Showing papers on "Modeling and simulation published in 2000"

Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems

Abstract: Part I: Basics. Introduction to Systems Modeling Concepts. Framework for Modeling and Simulation. Modeling Formalisms and Their Simulators. Introduction to Discrete Event System Specifications (DEVS). Hierarchy of System Specifications. Part II: Modeling Formalisms and Simulation Algorithms. Basic Formalisms: DEVS, DTSS, DESS. Basic Formalisms: Coupled Multicomponent Systems. Simulators for Basic Formalisms. Multiformalism Modeling and Simulation. DEVS-Based Extended Formalisms. Parallel and Distributed Discrete Event Simulation. Part III: System Morphisms: Abstraction, Representation, Approximation. Hierarchy of System Morphisms. Abstraction: Constructing Model Families. Verification, Validation, Approximate Morphisms: Living with Error. DEVS and DEVS-like Systems: Universality and Uniqueness. DEVS Representation of Systems. Part IV: System Design and Modeling and Simulation Environments. DEVS-Based Design Methodology. System Entity Structure/Model Base Framework. Collaboration and the Future.

Simulation of Communication Systems: Modeling, Methodology and Techniques

Abstract: From the Publisher: Since the first edition of this book was published seven years ago, the field of modeling and simulation of communication systems has grown and matured in many ways, and the use of simulation as a day-to-day tool is now even more common practice. With the current interest in digital mobile communications, a primary area of application of modeling and simulation is now in wireless systems of a different flavor from the 'traditional' ones. This second edition represents a substantial revision of the first, partly to accommodate the new applications that have arisen. New chapters include material on modeling and simulation of nonlinear systems, with a complementary section on related measurement techniques, channel modeling and three new case studies; a consolidated set of problems is provided at the end of the book.

Digital Signal Integrity: Modeling and Simulation with Interconnects and Packages

Abstract: From the Publisher: State-of-the-art techniques for predicting and achieving target performance levels Theory, practice, general signal integrity issues, and leading-edge experimental techniques Model and simulate high-speed digital systems for maximum performance Maximizing the performance of digital systems means optimizing their high-speed interconnections. Digital Signal Integrity gives engineers all the theory and practical methods they need to accurately model and simulate those interconnections and predict real-world performance. Whether you're modeling microprocessors, memories, DSPs, or ASICs, these techniques will get you to market faster with greater reliability. Coverage includes: In-depth reviews of inductance, capacitance, resistance, single and multiconductor transmission lines, generalized termination schemes, crosstalk, differential signaling, and other modeling/simulation issues Multiconductor interconnects: packages, sockets, connectors and buses Modal decomposition: understanding the outputs generated by commercial modeling software Layer peeling with time-domain reflectometry: its power and limitations Experimental techniques for characterizing interconnect parasitics In Digital Signal Integrity, Motorola senior engineer Brian Young presents broad coverage of modeling from data obtained through electromagnetic simulation, transmission line theory, frequency and time-domain modeling, analog circuit simulation, digital signaling, and architecture. Young offers a strong mathematical foundation for every technique, as well as over 100 end-of-chapterproblems. If you're stretching the performance envelope, you must be able to rely on your models and simulations. With this book, you can.

Modeling and transient simulation of planes in electronic packages

Abstract: This paper presents a modeling and simulation approach for ground/power planes in high speed packages. A plane pair structure is first characterized in terms of its impedance (Z) matrix at arbitrary port locations in the frequency domain. This solution is then extended for multiple plane pairs under the assumption that skin effect is prominent at higher frequencies causing isolation between the layers. Since the solutions are in analytical form, the frequency and transient response can be computed efficiently requiring small computational time. To develop spice models, equivalent circuits are constructed using resonator models with passive elements using model order reduction methods. This paper also discusses a method for incorporating decoupling capacitors into the plane models. The simulation results show good correlation with measured data.

