Showing papers on "Modeling and simulation published in 2005"

Modeling and simulation of dielectric elastomer actuators

Abstract: Dielectric elastomers are used as base material for so-called electroactive polymer (EAP) actuators. A procedure and a specific constitutive model (for the acrylic elastomer VHB 4910) are presented in this work for finite element modeling and simulation of dielectric elastomer actuators of general shape and set-up. The Yeoh strain energy potential and the Prony series are used for describing the large strain time-dependent mechanical response of the dielectric elastomer. Material parameters were determined from uniaxial experiments (relaxation tests and tensile tests). Thereby the inverse problem was solved using iterative finite element calculations. A pre-strained circular actuator was built and activated with a predefined voltage. A three-dimensional finite element model of the circular actuator was created and the electromechanical activation process simulated. Simulation and actual measurements agree to a great extent, thus leading to a validation of both the constitutive model and the actuator simulation procedure proposed in this work.

Modeling, Simulation, and control of switched reluctance motor drives

Abstract: This paper presents the modeling, simulation, and control aspects of four-quadrant switched reluctance motor (SRM) drives. The design of SRM drive systems must be focused on application-based appropriate control and engineering solutions needed to overcome the practical issues. A complex model is described for the physical motor simulation to incorporate the important dynamics of the SRM. A simpler, but quite accurate, model is presented for the SRM controller. Various practical limitations have been incorporated in the simulation model to make it closer to the experimental setup. The SRM control parameters are chosen based on torque-per-ampere maximization requirement. Experimental results for a 1.0-kW SRM obtained on a digital platform are presented along with useful guidelines for prototype implementation.

Phase field modeling and simulation of three-phase flows

Abstract: Abstract. In this paper, we derive a thermodynamically consistent pha se-field model for flows containing three (or more) liquid components. The model is based on a Navier-S tokes (NS) and Cahn-Hilliard system (CH) which accounts for surface tension among the different component s a d three-phase contact lines. We develop a stable conservative, second order accurate fully implicit discre tization of the NS and three-phase (ternary) CH system. We use a nonlinear multigrid method to efficiently solve the dis crete ternary CH system at the implicit time-level and then couple it to a multigrid/projection method that is used to solve the NS equation. We demonstrate convergence of our scheme numerically and perform numerical simulation s to show the accuracy, flexibility, and robustness of this approach. In particular, we simulate a three interface contact angle resulting from a spreading liquid lens on an interface, a buoyancy-driven compound drop, and the Rayl eigh-Taylor instability of a flow with three partially miscible components.

J-Sim: A Simulation Environment for Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) have gained considerable attention in the past few years. As such, there has been an increasing need for defining and developing simulation frameworks for carrying out high-fidelity WSN simulation. In this paper, the authors presented a modeling and simulation framework for WSNs in J-Sim - an open-source, component-based compositional network simulation environment that is developed entirely in Java. This framework is built upon the autonomous component architecture (ACA) and the extensible internetworking framework (INET) of J-Sim, and provides an object-oriented definition of (i) target, sensor and sink nodes, (ii) sensor and wireless communication channels, and (iii) physical media such as seismic channels, mobility model and power model (both energy-producing and energy-consuming components). Application-specific models can be defined by sub-classing classes in the simulation framework and customizing their behaviors. The use of the proposed WSN simulation framework was demonstrated by implementing several well-known localization, geographic routing, and directed diffusion protocols. In addition, performance comparisons were performed (in terms of execution time incurred, and the memory used) in simulating several typical WSN scenarios in J-Sim and ns-2. The simulation study indicates that the proposed WSN simulation framework in J-Sim is much more scalable than ns-2 (especially in memory usage).

Integrated Heat, Air and Moisture Modeling and Simulation in Hamlab, Reference: A41-T3-NL-05-2

Abstract: This paper gives an overview of recent developments and results of a new integrated heat, air and moisture (HAM) modeling toolkit in Matlab named HAMLab. The recent developments include integration of a whole building model with building systems and controllers, 2D/3D HAM transport in constructions and 2D airflow respectively. The results include a short review on HAM models, a motivation of the selected simulation environment Matlab and extensive verification/ validation results. Furthermore, the integration capabilities are demonstrated by applications. It is concluded that the simulation environment HAMLab is capable of solving a large scale of integrated HAM models. Limitations, benefits and drawbacks are discussed.

