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Advances in computational power have facilitated the development of simulations unprecedented in their computational size, scope of technical issues, spatial and temporal resolution, complexity and comprehensiveness. As a result, complex structures from airplanes to bridges can be almost completely based on model-based simulations. This book gives
This easy to read text provides a broad introduction to the fundamental concepts of modeling and simulation (M&S) and systems engineering, highlighting how M&S is used across the entire systems engineering lifecycle. Features: reviews the full breadth of technologies, methodologies and uses of M&S, rather than just focusing on a specific aspect of the field; presents contributions from specialists in each topic covered; introduces the foundational elements and processes that serve as the groundwork for understanding M&S; explores common methods and methodologies used in M&S; discusses how best to design and execute experiments, covering the use of Monte Carlo techniques, surrogate modeling and distributed simulation; explores the use of M&S throughout the systems development lifecycle, describing a number of methods, techniques, and tools available to support systems engineering processes; provides a selection of case studies illustrating the use of M&S in systems engineering across a variety of domains.
Model Engineering for Simulation provides a systematic introduction to the implementation of generic, normalized and quantifiable modeling and simulation using DEVS formalism. It describes key technologies relating to model lifecycle management, including model description languages, complexity analysis, model management, service-oriented model composition, quantitative measurement of model credibility, and model validation and verification. The book clearly demonstrates how to construct computationally efficient, object-oriented simulations of DEVS models on parallel and distributed environments. - Guides systems and control engineers in the practical creation and delivery of simulation models using DEVS formalism - Provides practical methods to improve credibility of models and manage the model lifecycle - Helps readers gain an overall understanding of model lifecycle management and analysis - Supported by an online ancillary package that includes an instructors and student solutions manual
The capability modeling and simulation (M&S) supplies for managing systems complexity and investigating systems behaviors has made it a central activity in the development of new and existing systems. However, a handbook that provides established M&S practices has not been available. Until now. Modeling and Simulation-Based Systems Engineering Handbook details the M&S practices for supporting systems engineering in diverse domains. It discusses how you can identify systems engineering needs and adapt these practices to suit specific application domains, thus avoiding redefining practices from scratch. Although M&S practices are used and embedded within individual disciplines, they are often developed in isolation. However, they address recurring problems common to all disciplines. The editors of this book tackled the challenge by recruiting key representatives from several communities, harmonizing the different perspectives derived from individual backgrounds, and lining them up with the book’s vision. The result is a collection of M&S systems engineering examples that offer an initial means for cross-domain capitalization of the knowledge, methodologies, and technologies developed in several communities. These examples provide the pros and cons of the methods and techniques available, lessons learned, and pitfalls to avoid. As our society moves further in the information era, knowledge and M&S capabilities become key enablers for the engineering of complex systems and systems of systems. Therefore, knowledge and M&S methodologies and technologies become valuable output in an engineering activity, and their cross-domain capitalization is key to further advance the future practices in systems engineering. This book collates information across disciplines to provide you with the tools to more efficiently design and manage complex systems that achieve their goals.
Data Engineering has become a necessary and critical activity for business, engineering, and scientific organizations as the move to service oriented architecture and web services moves into full swing. Notably, the US Department of Defense is mandating that all of its agencies and contractors assume a defining presence on the Net-centric Global Information Grid. This book provides the first practical approach to data engineering and modeling, which supports interoperabililty with consumers of the data in a service- oriented architectures (SOAs). Although XML (eXtensible Modeling Language) is the lingua franca for such interoperability, it is not sufficient on its own. The approach in this book addresses critical objectives such as creating a single representation for multiple applications, designing models capable of supporting dynamic processes, and harmonizing legacy data models for web-based co-existence. The approach is based on the System Entity Structure (SES) which is a well-defined structure, methodology, and practical tool with all of the functionality of UML (Unified Modeling Language) and few of the drawbacks. The SES originated in the formal representation of hierarchical simulation models. So it provides an axiomatic formalism that enables automating the development of XML dtds and schemas, composition and decomposition of large data models, and analysis of commonality among structures. Zeigler and Hammond include a range of features to benefit their readers. Natural language, graphical and XML forms of SES specification are employed to allow mapping of legacy meta-data. Real world examples and case studies provide insight into data engineering and test evaluation in various application domains. Comparative information is provided on concepts of ontologies, modeling and simulation, introductory linguistic background, and support options enable programmers to work with advanced tools in the area. The website of the Arizona Center for Integrative Modeling and Simulation, co-founded by Zeigler in 2001, provides links to downloadable software to accompany the book. - The only practical guide to integrating XML and web services in data engineering - Introduces linguistic levels of interoperability for effective information exchange - Covers the interoperability standards mandated by national and international agencies - Complements Zeigler's classic THEORY OF MODELING AND SIMULATION
This book provides a guide for systems engineering modeling and design. It focuses on the design life cycle with tools and application-based examples of how to design a system, focusing on incorporating systems principles and tools to ensure system integration. It provides product-based and service system examples to understand the models, tools, and activities to be applied to design and implement a system. The first section explains systems principles, models, and architecture for systems engineering, lifecycle models, and the systems architecture. Further sections explain systems design, development, and deployment life cycle with applications and tools and advanced systems engineering topics. Features: Focuses on model-based systems engineering and describes the architecture of the systems design models. Uses real-world examples to corroborate different and disparate systems engineering activities. Describes and applies the Vee systems engineering design methodology, with cohesive examples and applications of designing systems. Discusses culture change and the skills people need to design and integrate systems. Shows detailed and cohesive examples of the systems engineering tools throughout the systems engineering life cycle. This book is aimed at graduate students and researchers in systems engineering, modeling and simulation, any major engineering discipline, industrial engineering, and technology.
This book describes the latest research developments in modeling and simulation in industrial engineering. Topics such as decision and performance analysis and industrial control systems are described. Case studies in industry and services as well as engineering economy and cost estimation are also covered.
This invaluable text/reference reviews the state of the art in simulation-based approaches across a wide range of different disciplines, and provides evidence of using simulation-based approaches to advance these disciplines. Highlighting the benefits that simulation can bring to any field, the volume presents case studies by the leading experts from such diverse domains as the life sciences, engineering, architecture, arts, and social sciences. Topics and features: includes review questions at the end of every chapter; provides a broad overview of the evolution of the concept of simulation, stressing its importance across numerous sectors and disciplines; addresses the role of simulation in engineering design, and emphasizes the benefits of integrating simulation into the systems engineering paradigm; explains the relation of simulation with Cyber-Physical Systems and the Internet of Things, and describes a simulation infrastructure for complex adaptive systems; investigates how simulation is used in the Software Design Life Cycle to assess complex solutions, and examines the use of simulation in architectural design; reviews the function and purpose of simulation within the context of the scientific method, and its contribution to healthcare and health education training; discusses the position of simulation in research in the social sciences, and describes the simulation of service systems for simulation-based enterprise management; describes the role of simulation in learning and education, as well as in in military training. With its near-exhaustive coverage of disciplines, this comprehensive collection is essential reading for all researchers, practitioners and students seeking insights into the use of various modeling paradigms and the need for robust simulation infrastructure to advance their field into a computational future.