks. It is followed by a chapter on information warfare, discussing models of information diffusion, models of belief formation and the role of communications technology. The final chapters consider the need for situation-specific modeling, prescribe a methodology and a framework for developing human behavior representations, and provide recommendations for infrastructure and information exchange.The book is a valuable reference for simulation designers and system engineers.HANDBOOK OF SIMULATOR-BASED TRAININGby Eric Farmer (Ed.), Johan Reimersma, Jan Moraal, Peter JornaAshgate Publishing Company, 1999, ISBN: 0754611876.The rapidly expanding area of military modeling and simulation supports decision making and planning, design of systems, weapons and infrastructure. This particular book treats the third most important area of modeling and simulation – training. It starts with thorough analysis of training needs, covering mission analysis, task analysis, trainee and training analysis. The second section of the book treats the issue of training program design, examining current practices, principles of training and instruction, sequencing of training objectives, specification of training activities and scenarios, methodology of design and optimization of training programs. In the third section the authors introduce the problem of training media specification and treat technical issues such as databases and models, human-simulator interfaces, visual cueing and image systems, haptic, kinaesthetic and vestibular cueing, and finally, the methodology for training media specification. The final section of the book is devoted to training evaluation, covering the topics of performance measurement, workload measurement, and team performance. In the concluding part the authors outline the trends in using simulators for training.The primary audience for this book is the community of managers and experts involved in training operators. It can also serve as useful reference for designers of training simulators.CREATING COMPUTER SIMULATION SYSTEMS:An Introduction to the High Level Architectureby Frederick Kuhl, Richard Weatherly, Judith DahmannPrentice Hall, 1999, ISBN: 0130225118. - 212 pages.Given the increasing importance of simulations in nearly all aspects of life, the authors find that combining existing systems is much more efficient than building newer, more complex replacements. Whether the interest is in business, the military, or entertainment or is even more general, the book shows how to use the new standard for building and integrating modular simulation components and systems. The HLA, adopted by the U.S. Department of Defense, has been years in the making and recently came ahead of its competitors to grab the attention of engineers and designers worldwide. The book and the accompanying CD-ROM set contain an overview of the rationale and development of the HLA; a Windows-compatible implementation of the HLA Runtime Infrastructure (including test software). It allows the reader to understand in-depth the reasons for the definition of the HLA and its development, how it came to be, how the HLA has been promoted as an architecture, and why it has succeeded. Of course, it provides an overview of the HLA examining it as a software architecture, its large pieces, and chief functions; an extended, integrated tutorial that demonstrates its power and applicability to real-world problems; advanced topics and exercises; and well-thought-out programming examples in text and on disk.The book is well-indexed and may serve as a guide for managers, technicians, programmers, and anyone else working on building simulations.HANDBOOK OF SIMULATION:Principles, Methodology, Advances, Applications, and Practiceedited by Jerry BanksJohn Wiley & Sons, 1998, ISBN: 0471134031. Hardcover - 864 pages.Simulation modeling is one of the most powerful techniques available for studying large and complex systems. This book is the first ever to bring together the top 30 international experts on simulation from both industry and academia. All aspects of simulation are covered, as well as the latest simulation techniques. Most importantly, the book walks the reader through the various industries that use simulation and explains what is used, how it is used, and why.This book provides a reference to important topics in simulation of discrete- event systems. Contributors come from academia, industry, and software development. Material is arranged in sections on principles, methodology, recent advances, application areas, and the practice of simulation. Topics include object-oriented simulation, software for simulation, simulation modeling,and experimental design. For readers with good background in calculus based statistics, this is a good reference book.Applications explored are in fields such as transportation, healthcare, and the military. Includes guidelines for project management, as well as a list of software vendors. The book is co-published by Engineering and Management Press.ADVANCES IN MISSILE GUIDANCE THEORYby Joseph Z. Ben-Asher, Isaac YaeshAIAA, 1998, ISBN 1-56347-275-9.This book about terminal guidance of intercepting missiles is oriented toward practicing engineers and engineering students. It contains a variety of newly developed guidance methods based on linear quadratic optimization problems. This application-oriented book applies widely used and thoroughly developed theories such LQ and H-infinity to missile guidance. The main theme is to systematically analyze guidance problems with increasing complexity. Numerous examples help the reader to gain greater understanding of the relative merits and shortcomings of the various methods. Both the analytical derivations and the numerical computations of the examples are carried out with MATLAB Companion