Introduction To Modeling And Simulation PDF

Modeling and Simulation  MODULE 1 INTRODUCTION TO MODELING AND SIMULATION  SUBTOPIC 1 BASIC CONCEPTS ON MODELING AND SIMULATION Demonstrate understanding of the basic concepts on modeling and simulation. What is Modeling and Simulation (M & S)? M & S is a problem-based...

Modeling and Simulation  MODULE 1 INTRODUCTION TO MODELING AND SIMULATION  SUBTOPIC 1 BASIC CONCEPTS ON MODELING AND SIMULATION Demonstrate understanding of the basic concepts on modeling and simulation. What is Modeling and Simulation (M & S)? M & S is a problem-based discipline that allows for repeated testing of a hypothesis. The foundation of an M & S program of study is its curriculum built upon four precepts: Modeling Simulation Visualization Analysis Source: Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, Sokolowski and Banks, 2010  What is a system? A construct or collection of different elements that together produces results not obtainable by the elements alone. The elements can include people, hardware, software, facilities, policies and documents – all things required to produce system-level qualities, properties, characteristics, functions, behavior an performance. (International Council of Systems Engineering (INCOSE)). An aggregation of objects joined in some regular interaction or interdependence for achievement of a common goal. An organized set of interrelated ideas or principles. A collection of entities (people, parts, messages, machines, servers, …) that act and interact together toward some end. Sources: Modeling and Simulation: Singh & Singh Modeling and Analysis: Schmidt and Taylor, 1970 Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, Sokolowski and Banks, 2010 Types of system 1. Physical System – Something that already exist Types of system 2. Notional System – A plan or concept for something physical that does not exist.  Principal concepts in M & S 1. System It refers to the subject of model development. It is the subject or thing that will be investigated or studied using M & S. Types of systems a) Discrete – state variables (variables that completely describe a system at any given moment in time) change instantaneously at separate points in time. b) Continuous – state variables change continuously with respect to time. Source: Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, Sokolowski and Banks, 2010  Principal concepts in M & S Ways to study a system a) The actual system vs. a model of the system b) A physical vs. mathematical representation c) Analytic solution vs. simulation solution Primary concerns of the modeler in the study of systems a) The quantitative analysis of the systems b) The techniques for system design, control or use c) The measurement or evaluation of the system performance Source: Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, Sokolowski and Banks, 2010  Principal concepts in M & S 2. Model A physical, mathematical or otherwise logical representation of a system, entity, phenomenon or process. It serves as representations of events and/or things that are real (such as historic case study) or contrived (a use case). Examples: Electromagnetic field model Modulating sound with acoustic metafiber bundles Source: Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, Sokolowski and Banks, 2010  Principal concepts in M & S Types of models a) Physical b) Notional  Principal concepts in M & S 3. Simulation An applied methodology that can describe the behavior of that system using either a mathematical model or a symbolic model. It can be the imitation of the operation of a real-world process or system over a period of time. Examples: Software Simulation Tabular and Graphic Simulations Source: Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, Sokolowski and Banks, 2010  Principal concepts in M & S 4. M & S The overall process of developing a model and then simulating that model to gather data concerning performance of a system. It used models and simulations to develop data as a basis for making managerial, technical and training decisions. Steps: a) Developing computer simulation or a design based on a model of an actual or theoretical physical system. b) Executing that model on a digital computer. c) Analyzing the output. Source: Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, Sokolowski and Banks, 2010 Components of a System 1. Entity → It is used to denote an object of interest in a system. 2. Attribute → It is used to denote a property of an entity. 3. Activity → Process that causes changes in the system State of the System - Collection of all the entities, attributes and activities at one point of time. Source: Modeling and Simulation: Singh & Singh Ways to Study a System Examples of System Source: Modeling and Simulation: Singh & Singh System Environment - It is where changes occurring outside the system are said to happen. Types of Activities 1. Endogenous → Activities that occur within the system 2. Exogenous → Activities that occur outside the system 3. Deterministic → Activities wherein the outputs can be described completely in terms of its input 4. Stochastic → Activities wherein the outputs cannot be described in terms of its input or outputs Source: Modeling and Simulation: Singh & Singh Examples: Source: Modeling and Simulation: Singh & Singh Types of System 1. Open A system which has exogenous activities. 