Variable Structure in DEVS Component-Based Modeling and Simulation

Abstract: Variable structure refers to the ability of a system to dynamically change its structure according to different situations. It provides component-based modeling and simulation environments with powerful modeling capability and the flexibility to design and analyze complex systems. In this article, the authors discuss variable structure--specifically, the structure change and interface change capability--in DEVS-based modeling and simulation environments. The operations of structure change and interface change are discussed, and their respective operation boundaries are defined. Three examples are given to illustrate the role of variable structure and how it can be used to model and design adaptive complex systems. Principles for the implementation of variable structure are also presented and illustrated in the DEVSJAVA modeling and simulation environment.

Viptos: a graphical development and simulation environment for TinyOS-based wireless sensor networks

Abstract: We are announcing the first release of Viptos (Visual Ptolemy and TinyOS), an integrated graphical development and simulation environment for TinyOS-based wireless sensor networks. Viptos allows developers to create block and arrow diagrams to construct TinyOS programs from any standard library of nesC/TinyOS components. The tool automatically transforms the diagram into a nesC program that can be compiled and downloaded from within the graphical environment onto any TinyOS-supported target hardware. In particular, Viptos includes the full capabilities of VisualSense [1], which can model communication channels, networks, and non-TinyOS nodes. This release of Viptos is compatible with nesC 1.2 and includes tools to harvest existing TinyOS components and applications and convert them into a format that can be displayed as block (and arrow) diagrams and simulated.Viptos is based on TOSSIM and Ptolemy II. TOSSIM is an interrupt-level simulator for TinyOS programs. It runs actual TinyOS code but provides software replacements for the simulated hardware and models network interaction at the bit or packet level. Ptolemy II is a graphical software system for modeling, simulation, and design of concurrent, real-time, embedded systems. Ptolemy II focuses on assembly of concurrent components with well-defined models of computation that govern the interaction between components. VisualSense is a Ptolemy II environment for modeling and simulation of wireless sensor networks at the network level.Viptos provides a bridge between VisualSense and TOSSIM by providing interrupt-level simulation of actual TinyOS programs, with packet-level simulation of the network, while allowing the developer to use other models of computation available in Ptolemy II for modeling various parts of the system. While TOSSIM only allows simulation of homogeneous networks where each node runs the same program, Viptos supports simulation of heterogeneous networks where each node may run a different program. Viptos simulations may also include non-TinyOS-based wireless nodes. The developer can easily switch to different channel models and change other parts of the simulated environment, such as creating models to generate simulated traffic on the wireless network.Viptos inherits the actor-oriented modeling environment of Ptolemy II, which allows the developer to use different models of computation at each level of simulation. At the lowest level, Viptos uses the discrete-event scheduler of TOSSIM to model the interaction between the CPU and TinyOS code that runs on it. At the next highest level, Viptos uses the discrete-event scheduler of Ptolemy II to model interaction with mote hardware, such as the radio and sensors. This level is then embedded within VisualSense to allow modeling of the wireless channels to simulate packet loss, corruption, delay, etc. The user can also model and simulate other aspects of the physical environment including those detected by the sensors (e.g., light, temperature, etc.), terrain, etc.At IPSN in April 2005, we demonstrated a pre-release developmental version of Viptos with two simple applications. The first was a single node sensing application that displayed the value of the light sensor on the LEDs. The second was a two node send and receive application that transmitted the value of the light sensor on the first node to the second node. This release version of Viptos supports more sophisticated applications, such as multi-node routing, and demonstrates some of the more advanced features described in this abstract.

The OpenModelica Modeling, Simulation, and Development Environment

Abstract: Modelica is a modern, strongly typed, declarative, and object-oriented language for modeling and simulation of complex systems. This paper gives a quick overview of some aspects of the OpenModelica environment – an open-source environment for modeling, simulation, and development of Modelica applications. An introduction of the objectives of the environment is given, an overview of the architecture is outlined and a number of examples are illustrated.