Software: The authors have developed a set of MATLAB M-files that are available on a diskette bound into the book.CONTROL OF SPACECRAFT AND AIRCRAFTby Arthur E. Bryson, Jr.Princeton University Press, 1994, ISBN 0-691-08782-2.This text provides an overview and summary of flight control, focusing on the best possible control of spacecraft and aircraft, i.e., the limits of control. The minimum output error responses of controlled vehicles to specified initial conditions, output commands, and disturbances are determined with specified limits on control authority. These are determined using the linear-quadratic regulator (LQR) method of feedback control synthesis with full-state feedback. An emphasis on modeling is also included for the design of control systems. The book includes a set of MATLAB M-files in companion softwareMATHWORKSInitial information MATLAB is given in this volume to allow to present next the Simulink package and the Flight Dynamics Toolbox, providing for rapid simulation-based design. MATLAB is the foundation for all the MathWorks products. Here we would like to discus products of MathWorks related to the simulation, especially Code Generation tools and Dynamic System Simulation.Code Generation and Rapid PrototypingThe MathWorks code generation tools make it easy to explore real-world system behavior from the prototyping stage to implementation. Real-Time Workshop and Stateflow Coder generate highly efficient code directly from Simulink models and Stateflow diagrams. The generated code can be used to test and validate designs in a real-time environment, and make the necessary design changes before committing designs to production. Using simple point-and-click interactions, the user can generate code that can be implemented quickly without lengthy hand-coding and debugging. Real-Time Workshop and Stateflow Coder automate compiling, linking, and downloading executables onto the target processor providing fast and easy access to real-time targets. By automating the process of creating real-time executables, these tools give an efficient and reliable way to test, evaluate, and iterate your designs in a real-time environment.Real-Time Workshop, the code generator for Simulink, generates efficient, optimized C and Ada code directly from Simulink models. Supporting discrete-time, multirate, and hybrid systems, Real-Time Workshop makes it easy to evaluate system models on a wide range of computer platforms and real-time environments.Stateflow Coder, the standalone code generator for Stateflow, automatically generates C code from Stateflow diagrams. Code generated by Stateflow Coder can be used independently or combined with code from Real-Time Workshop.Real-Time Windows Target, allows to use a PC as a standalone, self-hosted target for running Simulink models interactively in real time. Real-Time Windows Target supports direct I/O, providing real-time interaction with your model, making it an easy-to-use, low-cost target environment for rapid prototyping and hardware-in-the-loop simulation.xPC Target allows to add I/O blocks to Simulink block diagrams, generate code with Real-Time Workshop, and download the code to a second PC that runs the xPC target real-time kernel. xPC Target is ideal for rapid prototyping and hardware-in-the-loop testing of control and DSP systems. It enables the user to execute models in real time on standard PC hardware.By combining the MathWorks code generation tools with hardware and software from leading real-time systems vendors, the user can quickly and easily perform rapid prototyping, hardware-in-the-loop (HIL) simulation, and real-time simulation and analysis of your designs. Real-Time Workshop code can be configured for a variety of real-time operating systems, off-the-shelf boards, and proprietary hardware.The MathWorks products for control design enable the user to make changes to a block diagram, generate code, and evaluate results on target hardware within minutes. For turnkey rapid prototyping solutions you can take advantage of solutions available from partnerships between The MathWorks and leading control design tools:q dSPACE Control Development System: A total development environment forrapid control prototyping and hardware-in-the-loop simulation;q WinCon: Allows you to run Real-Time Workshop code independently on a PC;q World Up: Creating and controlling 3-D interactive worlds for real-timevisualization;q ADI Real-Time Station: Complete system solution for hardware-in-the loopsimulation and prototyping.q Pi AutoSim: Real-time simulator for testing automotive electronic control units(ECUs).q Opal-RT: a rapid prototyping solution that supports real-time parallel/distributedexecution of code generated by Real-Time Workshop running under the QNXoperating system on Intel based target hardware.Dynamic System SimulationSimulink is a powerful graphical simulation tool for modeling nonlinear dynamic systems and developing control strategies. With support for linear, nonlinear, continuous-time, discrete-time, multirate, conditionally executed, and hybrid systems, Simulink lets you model and simulate virtually any type of real-world dynamic system. Using the powerful simulation capabilities in Simulink, the user can create models, evaluate designs, and correct design flaws before building prototypes.Simulink provides a graphical simulation environment for modeling dynamic systems. It allows to build quickly block diagram models of dynamic systems. The Simulink block library contains over 100 blocks that allow to graphically represent a wide variety of system dynamics. The block library includes input signals, dynamic elements, algebraic and nonlinear functions, data display blocks, and more. Simulink blocks can be triggered, enabled, or disabled, allowing to include conditionally executed