2. Closed A system which don’t have any exogenous activities. 3. Continuous A system in which the changes are predominantly smooth. (Ex: Aircraft system) 4. Discrete A system in which the changes are predominantly discontinuous. (Ex: Factory system) 5. Sampled-data A system which is intrinsically continuous but information Source: Modeling and Simulation: Singh & Singh about them is only available at discrete points in time. Why model a system? 1) The system might not be accessible. 2) The system might be dangerous to engage. 3) The system might be unacceptable to engage. 4) The system might simply not exist. Subtasks in Deriving a Model 1. Establishing the model structure is possible by: a. Determining the system boundary b. Identifying the entities, attributes and activities of the system. 2. Supplying the data means a. Provides the values of attributes b. Defines the relationships involved in the activities.  Types of Models Models Physical Mathematical Static Dynamic Static Dynamic Numerical Analytical Numerical Technique Technique Technique System simulation  Definition of Modeling (Modelling) It is the process of representing a model which includes its construction and working. It is the process of creating a model which represents a system including their properties. It is an act of building a model. NOTE: This model is similar to a real system, which helps the analyst predict the effect of changes to the system. In other words, modelling Source: www.tutorialspoint.com Principles used in Modeling 1. Block Building A system can be described in series of blocks. Each block describe a part of the system. It depends upon few input variables and results in a few output variables. The system can be described as interconnection between the blocks. The system can be represented graphically as a simple block diagram. 2. Relevance The model should only include relevant aspect of the system. 3. Accuracy The information gathered for the model must be correct and accurate. 4. Aggregation It is the extend to which the number of individual entities can be grouped together into larger entities. What is Simulation? It is the process of imitating the operations of a facility or process, usually via computer. (Source: Law: Simulation Modeling and Analysis) o What’s being simulated is the system. o To study system, often make assumptions/approximations, both logical and mathematical, about how it works. o These assumptions form a model of the system. o If model structure is simple enough, could use mathematical methods to get exact information on questions of interest — analytical solution. It is the operation of a model in terms of time or space, which helps analyze the performance of an existing or a proposed system. In other words, simulation is the process of using a model to study the performance of a system. It is an act of using a model for simulation. (Source: www.tutorialspoint.com) History of Simulation 1940 − A method named ‘Monte Carlo’ was developed by researchers (John von Neumann, Stanislaw Ulan, Edward Teller, Herman Kahn) and physicists working on a Manhattan project to study neutron scattering. 1960 − The first special-purpose simulation languages were developed, such as SIMSCRIPT by Harry Markowitz at the RAND Corporation. 1970 − During this period, research was initiated on mathematical foundations of simulation. 1980 − During this period, PC-based simulation software, graphical user interfaces and object-oriented programming were developed. 1990 − During this period, web-based simulation, fancy animated graphics, simulation-based optimization, Markov-chain Monte Carlo methods were developed. (Source: www.tutorialspoint.com) Some (not all) application areas for simulation: Designing and analyzing manufacturing systems Evaluating military weapons systems or their logistics requirements Determining hardware requirements or protocols for communications networks Determining hardware and software requirements for a computer system Designing and operating transportation systems such as airports, freeways, ports, and subways Evaluating designs for service organizations such as call centers, fast-food restaurants, hospitals, and post offices Reengineering of business processes Determining ordering policies for an inventory system Analyzing financial or economic systems Source: Law: Simulation Modeling and Analysis The Process of Simulation Classification of Simulation Models Static vs. dynamic Deterministic vs. stochastic Continuous vs. discrete Most operational models are dynamic, stochastic, and discrete – will be called discrete-event simulation models Other Types of Simulation 1.Hybrid Simulation 2.Real-time Simulation 3.Web-based Simulation System Simulation The technique of solving problems by the observation of the performance, over time, of a dynamic model of the system. Analytical Method Simulation Method Used when models are Used when systems are too complex simple and easy to to allow realistic models to be understand. evaluated analytically. Mathematical methods Computers are used to evaluate the (such as algebra, calculus model numerically and data are or probability theory) are gathered in order to estimate the used to get exact desired true properties. information needed to solve the problem. System Simulation Analytical Method Simulation Method 1. Gives general solution. 