Modeling for All Scales: An Introduction to System Simulation

Abstract: Preface. Part I: Modeling: Modeling and Simulation. Energy Systems Diagramming. Numbers on Networks. Part II: Simulation: Simulation with Picture Blocks. Simulation with General Systems Blocks. Equations with Diagrams. Calibrating Models. Simulating with Spreadsheets. Programming in BASIC. Simulating with STELLA. Simulating Energy and Transformity. Part III: Fundamental Minimodels: Suggestions for Simulation. Models of Production and Recycle. Models of Growth. Models of Competition and Cooperation. Models of Series and Oscillation. Minimodels of Succession and Evolution. Models of Microeconomics. Models of Macroeconomic Overview. Models of International Relations and Trade. Models of the Global Geobiosphere. Part IV: Application: Modeling Projects and Complexity. Simulation Approaches. Appendices: Programming Blocks for GENSYS using EXTEND. Notes on Computer Use and BASIC Programming. Answers to Questions. Use of Energy Systems Symbols. Contents of the CD with this Book. References and Suggested Readings. Tables. Legends. Figures.

Theory and applications of power system blockset, a MATLAB/Simulink-based simulation tool for power systems

Abstract: This paper presents a unified framework for power system simulation using a Simulink environment. The modeling and simulation of power systems under Simulink are described and discussed. Application examples for power systems including power networks, power electronics and electric machines are presented and compared with two popular simulation packages (EMTP, PSpice).

Model and analysis for combined package and on-chip power grid simulation

Abstract: We present new modeling and simulation techniques to improve the accuracy and efficiency of transient analysis of large power distribution grids. These include an accurate model for the inherent decoupling capacitance of non-switching devices, as well as a statistical switching current model for the switching devices. Moreover, three new simulation techniques are presented for problem size-reduction and speed-up. Results of application of these techniques on three PowerPC/sup TM/ microprocessors are also presented.

A method for reduced-order modeling and simulation of large interconnect circuits and its application to PEEC models with retardation

Abstract: The continuous improvement in the performance and the increases in the sizes of VLSI systems make electrical interconnect and package (EIP) design and modeling increasingly more important. Special software tools must be used for the design of high-performance VLSI systems. Furthermore, larger and faster systems require larger and more accurate circuit models. The partial element equivalent circuit (PEEC) technique is used for modeling such systems with three-dimensional full wave models. In this paper, we present a practical, readily parallelizable procedure for generating reduced-order frequency-domain models from general full wave PEEC systems. We use multiple expansion points, and piecemeal construction of pole-residue approximations to transfer functions of the PEEC systems, as was used in the complex frequency hopping algorithms. We consider general, multiple-input/multiple-output PEEC systems. Our block procedure consists of an outer loop of local approximations to the PEEC system, coupled with an inner loop where an iterative model-reduction method is applied to the local approximations. We systematically divide the complex frequency region of interest into small regions and construct local approximations to the PEEC system in each subregion. The local approximations are constructed so that the matrix factorizations associated with each of them are the size of the original system and independent of the order of the approximation. Results of computations on these local systems are combined to obtain a reduced-order model for the original PEEC system. We demonstrate the usefulness of our approach with three interesting examples.

Heterogeneous modeling and simulation of embedded systems in El Greco

Abstract: This paper describes the functional specification and verification portions of El Greco, a system for high-level, heterogeneous functional specification, efficient compiled simulation, and software and hardware implementation. Specifications in the form of dataflow graphs, hierarchical finite state machines, or a mixture, are supported. These specifications can be arbitrarily nested, as in Ptolemy [1]. When dataflow graphs are placed in a control context, the graph execution is fully controllable; its execution can be restarted or suspended and parameters can be changed. We describe system modeling and simulation generation in El Greco and compare to other approaches.

Composable simulations

Abstract: While there has been much attention paid to the applications of modeling and simulation (M&S) by the Department of Defense (DoD) lately, little has been done to address those broad technology areas that enable application-oriented simulations to be more easily constructed, run and analyzed. Model composabilty is the ability to compose models/modules across a variety of application domains, levels of resolution and time scales. A composability framework for simulations offers a quantum leap in capability and provides the sought after ease of use. However, composability is still a frontier subject in modeling and simulation, and current capability is limited. While model reuse is close to the Holy Grail of M&S, the goal is not within sight. We are discovering that unless models are designed to work together, they don't (at least not easily and cost effectively). Without a robust, theoretically grounded framework for design, we are consigned to repeat this problem for the foreseeable future. This paper outlines the challenges and basic research that needed for composable simulation developments.