subsystems within your models.FLIGHT DYNAMICS TOOLBOX – FDC 1.2report by Marc RauwFDC is an abbreviation of Flight Dynamics and Control. The FDC toolbox for Matlab and Simulink makes it possible to analyze aircraft dynamics and flight control systems within one softwareenvironment on one PC or workstation. The toolbox has been set up around a general non-linear aircraft model which has been constructed in a modular way in order to provide maximal flexibility to the user. The model can be accessed by means of the graphical user-interface of Simulink. Other elements from the toolbox are analytical Matlab routines for extracting steady-state flight-conditions and determining linearized models around user-specified operating points, Simulink models of external atmospheric disturbances that affect the motions of the aircraft, radio-navigation models, models of the autopilot, and several help-utilities which simplify the handling of the systems. The package can be applied to a broad range of stability and control related problems by applying Matlab tools from other toolboxes to the systems from FDC 1.2. The FDC toolbox is particularly useful for the design and analysis of Automatic Flight Control Systems (AFCS). By giving the designer access to all models and tools required for AFCS design and analysis within one graphical Computer Assisted Control System Design (CACSD) environment the AFCS development cycle can be reduced considerably. The current version 1.2 of the FDC toolbox is an advanced proof of concept package which effectively demonstrates the general ideas behind the application of CACSD tools with a graphical user- interface to the AFCS design process.MODELING AND SIMULATION TERMINOLOGYMILITARY SIMULATIONTECHNIQUES & TECHNOLOGYIntroduction to SimulationDefinitions. Defines simulation, its applications, and the benefits derived from using the technology. Compares simulation to related activities in analysis and gaming.DOD Overview. Explains the simulation perspective and categorization of the US Department of Defense.Training, Gaming, and Analysis. Provides a general delineation between these three categories of simulation.System ArchitecturesComponents. Describes the fundamental components that are found in most military simulations.Designs. Describes the basic differences between functional and object oriented designs for a simulation system.Infrastructures. Emphasizes the importance of providing an infrastructure to support all simulation models, tools, and functionality.Frameworks. Describes the newest implementation of an infrastructure in the forma of an object oriented framework from which simulation capability is inherited.InteroperabilityDedicated. Interoperability initially meant constructing a dedicated method for joining two simulations for a specific purpose.DIS. The virtual simulation community developed this method to allow vehicle simulators to interact in a small, consistent battlefield.ALSP. The constructive, staff training community developed this method to allow specific simulation systems to interact with each other in a single joint training exercise. HLA. This program was developed to replace and, to a degree, unify the virtual and constructive efforts at interoperability.JSIMS. Though not labeled as an interoperability effort, this program is pressing for a higher degree of interoperability than have been achieved through any of the previous programs.Event ManagementQueuing. The primary method for executing simulations has been various forms of queues for ordering and releasing combat events.Trees. Basic queues are being supplanted by techniques such as Red-Black and Splay trees which allow the simulation store, process, and review events more efficiently than their predecessors.Event Ownership. Events can be owned and processed in different ways. Today's preference for object oriented representations leads to vehicle and unit ownership of events, rather than the previous techniques of managing them from a central executive.Time ManagementUniversal. Single processor simulations made use of a single clocking mechanism to control all events in a simulation. This was extended to the idea of a "master clock" during initial distributed simulations, but is being replaced with more advanced techniques in current distributed simulation.Synchronization. The "master clock" too often lead to poor performance and required a great deal of cross-simulation data exchange. Researchers in the Parallel Distributed Simulation community provided several techniques that are being used in today's training environment.Conservative & Optimistic. The most notable time management techniques are conservative synchronization developed by Chandy, Misra, and Bryant, and optimistic synchronization (or Time Warp) developed by David Jefferson.Real-time. In addition to being synchronized across a distributed computing environment, many of today's simulators must also perform as real-time systems. These operate under the additional duress of staying synchronized with the human or system clock perception of time.Principles of ModelingScience & Art. Simulation is currently a combination of scientific method and artistic expression. Learning to do this activity requires both formal education and watching experienced practitioners approach a problem.Process. When a team of people undertake the development of a new simulation system they must follow a defined process. This is often re-invented for each project, but can better be derived from experience of others on previous projects.Fundamentals. Some basic principles have been learned and relearned by members of the simulation community. These have universal application within the field and allow new developers to benefit from the mistakes and experiences of their predecessors.Formalism. There