1. Gives specific solutions. 2. It considers all the conditions to solve a 2. Tells only about particular conditions. problem. 3. There is limited problem that can be 3. It is an extension of mathematical solution. solved mathematically. 4. Analytical results occur in the form of 4. Provides a quicker or more convenient way complex series or integrals that still of deriving results. require extensive evaluation. 5. Expensive and time consuming. 5. Gives results in few minutes at a very low 6. There are many simple limitations such cost. as physical stops, finite time delays or 6. Easily removes limitations such as physical nonlinear forces. stops. 7. Employs the deductive reasoning of 7. Employs numerical methods. mathematics to solve the model. Advantages of Modeling and Simulation ❑ Easy to understand − Allows to understand how the system really operates without working on real-time systems. ❑ Easy to test − Allows to make changes into the system and their effect on the output without working on real-time systems. ❑ Easy to upgrade − Allows to determine the system requirements by applying different configurations. ❑ Easy to identifying constraints − Allows to perform bottleneck analysis that causes delay in the work process, information, etc. ❑ Easy to diagnose problems − Certain systems are so complex that it is not easy to understand their interaction at a time. However, Modelling & Simulation allows to understand all the interactions and analyze their effect. Additionally, new policies, operations, and procedures can be explored without affecting the real system. Source: www.tutorialspoint.com Disadvantages of Modeling and Simulation ❑Designing a model is an art which requires domain knowledge, training and experience. ❑Operations are performed on the system using random number, hence difficult to predict the result. ❑Simulation requires manpower and it is a time-consuming process. ❑Simulation results are difficult to translate. It requires experts to understand. ❑Simulation process is expensive. Source: www.tutorialspoint.com Steps in Simulation and Model Building  M & S Development Process Cycle & Relevant Technologies Source: Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, Sokolowski and Banks, 2010 Phases of a Cyclic Movement of Process of M&S 1. Model Phase = modeling technologies 2. Code Phase = development technologies 3. Execute Phase = computational technologies 4. Analyze Phase = data/information technologies Source: Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, Sokolowski and Banks, 2010 1. Probability and Statistics Generation of random variates to model system random input variables that represent uncertainty and variability. To analyze the output from stochastic models or systems. Example: Uncertainty in the movement of cars at a stop light: a) How long before the first car acknowledges the light change to green? b) How fast does that car take off? c) At what time does the second car start moving? d) What is the spatial interval between cars? e) What happens if one of the cars in the chain stalls? 2. Analysis and Operations Research The conduct of a simulation study results in the generation of system performance data, most often in large quantities. These data are stored in a computer system as large arrays of numbers. Analysis: Process of converting the data into meaningful information that describes the behavior of the system. Operations Research: The development and use of the techniques and approaches for analysis. 3. Computer Visualization Visualization: Ability to represent data as a way to interface with the model. Computer Animations: Offshoots of computational science that allow for additional variations in modeling. 4. Human Factors Designer must have a basic understanding of human cognition and perception. 5. Project Management Required when computer simulation is the only method available to investigate a large-scale project. 1. Monte Carlo Simulation It randomly samples values from each input variable distribution and uses that sample to calculate the model’s output. This process of random sampling is repeated until there is a sense of how the output varies given the random input values. It uses probabilities. Example: 2. Continuous Simulation The system variables are continuous functions of time. Time is the independent variable and the system variables evolve as time progresses. Uses differential equations in developing the model. Example: Graph Representing Continuous Behavior for a depletion mode transistor acting as a pull up for a capacitive load 3. Discrete-Event Simulation The system variables are discrete functions of time. These discrete functions in time result in system variables that change only at distinct instants of time. The changes are associated with an occurrence of a system event. It advances time from one event to the next event. It adheres to queuing theory models. Example: 1. Fidelity The term used to describe how the model or simulation closely matches reality. ❖ High Fidelity – when the model or simulation closely matches or behaves like the real system. Not easy to attain because models can never capture every aspect of a system. ❖ Low Fidelity – tolerated with regard to the components of the system that are not important to the investigation. 