Model continuity in the design of dynamic distributed real-time systems

Abstract: Model continuity refers to the ability to transition as much as possible a model specification through the stages of a development process. In this paper, the authors show how a modeling and simulation environment, based on the discrete event system specification formalism, can support model continuity in the design of dynamic distributed real-time systems. In designing such systems, the authors restrict such continuity to the models that implement the system's real-time control and dynamic reconfiguration. The proposed methodology supports systematic modeling of dynamic systems and adopts simulation-based tests for distributed real-time software. Model continuity is emphasized during the entire process of software development $the control models of a dynamic distributed real-time system can be designed, analyzed, and tested by simulation methods, and then smoothly transitioned from simulation to distributed execution. A dynamic team formation distributed robotic system is presented as an example to show how model continuity methodology effectively manages the complexity of developing and testing the control software for this system.

Electric ship power system integration analyses through modeling and simulation

Abstract: The Center for Electromechanics (CEM) at the University of Texas is engaged in the development of a comprehensive power system model in order to address several challenging issues facing the development of a viable and effective integrated power system architecture for future naval platforms. The power system under consideration reflects the notional DD power system architecture and is developed in the Matlab/Simulink environment. System components such as motors and generators are modeled using parameters based on actual machine design and analysis work performed at CEM. Simulation results of models including permanent-magnet propulsion motors and generators with simple reconfiguration scenarios simulating loss and recovery of power to propulsion and vital loads are presented.

A Simulink-based microturbine model for distributed generation studies

Abstract: This paper presents the modeling and simulation of a microturbine generation system suitable for isolated as well as grid-connected operation. The system comprises of a permanent magnet synchronous generator driven by a microturbine. A brief description of the overall system is given, and mathematical models for the microturbine and permanent magnet synchronous generator are presented. Also, the use of power electronics in conditioning the power output of the generating system is demonstrated. Simulation studies have been carried out in MATLAB/Simulink under different load conditions.

Discrete event multi-level models for systems biology

Abstract: Diverse modeling and simulation methods are being applied in the area of Systems Biology. Most models in Systems Biology can easily be located within the space that is spanned by three dimensions of modeling: continuous and discrete; quantitative and qualitative; stochastic and deterministic. These dimensions are not entirely independent nor are they exclusive. Many modeling approaches are hybrid as they combine continuous and discrete, quantitative and qualitative, stochastic and deterministic aspects. Another important aspect for the distinction of modeling approaches is at which level a model describes a system: is it at the “macro” level, at the “micro” level, or at multiple levels of organization. Although multi-level models can be located anywhere in the space spanned by the three dimensions of modeling and simulation, clustering tendencies can be observed whose implications are discussed and illustrated by moving from a continuous, deterministic quantitative macro model to a stochastic discrete-event semi-quantitative multi-level model.

Extending SystemC to support mixed discrete-continuous system modeling and simulation

Abstract: Systems on chip are more and more heterogeneous and include software, analog/RF and digital hardware, and non-electronic components, such as sensors or actuators. The design and verification of such systems require appropriate modeling means to deal with the increasing complexity and to achieve efficient simulation. SystemC, a set of C++ classes and methods, provides a modeling and simulation framework that supports digital (discrete) hardware and software systems from abstract specifications to register transfer level models. We propose a way to extend the capabilities of SystemC to support mixed discrete-continuous systems by implementing a synchronous dataflow (SDF) model of computation (MoC). The SDF MoC is used to embed continuous-time behavior in SDF modules and to support synchronization with the existing SystemC kernel. The paper presents an overview of the architecture and the syntax of the proposed extensions and gives modeling examples with simulation results.

Modeling and simulation of broad-band antennas using the time-domain finite element method

Abstract: This paper presents a time-domain finite element method (TDFEM) for the modeling and simulation of complex broad-band antennas. Two critical components, namely the modeling of antenna feeds and the truncation of infinite free space, are discussed in detail. An accurate waveguide port boundary condition is employed to model the commonly used coaxial feeds, which is also applicable to other waveguide feeding structures. An improvement to the simplified probe feed model is also presented. The formulation and implementation of perfectly matched layers are briefly described for the truncation of the TDFEM computational domain. Numerical examples are presented to demonstrate the modeling and simulation of typical broad-band antennas, which include a logarithmic spiral antenna, a Vivaldi antenna, and a Vlasov antenna.