has been some concentrated effort to define a formalism for simulation such that models and systems are provably correct. These also allow mathematical exploration of new ideas in simulation.Physical ModelingObject Interaction. Military object modeling is be divided into two pieces, the physical and the behavioral. Object interactions, which are often viewed as 'physics based', characterize the physical models.Movement. Military objects are often very mobile and a great deal of effort can be given to the correct movement of ground, air, sea, and space vehicles across different forms of terrain or through various forms of ether.Sensor Detection. Military object are also very eager to interact with each other in both peaceful and violent ways. But, before they can do this they must be able to perceive each other through the use of human and mechanical sensors.Engagement. Encounters with objects of a different affiliation often require the application of combat engagement algorithms. There are a rich set of these available to the modeler, and new ones are continually being created.Attrition. Object and unit attrition may be synonymous with engagement in the real world, but when implemented in a computer environment they must be separated to allow fair combat exchanges. Distributed simulation systems are more closely replicating real world activities than did their older functional/sequential ancestors, but the distinction between engagement and attrition are still important. Communication. The modern battlefield is characterized as much by communication and information exchange as it is by movement and engagement. This dimension of the battlefield has been largely ignored in previous simulations, but is being addressed in the new systems under development today.More. Activities on the battlefield are extremely rich and varied. The models described in this section represent some of the most fundamental and important, but they are only a small fraction of the detail that can be included in a model.Behavioral ModelingPerception. Military simulations have historically included very crude representations of human and group decision making. One of the first real needs for representing the human in the model was to create a unique perception of the battlefield for each group, unit, or individual.Reaction. Battlefield objects or units need to be able to react realistically to various combat environments. These allow the simulation to handle many situations without the explicit intervention of a human operator.Planning. Today we look for intelligent behavior from simulated objects. Once form of intelligence is found in allowing models to plan the details of a general operational combat order, or to formulate a method for extracting itself for a difficult situation.Learning. Early reactive and planning models did not include the capability to learn from experience. Algorithms can be built which allow units to become more effective as they become more experienced. They also learn the best methods for operating on a specific battlefield or under specific conditions.Artificial Intelligence. Behavioral modeling can benefit from the research and experience of the AI community. Techniques of value include: Intelligent Agents, Finite State Machines, Petri Nets, Expert and Knowledge-based Systems, Case Based Reasoning, Genetic Algorithms, Neural Networks, Constraint Satisfaction, Fuzzy Logic, and Adaptive Behavior. An introduction is given to each of these along with potential applications in the military environment.Environmental ModelingTerrain. Military objects are heavily dependent upon the environment in which they operate. The representation of terrain has been of primary concern because of its importance and the difficulty of managing the amount of data required. Triangulated Irregular Networks (TINs) are one of the newer techniques for managing this problem. Atmosphere. The atmosphere plays an important role in modeling air, space, and electronic warfare. The effects of cloud cover, precipitation, daylight, ambient noise, electronic jamming, temperature, and wind can all have significant effects on battlefield activities.Sea. The surface of the ocean is nearly as important to naval operations as is terrain to army operations. Sub-surface and ocean floor representations are also essential for submarine warfare and the employment of SONAR for vehicle detection and engagement.Standards. Many representations of all of these environments have been developed.Unfortunately, not all of these have been compatible and significant effort is being given to a common standard for supporting all simulations. Synthetic Environment Data Representation and Interchange Specification (SEDRIS) is the most prominent of these standardization efforts.Multi-Resolution ModelingAggregation. Military commanders have always dealt with the battlefield in an aggregate form. This has carried forward into simulations which operate at this same level, omitting many of the details of specific battlefield objects and events.Disaggregation. Recent efforts to join constructive and virtual simulations have required the implementation of techniques for cross the boundary between these two levels of representation. Disaggregation attempts to generate an entity level representation from the aggregate level by adding information. Conversely, aggregation attempts to create the constructive from the virtual by removing information.Interoperability. It is commonly accepted that interoperability in these situations is best achieved though disaggregation to the lowest level of representation of the models involved. In any form the patchwork battlefield seldom supports the same level of interoperability across model levels as is found within models at the same level of resolution.Inevitability. Models