2. Resolution It is also known as granularity. It is the degree of detail with which the real world is simulated. ❖ High Resolution – when the model or simulation includes more details. 3. Scale It is also known as level. The size of the overall scenario or event the simulation represents. 1. Physics-Based Modeling Method of modeling that used mathematical models where the model equations are derived from basic physical principles. Example: Newton’s law of gravity 2. Finite Element Modeling (FEM) Method used for modeling large or complicated objects by decomposing these elements into a set of small elements and then modeling the small elements. It is widely used for engineering simulations. Example: Aerospace Engineering 3. Data-Based Modeling Results from models based on data describing represented aspects of the subject of the model. Often used when the real system cannot be engaged or when the subject of the model is notional. 4. Agent-Based Modeling (ABM) An important modeling paradigm for investigating many types of human and social phenomena. 5. Aggregate Modeling This method facilitates a number of smaller objects and actions represented in a combined, or aggregated, manner. Most often used in constructive models. 6. Hybrid Modeling Entails combining more than one modeling paradigm. Note: Other model types Markov Chains Finite-state Automata Particle Systems Queuing Models Bond Graphs Petri Nets 1. Live Simulation Involves real people operating real systems. Often involves real equipment or systems. Example: Worcester Preparatory School 10th grade students in AP Biology got a glimpse of what it would be like to be a doctor on a mission trip with the CyberSurgeons live simulation program. It tasked students with determining how to treat patients suffering from a variety of diseases and injuries as they took a simulated voyage down the Amazon River. 2. Virtual Simulation Involves real people operating in simulated systems. These systems are recreated with simulators, and they are designed to immerse the user in a realistic environment. Example: 3. Constructive Simulation Involves real people making inputs into a simulation that carry out those inputs by simulated people operating in simulated systems. Example: US military’s modular semiautomated forces (ModSAF), a constructive combat model designed to train doctrine and rules of engagement. ModSAF Simulated Environment ModSAF view of Simulated Helicopters Table 1.4  Applications are the purposes for developing models and simulations. 1. Training Applications Its intent is to produce learning in the user or participant. 2. Analysis Applications The process of conducting a detailed study of a system to prepare for the design, testing, performance, evaluation and/or prediction of behavior in different environments. 3. Experimentation Applications Its intent is to explore design or solution spaces. It also serves to gain insight into an incompletely understood situation. It is an iterative process of collecting, developing and exploring concepts to identify and recommend value-added solutions for change. 4. Engineering Applications Used to design systems which can be tested or changed in the simulation. The desired end product is validity. Simulation tools used include finite element M & S tools, MATLAB (for modeling continuous systems) and ARENA (for modeling discrete-event systems). 5. Acquisition Applications Entails the process of specifying, designing, developing and implementing new systems. The process includes the entire life cycle of a system from concept to disposal. Its intent is to use the simulation to evaluate cost-effectiveness and correctness before committing funds for an acquisition.  It asks the following question: Does an M&S process include uncertainty and variability? Comprised of two types of simulations: 1. Deterministic Simulation Takes place when a given set of inputs produce a determined, unique set of outputs. This simulation include no uncertainty and no variability. Examples: Physics-based simulations Engineering simulations 2. Stochastic Simulation Accepts random variables as inputs, which logically lead to random outputs. More difficult to represent and analyze because appropriate statistical techniques must be used. This simulation include uncertainty and variability. Example: Discrete-event systems models  The domain of an M&S process refers to the subject area of the process. Examples: Military Simulations Transportation Simulations Decision Support Simulations Game-based Learning Simulations Medical Simulations Formative Assessment 1 Check Canvas for instructions Summative Assessment 1 Check Canvas for instructions Online: www. tutorialspoint.com E-Books and Textbooks: Sokolowski, John A. & Banks, Catherine M. (2010). Modeling and Simulation Fundamentals Theoretical Underpinnings and Practical Domains. John Wiley & Sons Singh, P. & Singh, N. (2012). Modeling and Simulation. S K Kataria and Sons Schmidt and Taylor. (1970). Modeling and Analysis