File Name: principles of modeling and simulation .zip
- Modeling and simulation
- Testing and Validation of Computer Simulation Models
- Modeling and simulation
- Principles of modeling and simulation : a multidisciplinary approach
This model is similar to a real system, which helps the analyst predict the effect of changes to the system. Simulation of a system is the operation of a model in terms of time or space, which helps analyze the performance of an existing or a proposed system.
Modeling and simulation
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Sokolowski, Catherine M. Includes bibliographical references and index. ISBN cloth 1. Mathematical models. Simulation methods. Interdisciplinary research. Banks, Catherine M. S —dc22 Printed in the United States of America. Sokolowski Benjamin M. Mercury —Catherine M. What Is Modeling and Simulation? Reynolds, Jr. Queue Modeling and Simulation 58 69 69 69 71 Paul A.
Human Interaction with Simulations 71 72 74 79 81 87 88 89 89 89 91 Michael P. Bailey and Ahmed K. Verification and Validation 91 91 Mikel D. Uses of Simulation Tuncer I. Fontaine, David P. Cook, C. Donald Combs, John A. Sokolowski, and Catherine M. The Future of Simulation R. Bowen Loftin Introduction A Brief.
History of Simulation Convergent Simulations Serious Games Human-Simulator Interfaces Computing Technology The Role of Education in Simulation The Future of Simulation Key Terms Index Preface The impetus for this study is the realization that no textbook exists that provides an introduction to modeling and simulation suitable for multiple disciplines, especially those that are outside the science and engineering fields.
Many universities are realizing that modeling and simulation is becoming an important tool in solving and understanding numerous and diverse problems. They have begun to offer introductory courses in this field to acquaint their students with the foundational concepts that will help them apply modeling and simulation in many areas of research. This text serves to provide an orientation to the theory and applications of modeling and simulation from a multidisciplinary perspective. To students who will be reading this text we offer a concise look at the key concepts making up the field of modeling and simulation.
While modeling and simulation necessarily entails mathematical representations and computer programs, students need only be familiar with math at the college algebra level and the use of spreadsheets to understand the modeling and simulation concepts covered in this book.
The text is divided into three parts with nine chapters. Part Two, Theoretical Underpinnings, examines the most fundamental aspects of modeling and simulation. Part Three, Practical Domains, affords the student an opportunity to consider the many uses of modeling and simulation as a tool in workforce development.
Students will also review case studies and research conducted in various disciplines emphasizing the notion that models serve as approximations of real-world events. It provides answers to the questions: Is modeling and simulation a tool or discipline?
How should education, research, and training be conducted to support workforce development? Contributors Michael P. Catherine M. At the core of the discipline is the fundamental notion that models are approximations for the real-world. The model is then followed by simulation, which allows for the repeated observation of the model. After one or many simulations of the model, a third step takes place and that is analysis.
Analysis aids in the ability to draw conclusions, verify and validate the research, and make recommendations based on various iterations or simulations of the model. In , then Director Dr. Rita R. Colwell declared simulation as the third branch of science at the fall meeting of the American Geophysical Union . Sokolowski and Catherine M. Students study the basics of modeling as a way to understand the various modeling paradigms appropriate for conducting digital computer simulations.
They must understand simulation and the methodology, development, verification and validation, and design of simulation experiments. Students who are able to engage visualization are able to provide an overview of interactive, real-time 3D computer graphics and visual simulations using high-level development tools.
In other words, analysis also includes making known the limitations of the research. It was political scientist Herbert A. Simon — who introduced the notion of learning by doing also known as experiential learning. It is the simulation of a model that allows for the imitation of the operation of a real-world process or system over time. To imitate an operation over time one must generate a history, real or artificial, to draw inferences concerning the operating characteristics of the real system that is represented .
Simon was a political scientist who conducted research in a variety of disciplines including cognitive psychology, computer science, public administration, economics, management, and philosophy of science.
He was the first to analyze the architecture of complexity and to propose a preferential attachment mechanism to explain power law distributions. He introduced the notion of experiential learning, bounded rationality, and satisficing.
He remains one of the most influential social scientists of the 20th century. It can be a representation of an actual system.