are abstractions of the real world generated to address a specific problem. Since all problems are not defined at the same level of physical representation, the models built to address them will be at different levels. The modeling an simulation problem domain is too rich to ever expect all models to operate at the same level. Multi-Resolution Modeling and techniques to provide interoperability among them are inevitable.Verification, Validation, and AccreditationVerification. Simulation systems and the models within them are conceptual representations of the real world. By their very nature these models are partially accurate and partially inaccurate. Therefore, it is essential that we be able to verify that the model constructed accurately represents the important parts of the real world we are try to study or emulate.Validation. The conceptual model of the real world is converted into a software program. This conversion has the potential to introduce errors or inaccurately represent the conceptual model. Validation ensures that the software program accurately reflects the conceptual model.Accreditation. Since all models only partially represent the real world, they all have limited application for training and analysis. Accreditation defines the domains and。
仿真模型可信度评估研究框架
第6期 2020年6月Journal of CAEITVol. 15 No. 6 Jun. 2020d o i ： 10.3969/j. issn. 1673-5692.2020.06.011仿真模型可信度评估研究框架杨小军，徐忠富，崔龙飞，耿杰恒(中国洛阳电子装备试验中心，河南洛阳471003)摘要：仿真模型在军事、社会、经济等领域得到了广泛应用，与此同时，人们对仿真模型可信度的要求也越来越高。
然而，仿真模型可信度评估牵涉到对大量定性和定量指标的测量与评估，需要综 合运用多种方法和技术。
通过分析多指标综合评估的一般步骤，建立了仿真模型可信度评估研究 框架，并对研究框架中的13个处理过程及其常用方法进行了详细描述，分析了研究重点和方向。
该研究框架的建立有利于从总体上把握可信度评估的基本步驟和方法，明确重点研究内容和方向， 从而推进可信度评估研究和实践。
关键词：可信度评估；仿真模型；研究框架；指标体系中图分类号：TP391.9 文献标志码：A 文章编号：1673-5692(2020)06-5574)9Research Framework for Credibility Assessment of Simulation ModelsY A N G Xiao -j u n , X U Z h o n g -fu , C U I Lon g -fei , G E N G Jie-heng(Luoyang Electronic Equipment Test Center of China, Luoyang 471003 , China)A b s t r a c t ： Simulation models have been applied in military , social and economic areas widely theseyears . M e a n w h i l e , the credibility of simulation models is becoming more and more concerned . W h e r e a s , credibility assessment is a very complex process , involves the measurement and evaluation of hundreds of qualitative and quantitative indicators , and requires the integration of heterogeneous methods and tech niques . T h e research framework for credibility assessment of simulation models is constructed based on the analysis of multi-criteria comprehensive evaluation process . T h e n , thirteen processes in the framework and various methods for each process are introduced respectively , and the research keystones and direc tions are analyzed . T h e framework provides an overview of processes and methods for credibility assess m e n t , outlines the research issues and directions , thus m a y advance the research and practice on credi bility assessment of simulation models .K e y w o r d s ： credibility assessment ； simulation m o d e l s ； research framework ； indicator hierarchy〇引言随着计算机和仿真技术的发展，仿真模型在军 事、社会、经济等领域得到了广泛应用。
System Modeling and Simulation
System Modeling and Simulation System modeling and simulation play a crucial role in various fields such as engineering, business, and science. It involves creating a simplified representation of a system in order to analyze its behavior and make predictions. This process allows for better understanding of complex systems and helps in making informed decisions. However, there are several challenges and issues that need to be addressed when it comes to system modeling and simulation. One of the main problems with system modeling and simulation is the accuracy of the model. Creating a model that accurately represents the real-world system can be a daunting task. It requires a deep understanding of the system and its components, as well as the ability to translate this knowledge into a mathematical or computational model. Inaccurate models can lead to misleading results and flawed predictions, which can have serious consequences in fields such as engineering and healthcare. Another challenge is the validation of the model. Once a model is created, it needs to be validated to ensure that it accurately represents thereal-world system. This involves comparing the model's predictions with actual data and making adjustments as necessary. Validation is a time-consuming and complex process that requires a deep understanding of the system and its behavior. Without proper validation, the model's predictions cannot be trusted, which undermines the entire purpose of modeling and simulation. Furthermore, there is the issue of scalability. Many real-world systems are incredibly complex and involve a large number of components and interactions. Creating a model that accurately represents such systems can be extremely challenging, and running simulations on these models can be computationally intensive. As a result, scalability becomes a major concern in system modeling and simulation. It is important to develop techniques and tools that can handle the complexity and size of real-world systems without sacrificing accuracy and efficiency. In addition, there is the problem of uncertainty. Many real-world systems are subject to various sources of uncertainty, such as random events, measurement errors, and incomplete information. Incorporating uncertainty into models and simulations is a difficult task that requires a deep understanding of probability theory and statistics. Failure to properly account for uncertainty can lead to unreliablepredictions and decisions, which can have serious consequences in fields such as finance and environmental management. Moreover, there is the challenge of interdisciplinary collaboration. System