DEVStone: a benchmarking technique for studying performance of DEVS modeling and simulation environments

Abstract: DEVS (Discrete EVents systems Specification) is a sound, formal modeling and simulation (M&S) framework that supports hierarchical, modular model composition DEVS-based M&S environments have been used successfully to understand, analyze, and develop a wide variety of systems As the systems under study become larger and more complex, the performance of the simulator becomes critical Nevertheless, evaluating the performance of such simulators is a complex process that requires the execution of large numbers of models with different characteristics We present DEVStone, a synthetic benchmark devoted to automate the evaluation of DEVS-based simulation approaches, which generates models similar to those existing in the real world DEVStone facilitates performance analysis for successive versions (eg, upgrades or fixes) of the same simulation engine, and provides a common metric to compare different M&S environments

Description of a modeling, simulation, animation, and real-time control (MoSART) environment for a class of electromechanical systems

Abstract: This paper describes an Interactive Modeling, Simulation, Animation, and Real-Time Control (MoSART) Environment that is useful for controls education and research. The described MoSART environment is shown to be useful for analyzing, designing, visualizing, and evaluating control systems for a class of "cart-pendulum" electromechanical systems. The environment-referred to as Cart-Pendulum Control3D-Lab-is based on Microsoft Windows, Visual C++, Direct-3D, and MATLAB/Simulink. The environment can be used as a stand-alone application or together with MATLAB, Simulink, and toolboxes. When used as a stand-alone application, a friendly graphical user interface permits easy interaction. Users may select (via pull-down menus) systems, dynamical models, control laws, exogenous signals (including joystick inputs) and associated parameters, initial conditions, integration routines, and associated parameters. When used with MATLAB, Simulink, and toolboxes, the previously mentioned nominal features are significantly enhanced. In either case, the interface permits users to access the following (via pull-down menus): animation models, mesh properties, texture and lighting models, system-specific visual indicators, graphics to be displayed, animation/data display/storage rates, simulation control buttons, and extensive documentation. When Simulink is present, users can exploit extensive visualization and three-dimensional (3-D) animation features through provided and/or user-generated Simulink diagrams. This capability makes the developed environment very extensible with respect to mathematical models and control laws. In addition, users may readily export simulation data to MATLAB/toolboxes for postprocessing and further analysis. The environment also contains a suite of well-documented (easy-to-modify) models and control laws that are implemented within the provided Simulink block diagrams. Provided (special) blocks enable animation, joystick inputs, and (near) real-time simulation and animation (when possible). (Near real-time-or faster-than-real-time-simulation and animation are possible whenever the mathematical and animation models are sufficiently simple and data manipulation requirements, e.g. storage and display, are sufficiently mild. For the systems considered, (near) real-time simulation and animation is readily achievable.) Associated with each block diagram are system-specific, menu-accessed m-files that permit detailed analysis and design. A hardware module permits real-time control of actual hardware experiments. The developed environment is shown to be a valuable tool for enhancing both controls education in a variety of classes as well as research. Examples are presented to illustrate the utility of the environment.

Performance and Reliability Analysis of Wind Turbines Using Monte Carlo Methods Based on System Transport Theory