It can be something used in lieu of the real thing to better understand a certain aspect about that thing. To produce a model you must abstract from reality a description of a vibrant system. The model can depict the system at some point of abstraction or at multiple levels of the abstraction with the goal of representing the system in a mathematically reliable fashion.
A simulation is an applied methodology that can describe the behavior of that system using either a mathematical model or a symbolic model . Simply, simulation is the imitation of the operation of a real-world process or system over a period of time . Models and the ability to act out with those models is a credible way of understanding the complexity and particulars of a real entity .
You will no doubt review the work schedule and note that the manager has scheduled more cashiers during peak times. You will then assess how many customers are processed during peak times based on the cashier tapes.
Also, you might want to see how long it takes to process a customer at slow periods and at heavy traffic periods—you might be surprised to find that customers are processed in shorter exchanges at busy times. Do the customers feel rushed? How many errors are made? Do the customer lines flow smoothly? Are the cash registers placed in good locations? All of this is part of the initial research you will do to develop your model.
Once you have sufficient data you can create your model. It is important to note that models are driven by data and so your data collection must be done with great accuracy. Once the model is created you can craft a fairly well-thought-out and credible hypothesis such as, if the store manager does this, this will be his result.
But are you certain? There may be unexpected changes to the model—a cashier is out sick, a cash register breaks, the power goes out and stops all transactions. What can the manager do to accommodate these unforeseen occurrences? In essence, you have the ability to repeat the testing of the hypothesis through various simulations. First, we must appreciate that defining simulation is not as clear-cut as defining model. The real system may not be engaged because 1 it may not be accessible, 2 it may be dangerous to engage the system, 3 it may be unacceptable to engage the system, or 4 the system may simply not exist.
Modeling depends on computational science for the visualization and simulation of complex, large-scale phenomena. These models may be used to replicate complex systems that exhibit chaotic behavior and so simulation must be used to provide a more detailed view of the system. Simulation also allows for virtual reality research whereby the analyst is immersed within the simulated world through the use of devices such as headmounted display, data gloves, freedom sensors, and forced-feedback elements .
Artificial Life and Computer Animation are offshoots of computational science that allow for additional variations in modeling.
Testing and Validation of Computer Simulation Models
Skip to content. All Homes Search Contact. Downey A. MATLAB based Simscape language, enabling text-based authoring of physical modeling components, domains, and libraries Physical units for parameters and variables, with all unit conversions handled automatically Ability to simulate models that include blocks from related physical modeling products without purchasing those products More Filters. This book is for people who have never programmed before. However, the LaTex source is also available and allows for customization and hyperlinking, and you can even create a version for an e-reader.
Modeling and simulation
Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. As part of system development, many industries, including the automobile industry, make substantial use of modeling and simulation to help understand system performance. Modeling and simulation sometimes referred to here as simulation are currently used for a number of applications in the Department of Defense, notably for training users of new systems and to help support arguments presented in the analysis of alternatives formerly cost and operational effectiveness analyses to justify going ahead with system development. Its success in similar applications in industry, and its cost, safety, and environmental advantages over operational testing, have raised interest in the use of modeling and simulation in operational testing and evaluation where it enjoys the same advantages.
Principles of modeling and simulation : a multidisciplinary approach
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C omputer modeling has grown over the years into a scientific discipline on its own. Models are utilized to assess real-world phenomena maybe too complex to be analyzed in the laboratory or under hypotheses at a fraction of the cost of undertaking the actual activities. Models in industry, government, and educational institutions shorten design cycles, reduce costs, and enhance knowledge. The properties of the materials should emerge from the numerical solution of these laws. The models here are actually a faithful representation of reality, but in a controlled environment.
Reservoir engineers today need to acquire more complex reservoir management and modeling skills. Principles of Applied Reservoir Simulation, Fourth Edition, continues to provide the fundamentals on these topics for both early and seasoned career engineers and researchers. Enhanced with more practicality and with a focus on more modern reservoir simulation workflows, this vital reference includes applications to not only traditional oil and gas reservoir problems but specialized applications in geomechanics, coal gas modelling, and unconventional resources.
In the computer application of modeling and simulation a computer is used to build a mathematical model which contains key parameters of the physical model. The mathematical model represents the physical model in virtual form, and conditions are applied that set up the experiment of interest.