modeling and simulation often require expertise from multiple disciplines, such as mathematics, computer science, and engineering. Bringing together experts from different fields and integrating their knowledge can be a complex and time-consuming process. Effective collaboration is essential for creating accurate and reliable models, but it requires overcoming communication barriers and aligning different perspectives and methodologies. Lastly, there is the issue of ethical considerations. System modeling and simulation can have far-reaching implications, especially in fields such as healthcare, finance, and environmental management. The predictions and decisions made based on models can have significant impacts on people's lives and the environment. It is important to consider the ethical implications of modeling and simulation, such as ensuring fairness, transparency, and accountability in decision-making processes. Failing to address these ethical considerations can lead to misuse and abuse of modeling and simulation, which can have detrimental effects on society. In conclusion, system modeling and simulation are powerful tools for understanding and predicting the behavior of complex systems. However, there are several challenges and issues that need to be addressed in order to ensure the accuracy, reliability, and ethical use of models and simulations. By addressing these challenges, we can harness the full potential of system modeling and simulation and make informed decisions that benefit society as a whole.。
Introduction_to_Modeling_and_Simulation[1]
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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.。
OrCADPSpiceDesigner
OrCAD® PSpice® and OrCAD Capture combine to provide industry-leading, schematic entry, native analog, mixed-signal, and analysis engines to deliver a complete circuit simulation and verification solution. Whether you’re prototyping simple circuits or designing complex systems, the PSpice Designer product provides the best circuit-simulation technology to analyzeand refine your circuits, components, and parameters before committing to layout and fabrication.OverviewOrCAD PSpice is a high-performance, industry-proven,mixed-signal simulator and waveform viewer for analog and mixed-signal circuits. As one of the most popular, general-purpose and mixed-mode circuit simulators with extensively available models from component and IC vendors, OrCAD PSpice simulation technology is applicable for product designin numerous industries such as aerospace, medical, power electronics, and automotive. It is also utilized extensively within the research community as a reference implementation. It is capable of simulating your designs from simple circuits, complex electronics, and power supplies to radio-frequency systemsand targeted IC designs. With built-in mathematical functions, behavioral modeling, circuit optimization, and electromechanical co-simulation, the OrCAD PSpice environment goes far beyond general circuit simulation.Included in the OrCAD PSpice Designer product with OrCADPSpice, OrCAD Capture provides fast, easy, and intuitive circuit capture, along with highly integrated flows supporting the engineering process. With an upgrade to the OrCAD PSpice Designer Plus product, Advanced Analysis simulation engines provide you with functional simulation to improve design perfor-mance, cost-effectiveness, and reliability.In addition, integration with MathWorks MATLAB Simulink provides an analysis flow enabling multi-domain simulation, such as electromechanical co-simulation.Simulation FeaturesSimulationOrCAD PSpice simulation technology provides DC, AC, and transient analysis, so you can test the response of your circuits to varying inputs. It also provides digital worst-case timing analysis to help you find timing problems that occur with circuit signal transitions. Mixed-signal designs can also be verified wherethe analog portions have digital content embedded. Integrated Highlights• Extensive model library, model association and creation, multi-core support, and full integration with OrCAD Capture improve productivity and data integrity• MATLAB Simulink interface allows system-level interfaces to be tested with electrical designs emulating real-world applications• Ability to determine which components areover-stressed using Smoke analysis or observing the affects of component variations on yield using Monte Carlo analysis helps prevent “in-field” failures• Multi-vendor models, built-in mathematical functions, and behavioral modeling techniques enable highly tailored simulations• Powerful waveform viewing and post-processing expression support speed review and analysis without having to rerun simulations• Open architecture and program platformallows easy customization of algorithms andpost-processing of resultsThe extensive capabilities of Probe enable complex measurements, multiple waveform plots, and an expansive set of mathematical functions2analog and event-driven digital simulations mean improvedspeed without loss of accuracy, and complex measurements can be created and viewed as the simulation progresses.Results and data displayThe extensive capabilities of OrCAD PSpice Probe enable you to make complex measurements, cross-probe with the circuit design, view waveforms in multiple plots, and provide you with an expanded set of mathematical functions to apply to simulation output variables. OrCAD PSpice Probe also enables the measurement of performance characteristics of a circuit using built-in functions and the creation of custom measurements.With OrCAD PSpice Probe, you can plot both real and complex functions of circuit voltage, current, and power consumption, including Bode plots for gain and phase margin and derivatives for small-signal characteristics. You