Abstract: Reliability and performance assessments of wind turbine systems are particularly challenging as they operate in highly stochastic, non-linear, coupled, multidisciplinary environments. The traditional approach has been to decouple performance from reliability and analyze them separately, which results in sub-optimal design and operational practices. In this paper, a method of jointly simulating both the performance and reliability of wind turbines is presented. The approach is based on system simulation using novel Monte Carlo algorithms derived from system transport theory (SPAR technology), a method originally developed for nuclear physics applications. In the representative wind turbine case study discussed in this paper, both machine availability and energy produced is simulated as a function of basic weather variables like wind speeds, turbulence intensity and design intent. In addition, statistical confidence bounds on energy and availability are also calculated for a full twenty year life. INTRODUCTION & OVERVIEW Wind Turbine systems are rapidly becoming an economically viable source of renewable energy. A key element in making wind energy both a technical and commercial success is the ability to develop accurate and computationally efficient modeling and simulation platforms which serve as the basis for machine design and performance optimization. Two key elements of wind turbine technology are turbine performance and availability. Turbine performance (energy produced) is a function of design variables and a highly stochastic operating environment. Machine availability is a function of system reliability, and is impacted by design, operating environment and maintenance considerations. Hence, the wind turbine simulation problem includes elements of probabilistic design, multi-state reliability theory, multidisciplinary optimization as well as traditional fields like engineering and operations research. Hence, any modeling framework will have to include elements of all these subjects. In recent years, researchers have recognized the benefits of incorporating both reliability and performance in a unified mathematical model, giving rise to the emerging field of “performability” analysis [Trivedi, 2001]. For wind turbines, “performability” analysis has applications in developing design specifications, in choosing wind farm sites, establishing maintenance and logistics protocols and in modeling power performance and equipment availability guarantees. This paper deals with a wind turbine case study analyzed using a new, unified approach to the wind turbine “performability” problem; and is based on a Monte Carlo approach derived from system transport theory of nuclear physics [Dubi, 2000]. The full paper will include a detailed description of system transport theory as applied to the reliability analysis of mechanical systems along with numerical implementation. In this extended abstract, a brief description of the theory is provided in subsequent sections. MULTI-STATE RELIABILITY ANALYSIS USING SYSTEM TRANSPORT THEORY Historically, reliability theory has been based on a binary approach, where a system can exist in two states – an “up” state where the system is completely operational and working at full performance; and a “down” state where the system has failed. The probability of a system existing in the “up” state is characterized by the reliability, R(t), which is the probability of the system being operational at time ‘t’, as well as system availability, A(t|k), which is the probability of the system being operational at time ‘t’ given that it has seen ‘k’ failures in the past. It is clear that R(t) refers to system survival before the first failure, and A(t|k) refers to system survival for repairable systems, i.e. R(t) is the special case of A(t|0). In reality, complex systems exist in multiple degraded states, which is studied under the emerging discipline of Multi-State reliability theory [Lisnianski, 2003]. There are two main approaches for modeling multistate problems for systems with non-exponential failure and repair distributions, (E.g. most mechanical systems) – Markovian Models, and a more general approach, which is System Transport Theory. Variations of Markov approaches include Semi-Markov or Generalized Markov theory [Bolch, et al, 1998]. Markov-based approaches work best when the failure and repair rates Copyright 2004 by S. Vittal (MemberAIAA) and M. Teboul. Published by the American Institute for Aeronautics & Astronautics with permission

Modeling, simulation, and testing of the mechanical dynamics of an RF MEMS switch

Abstract: Mechanical dynamics can be a determining factor for the switching speed of radio-frequency microelectromechanical systems (RF MEMS) switches. This paper presents the simulation of the mechanical motion of a microswitch under actuation. The switch has a plate suspended by springs. When an electrostatic actuation is applied, the plate moves toward the substrate and closes the switch. Simulations are calculated via a high-fidelity finite element model that couples solid dynamics with electrostatic actuation. It incorporates non-linear coupled dynamics and accommodates fabrication variations. Experimental modal analysis gives results in the frequency domain that verifies the natural frequencies and mode shapes predicted by the model. An effective 1D model is created and used to calculate an actuation voltage waveform that minimizes switch velocity at closure. In the experiment, the switch is actuated with this actuation voltage, and the displacements of the switch at various points are measured using a laser Doppler velocimeter through a microscope. The experiments are repeated on several switches from different batches. The experimental results verify the model.

Agent Directed HLA Simulation for Complex Supply Chain Modeling

Abstract: The authors outline a new modeling methodology designed to simulate a complex logistics network and to ensure interoperability through application of Intelligent Agent High-Level Architecture for distributed supply chain management.