Using simulation to analyze supply chains

Abstract: Supply Chain management, the management of the flow of goods or services from materials stage to the end user, is a complex process because of the level of uncertainty at each stage of the supply chain. Computer simulation, because it can be applied to operational problems that are too difficult to model and solve analytically, is an especially effective tool to help analyze supply chain logistical issues.While most engineers have had some exposure to the tools and technology of computer modeling and simulation, the use of simulation for supply chain analysis has not been prevalent until recently. The software tool, Supply Solver, was developed in an effort to provide supply chain solutions using simulation as the foundation. In this paper, the goal will be to show how discrete-event simulation is used to analyze supply chain processes. This paper will also demonstrate what some of the considerations are in using Supply Solver to help solve supply chain design problems.

Analysis of the virtual enterprise using distributed supply chain modeling and simulation: an application of e-SCOR

Abstract: Supply chains are large systems consisting of many entities interacting in complex ways. The challenge faced by companies is how to design and manage such systems. Modeling and simulation enables analysis of complex systems but as the model increases in size and realism, or when it is necessary to locate model components geographically, a distribution capability is needed. The High Level Architecture (HLA), developed by the Department of Defense provides the infrastructure needed for large-scale distributed simulation. The supply chain management field is characterized by a lack of standards and definitions. The Supply Chain Council has established a standard way to examine and analyze supply chains with their Supply Chain Operations Reference, or SCOR model. The SCOR model provides a standard way of viewing a supply chain, a common set of manipulate-able variables and a set of accepted metrics for understanding the dynamic behavior of supply chains. The e-SCOR modeling and simulation environment is based on SCOR and adds discrete event simulation capabilities. This paper describes the architectural components used to implement a distributed supply chain modeling tool (e-SCOR) and applications of e-SCOR that demonstrate how enterprises are modeled and analyzed to determine the validity of alternative, virtual business models.

Modeling and simulation uncertainty in multidisciplinary design optimization

Abstract: This paper is intended to contribute to the ongoing discussion of selected concepts related to the topic of technical risk or uncertainty in the model-based design of physical artifacts. The paper focuses on the use of analytic models and numerical simulation in the multidisciplinary design optimization process. It considers how issues of physical process variability, information uncertainty and the use of models and simulations influence the design decision process. This paper only qualitatively addresses these issues but the goal is to provide a focus for discussion of concepts associated with information uncertainty as applied to model-based multidisciplinary design and optimization.

Improving the aircraft design process using Web-based modeling and simulation

Abstract: Designing and developing new aircraft systems is time-consuming and expensive. Computational simulation is a promising means for reducing design cycle times, but requires a flexible software environment capable of integrating advanced multidisciplinary and multifidelity analysis methods, dynamically managing data across heterogeneous computing platforms, and distributing computationally complex tasks. Web-based simulation, with its emphasis on collaborative composition of simulation models, distributed heterogeneous execution, and dynamic multimedia documentation, has the potential to meet these requirements. This paper outlines the current aircraft design process, highlighting its problems and complexities, and presents our vision of an aircraft design process using Web-based modeling and simulation.

Model composability: formulating a research thrust: composable simulations

Abstract: While there has been much attention paid to the applications of Modeling and Simulation (M&S) by the Department of Defense (DoD) lately, little has been done to address those broad technology areas that enable application-oriented simulations to be more easily constructed, run and analyzed. Model Composabilty is the ability to compose models/modules across a variety of application domains, levels of resolution and time scales. A composability framework for simulations offers a quantum leap in capability and provides the sought after ease of use. However, Composability is still a frontier subject in Modeling and Simulation, and current capability is limited. While model reuse is close to the Holy Grail of M&S, the goal is not within sight. We are discovering that unless models are designed to work together, they don't (at least not easily and cost effectively). Without a robust, theoretically grounded framework for design, we are consigned to repeat this problem for the foreseeable future. This position paper outlines the challenges and basic researches that are needed for composable simulation developments.