can display Fourier transforms of time domain signals or inverse Fourier transforms of frequency domain signals. You can also vary component values overmultiple runs and quickly view results as a family of waveforms with parametric, Monte Carlo, and worst-case analysis.Models and modelingAlong with numerous vendor models and model libraries available online, the OrCAD PSpice model library offers more than 30,000 analog and mixed-signal models. This library includes parameterized models such as BJTs, JFETs, MOSFETs, IGBTs, SCRs, discretes, operational amplifiers, optocouplers, regulators, PWM controllers, and multipliers. A device equations developer’s kit (DEDK) allows implementation of new and custom internal model equations.You can describe behavior modeling though functional blocks using mathematical expressions and functions, which allow you to leverage a full set of mathematical operators, nonlinear functions, and filters. Circuit behavior can be defined in the time or frequency domain, by formula (including Laplace transforms) or by look-up tables.The integrated OrCAD PSpice Model Editor provides you with an easy way to create models using device characteristic curves. An intuitive stimulus creation capability makes it easy to create a variety of simulation stimuli. Any shape stimulus can be created with built-in functions and can be described parametrically or freehand with the mouse to draw piece-wise linear (PWL) signals.Flexibility and controlThe OrCAD PSpice CheckPoint Restart feature provides greater control over your simulations. You can stop and restart, generate checkpoints at specified points in time of a simulation, and then restart the simulation from a specific checkpoint.In addition, you can add assertions to detect failure or warning conditions as the simulation progresses, so you don’t need to wait for complete simulations to detect error conditions. Simulation profiles allow binding of models and stimulus to enable simulation of different test conditions using same schematic, and you can also queue-up simulations for overnight results.Stimulus editorThe OrCAD PSpice Stimulus Editor is an interactive, graphical environment to define and preview circuit stimulus character-istics. The use model allows access to built-in stimulus functions that can be described parametrically, or provides the ability to draw PWL signals freehand with the mouse to create any shape stimulus. You can create digital stimuli for signals, clocks, and buses, and then click-and-drag to introduce and move transitions.Design Solutions and FlowsCapture front-end integrationOrCAD PSpice technology is seamlessly integrated with OrCAD Capture—one of the most widely used schematic design solutions—allowing you to easily cross-probe between theschematic design and simulation plot results and measurements. This integration also allows you to use the same schematic for both simulation exploration and PCB layout, reducing rework and errors. Even if you’re not creating a circuit for use in the PCB flow, the integration allows for easy setup, model placement, circuit creation, and simulation.Integration with MATLAB SimulinkOrCAD PSpice integration with MATLAB Simulink (SLPS)brings two industry-leading simulation tools in a co-simulation environment. SLPS integration enables designers of electrome-chanical systems—such as control blocks, motors, sensors, and power converters—to perform integrated system and circuitsimulations that include realistic, electrical OrCAD PSpice models of physical components.© 2014 Cadence Design Systems, Inc. All rights reserved worldwide. Cadence, OrCAD, PSpice, and the Cadence logo are registered trademarks and the OrCAD logo is a trademark of Cadence Design Systems, Inc. in the United States and other countries. All others are the properties of their respective holders. 2355 04/14 SA/DM/PDFAdvanced AnalysisOrCAD PSpice Advanced Analysis simulation is used to improve your design’s performance, yield, and reliability. Capabilities such as temperature and stress analysis, worst-case analysis, Monte Carlo analysis, and automatic performance-optimization algorithms improve design quality and maximize circuit perfor-mance.Open Architecture PlatformEnabling an extensible and customizable design environment, OrCAD’s open architecture platform incorporates a highly integrated Tcl/HTML5 programming infrastructure that allows the creation or enhancement of features, functionality, design capabilities, and flows. The Tcl programming interface provides programming access to the user interface, command structure, simulation data, and algorithm process. Custom features that do not exist natively can be created, further enhancing and extending the OrCAD PSpice environment.For the latest product or release information, visit us at or contact your local Cadence Channel Partner.Sales, Technical Support, and TrainingThe OrCAD product line is owned by Cadence Design Systems, Inc., and is supported by a worldwide network of CadenceChannel Partners (VARs). For sales, technical support, or training, contact your local channel partner. For a complete list of autho-rized channel partners, visit /CCP-Listing .。
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.。
数据链系统通用建模与仿真框架
数据链系统通用建模与仿真框架∗王文政;曹琦【摘要】In order to provide a general tool for research on the analysis, evaluation and training of data link system, the modeling and simulation framework is proposed based on the analysis of the needs of modeling and si-mulation of data link system, which is aimed at improving the reuse of data link system model, meeting the needs of different levels of data link system simulation at the same time, and enhancing the data link si-mulation system scalability. Firstly, the framework of component modeling of data link system is presented, the logic structure of the models in which is analyzed, and the model-ing process is described. Then, the framework of data link system simulation is designed based on the modeling framework, and its structure and application process are described in detail.%为开展数据链系统性能分析、效能评估及模拟训练等多样化研究，在建模和仿真需求分析的基础上，提出了通用数据链系统建模与仿真框架，旨在提高模型重用性，满足不同层次数据链仿真需求以及增强仿真系统可扩展性。