The modeling and simulation of a permanent magnet synchronous motor with direct torque control based on Matlab/Simulink

Abstract: This paper introduces the modeling of the direct torque control (DTC) system of permanent magnet synchronous motor based on Matlab/Simulink. The process of the building simulation system is discussed in detail. Simulation results are presented to help understand the system performance and the influence of PI controller parameters on it. The relationship between torque hysteresis loop's width and torque ripple amplitude is analyzed and the result shows that the torque ripple can be reduced by narrowing the torque hysteresis loop's width. The simulation also demonstrates the PI controller parameters Kp and Ki should be properly matched to achieve high system performance. The simulation results and user-friendly graphic user interface proved that Matlab /Simulink is an effective tool to simulate and analyse a motor drive system

Information Fusion for Simulation Based Decision Support in manufacturing

Abstract: Robust and informed decisions are important for the efficient and effective operation of installed production facilities. The paper discusses Information fusion (IF) including a generic model for IF, and situations for decision-making. The paper also discusses current and future use of manufacturing resource simulation for design/configuration, operational planning and scheduling, and service and maintenance of manufacturing systems. Many of these applications use IF in some way, as is explained in more detail for simulation based service and maintenance. An extension of the generic model for IF is presented which incorporates modeling and simulation, and active databases as used in a simulation based service and maintenance system at the authors’ laboratory.

Modeling and simulation for RF system design

Abstract: Preface. Acknowledgments. 1 Introduction. 2 Design Flow Overview. 2.1 Design Levels. 2.2 Top-down System Design. 2.3 Bottom-up Verification. 3 Simulation Tools in System Design. 3.1 Use of Simulation Tools within the Design Flow. 3.2 Specific Simulation Algorithms of RF Simulators. 3.3 Criteria of the Simulator Selection. 3.4 Internet Resources for Simulation Tools. 4 System Level Modeling. 4.1 System Level Simulation. 4.2 Simulation Technology of System Level Simulators. 4.3 Complex Baseband Simulation. 4.4 Model Libraries for System Simulation. 4.5 Creation of Own Primitive and Hierarchical Models. 5 VHDL-AMS for Block Level Simulation. 5.1 Introduction. 5.2 VHDL-AMS Standardization. 5.3 A Simple Block Level Example - Analog PLL. 5.4 Summary 6 Introduction to VHDL-AMS. 6.1 Aim of this Introduction. 6.2 Repetition of Basics of VHDL 1076-1993. 6.3 Conservative Systems Description. 6.4 Description of Nonconservative Systems. 6.5 Mixed-Signal Simulation. 6.6 Analysis Domains. 6.7 Summary. 7 Selected RF Blocks in VHDL-AMS. 7.1 Library Overview. 7.2 Signal Sources. 7.3 Basic RF Building Blocks. 7.4 Measurement and Observation Units. 7.5 Block Level Example of a Linear PLL. 8 Macromodeling in VHDL-AMS. 8.1 Introduction. 8.2 General Methodology. 8.3 Input and Output Stages. 8.3.1 Input stages. 8.3.2 Output stages. 8.4 OpAmp Macromodel. 9 Complex Example: WLAN Receiver. 9.1 Introduction. 9.2 Example Specification. 9.3 Example Modeling. 9.4 Example Calibration. 9.5 Example Verification. 10 Modeling of Analog Blocks in Verilog-A. 10.1 Introduction. 10.2 Writing Custom Behavioral Models. 10.3 Overview of the Cadence Model Library rfLib. 10.4 Modeling and Simulation of a WLAN Receiver. 11 Characterization forBottom-Up Verification. 11.1 Concept of Characterization. 11.2 RF Characteristics and Parameters. 11.3 Application of Characterization. 11.4 Example Characterization of an LNA. 11.5 Characterization Environment. 11.6 Characterization Using the OCEAN Script Language. 12 Advanced Methods for Overall System Specification and Validation. 12.1 Gap between System Level and Block Level Simulation. 12.2 File Coupling of Simulators. 12.3 Direct Cosimulation of System Level and Analog Simulators. 12.4 Generated Black Box Models. References. Index.

Modeling and simulation in security evaluation

Abstract: Digital computers' earliest applications evaluated models of physical systems to predict their behavior under controlled conditions. To do this, they used simulation, computing changes to the models' state variables as a function of time. Since then, simulation has become fundamental to computer science. Developments in security have their roots elsewhere, but points of contact are increasing between security and simulation, particularly in several security evaluation areas, including: 1) impact assessment for determining how security measures affect system and application performance; 2) emulation, in which real and virtual worlds are combined to study the interaction between malware and systems, and probe for new system weaknesses; 3) cyberattack exercises and training scenarios; and 4) risk assessment based on known vulnerabilities, exploits, attack capabilities, and system configuration.