Modeling and simulation of storage area networks

Abstract: Storage area networks (SANs) are an emerging data communications platform which interconnects servers and storage devices (such as disks, disk arrays, and tape drives) to create a pool of storage that users can access directly. This networking approach reports benefits such as computer clustering, topological flexibility, fault tolerance, high availability, and remote management. In order to evaluate the performance of these systems it is necessary to have the adequate tools. Usually, performance evaluation may be based on analytical modeling or simulation. Each of them differs in their scope and applicability. However the simulation modeling technique offers more freedom, flexibility, and accuracy than the analytical methods. Thus, when evaluating the performance of SANs, simulation modeling should be used. In this paper the issues involved in the modeling and design of a very flexible and easy to use SAN simulator are presented. This tool is able to consider among others, both real-world I/O traces and synthetic I/O traffic, message packetization, faults in links and switches, virtual channels, different routing algorithms, etc. We describe its main internal organization, the basic modeling mechanisms the simulator is based on, the main input parameters and output performance variables. Also, the analysis of preliminary results using I/O traces is presented, showing that the storage network increases self-similarity of the traffic received by servers, latency variations are more important for control messages than for data messages, and links have a low utilization.

Modeling and Asynchronous Distributed Simulation: Analyzing Complex Systems

Abstract: From the Publisher: "Whether you are designing intelligent transportation systems or buffers in ATM switches, you will find key asynchronous distributed simulation techniques in this insightful book. These techniques will help revolutionize your large-scale systems? designs of today and tomorrow. Drawing on nearly 20 years of experience in modeling and simulation, the authors bring you the first book to present fundamental principles for asynchronous distributed simulation. Throughout Modeling and Asynchronous Distributed Simulation, you will explore a wealth of case studies that provide real-world approaches to a range of diverse technology disciplines.You will also discover essentials to improve your understanding of complex systems, including:*Determination of the simulation timestep*Analysis of accuracy for simulation results *Examination of how simulation results yield qualitative insights into complex system behavior *Generation of input stimuli*Future research trends in simulationThis valuable text offers systems designers, graduate students, and practicing computer science engineers both basic principles and complex concepts of modeling and asynchronous distributed simulation."

Modeling and simulation of a cycloconverter drive system for harmonic studies

Abstract: This paper discusses the steady-state harmonic modeling and simulation of a cycloconverter drive system (CDS). The operation and control of a cycloconverter drive and a synchronous motor load were modeled in the time domain. Based on the duality principle of electric and magnetic circuits, four types of three-phase transformer models with detailed nonlinear magnetizing characteristics were implemented. The harmonic behaviors of the entire system were studied under several worst operation conditions. The goal of this paper is to understand the harmonic problems associated with a CDS from an integrated system point of view, with special attention given to harmonic filtering and cancellation effect of converter coupling transformers.