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Working in a university research environment, one goal was to develop a tool that could be used to analyze and simulate computer communication network designs. One of our first challenges was to develop a mathematical formulation to represent power and signal flow in components in a computer communication network. A second challenge was to be able to simulate a complete system. Another challenge was to develop component models based on specificationsof component characteristics in power and dB attenuation loss terminology. Some of the initial concerns were the impact of bidirectional communication on component models, a lack of readily available circuit models for cable systems components, and a natural trend to perform discrete simulation to conform to digital signals and systems. Computer communication networks (such as Ethernet or MAP or RS-232) are becoming a standard/common item in office and manufacturing environments [l-51. Yet, their fundamental electrical operation and characteristics are understood by a small number of engineers and communication network designers. This article has as one goal the greater dissemination of information about components used in a computer communication network. II. COMPONENTS, SPECIFICATIONS, and TERMINOLOGY of COMPUTER COMMUNICATION NETWORKS. Components that normally are used in computer communication networks have names such as coax (cable), transceivers, repeaters, bridges, routers, head-end remodulator, modem, (signal) splitter, directional coupler/tap, or multiple-way (e.g., 4-way or 8-way) directional tap. In a communication network, each of the components must have bi-directional signal transmission. In some cases, the components must be capable of transmitting signals that contain different frequency ranges, or frequency multiplexed signals. Terms such as characteristic impedance, attenuation, dB loss, insertion loss, isolation loss, dBm, etc. are frequently used as terminology. An example specification for a coax cable could include the following:
A few knowledgeable individuals (faculty, graduate students, and industry colleagues) were contacted about the component specifications and characteristics, as well as methods for analysis of a computer communication network. Formal network analysis, simulation, and design algorithms for computer networks were not available to the general technical community. While some of the individuals could (and did) speak about the general properties of the components, not one of them presented a mathematical representation of the components, nor were they able to recommend math based references about the component characteristics. And, such information was considered proprietary by s at cable system design firms.
Models and Simulation for Analysis of a Computer Network L. C. McAfee, Jr. Associate Professor Electrical Engineering and Computer Science Department The University of Michigan Ann Arbor, Michigan 481 09-2122 313-764-0218 Research supported by the Semiconductor Research Corporation ABSTRACT Computer communication networks are becoming common items in office and manufacturing environments. Electrical component characteristics and network operation are understood by a small number of engineers and designers with knowledge about communication networks. This lack of widespread knowledge was recognized when a high-speed computer communication network was being installed in the integrated circuit (IC) fabrication facility in the Solid-state Electronics Laboratory (SSEL) at The University of Michigan. At a reasonable cost, it was not possible to independently verify a proper design for that facility. To develop a capability to analyze computer communication networks, two important methods were developed: (1) a model parameter extraction algorithm for network components and ( 2 ) a simulation algorithm to analyze electrical operation of an overall network. The model parameter extraction algorithm is crucial to the success of this effort. The model parameter extraction algorithm is based on a consistent mathematical formulation of multi-port models to represent the real-world situation of components with several input/output ports, and components that have specifications cited in terms of attenuation losses. The extraction algorithm has been used to compute model parameters for components having 1 port to 10 ports. The extracted model parameters are then placed in the network analysis simulation algorithm to compute characteristics for the complete network. The simulator includes automated input of the computer network description with component specifications, internal models of computer network components, automatic equation generation, equation solution methods, and output (display) of electrical operational characteristics of the computer network. The simulator has been used to analyze the network characteristics of the SSEL IC facility and other networks, including a generic computer network that was developed to exercise and validate all formulated models in the simulator. The important results of this article are: (1) a simulation algorithm to analyze computer communication networks using internal models of components, (2) an algorithm for model parameter extraction for the components of the networks, and (3) analysis of sample computer networks to validate the models, the model parameter extraction algorithms, and the simulation algorithm.