Simulation Modeling for Watershed Management

Abstract: I History, Theory, and Challenges.- 1 Characterizing Watershed and Landscape Management.- 1.1 Locally Led Watershed Management.- 1.2 Management Committees and Plans.- 2 Challenges of Management and Modeling.- 2.1 Simulation Modeling Objectives.- 2.2 Which Comes First, Data or Models?.- 2.3 Making Modeling Cost Effective.- 2.4 How Formal Should a Model Be?.- 3 Perspectives in Ecological Modeling and Simulation.- 3.1 Underlying Theories of Ecology.- 3.2 Ecological Simulation Software.- 4 Perspectives in Hydrologic Modeling and Simulation.- 4.1 Simulation Models.- 4.2 The Role of Geographic Information Systems.- II Choosing Models and Modeling Environments.- 5 Questions Addressed with Single-Discipline Simulation Models.- 5.1 Questions Addressed by Surface Water Erosion and Pollution Models.- 5.2 River Management.- 5.3 Stream Management.- 5.4 Plant Community Succession.- 5.5 Urban Growth.- 6 Questions Addressed with Multidiscipline Simulation Models.- 6.1 Sample Model Integration Environments.- 6.2 Sample Multidisciplinary Models.- 7 Creating New Models.- 7.1 Geographic Information Systems-Based Approaches.- 7.2 Introductory Dynamic Modeling Software.- 7.3 Power Dynamic Modeling Software.- 8 Coordinating Large-Scale, Interdisciplinary Watershed Modeling.- 8.1 Modeling Steps.- 8.2 Management Concerns.- 8.3 Conclusions.- 9 Analyzing Alternatives.- 9.1 Decision Trade-Off Analysis.- 9.2 Approach to Analysis.- 9.3 Summary.- 10 Who Develops and Runs the Models?.- 11 Error and Uncertainty Analysis 107.- 11.1 Sources of Error.- 11.2 Tracking Error and Uncertainty.- 12 Model Evaluation Guidelines.- 12.1 Requirements Identification.- 12.2 Establishing Modeling Expectations.- 12.3 Model Selection Criteria.- 12.4 Modeling Environment Selection Criteria.- III An Integrated Watershed Modeling and Simulation Future.- 13 Approaches to Future Model Integration.- 13.1 Common User Interface.- 13.2 Scientific Models Integrated Behind Management Models.- 13.3 Scientific Models Converted to Management Model Modules.- 13.4 New Management Models.- 13.5 New Modeling Languages.- 14 Design Philosophies.- 14.1 Embrace Current Ecological, Economic, and Management Theories.- 14.2 Use Existing Code.- 14.3 Minimize the Number of Authors of Any Given Module.- 14.4 Embrace Legacy Software.- 14.5 Design Everything to Be Modular.- 14.6 Distributed.- 14.7 Multiple Interface Levels.- 14.8 Model Components as Objects.- 15 Watershed Manager's View.- 15.1 System Design Philosophy.- 15.2 Multiple Models.- 15.3 Model Modification.- 16 Modeler's View.- 16.1 Audience.- 16.2 Imagine.- 16.3 System Design Philosophy.- 16.4 Model Control Center.- 16.5 Subsystems.- 16.6 Viewers and Controllers.- 17 Programmer's View.- 17.1 Audience.- 17.2 System Design Philosophy.- 17.3 System Overview.- 17.4 Subsystem Encapsulation.- 17.5 Data Cache Objects and the Data Register.- 17.6 Simulation Timekeeper.- 17.7 Viewers and Controllers.- 17.8 Implementation Approaches.- Conclusions.- References.

Modeling scintillation from an aperiodic Kolmogorov phase screen

Abstract: We propose a technique for the accurate modeling and simulation of scintillation patterns that are due to Kolmogorov statistics without assuming periodic boundary conditions. We show how the more physically justifiable assumption of smoothness results in a propagation kernel of finite extent. This allows the phase screen dimensions for an accurate simulation to be determined, and truncation can then be used to eliminate the unwanted spectral leakage and diffraction effects usually inherent in the use of finite apertures. A detailed outline of the proposed technique and comparison of simulations with analytic results are presented.

Well-defined intended uses: an explicit requirement for accreditation of modeling and simulation applications

Abstract: A modeling and simulation (M&S) application is built for a specific purpose and its acceptability assessment is carried out with respect to that purpose The accreditation decision for an M&S application is also made with respect to that purpose The purpose is commonly expressed in terms of intended uses The quality of expressing the intended uses significantly affects the quality of the acceptability assessment as well as the quality of making the accreditation decision The purpose of this paper is to provide guidance in proper definition of the intended uses It uses an M&S application for simulating the US National Missile Defense (NMD) system design as an example to illustrate the definition of the intended uses

Qualitative simulation of large and complex genetic regulatory systems

Abstract: Modeling and simulation techniques developed within qualitative reasoning might be profitably used for the analysis of genetic regulatory systems. A major problem with current qualitative simulation techniques is their lack of upscalability. We describe a method that is able to deal with large and complex systems, and discuss its performance in simulation experiments with random regulatory networks.