Modeling and Simulation
A Simulation is the imitation of the operation of a realworld process or system over time. It involves the generation of an artificial history of a system and observation of the artificial history to draw inferences concerning the operating characteristics of the real system. Simulation can be used both as an analysis tool for predicting the effect of changes to existing systems and as a design tool to predict performance of new systems under varying sets of circumstances Advantages of Simulation:
1 Choose correctly.
2 Time compression and expansion
3 Explore possibilities.
4 Diagnose problems.
5 Identify constraints.
6 Develop understanding.
7 Visualize the plan.
8 Prepare for change.
9 Wise investments.
10 Specify requirements.
Disadvantages of simulation:
1. Model building requires special training. It is an art that is learned over time and through experience. 2. Simulation results can be difficult to interpret.
3. Simulation modeling and analysis can be time consuming and expensive.
When Simulation is the appropriate Tool?
1. When you want to study the internal interactions of a complex system. 2. If you want to observe the effect of the informational, organizational and environmental changes on the model’s behavior. 3. The knowledge gained during the designing of a simulation model could be a great value toward suggesting improvement in the system under investigation. 4. Discover the important variables in the system, and discover how they interact. 5. Simulation can be used to verify analytical Solutions. 6. Animation shows a system in simulated operation so that the plan can be visualized. When Simulation is not appropriate?
Simulation should not be used when:
1. The problem can be solved by common sense or analytically. 2. It is easier to perform direct experiments
3. The costs exceed the savings
4. The resources or time are not...
...www.ncetianz.webs.com
System Modeling And Simulation Notes —————— Presented By
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CHAPTER – 1
INTRODUCTION TO SIMULATION
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Simulation A Simulation is the imitation of the operation of a realworld process or system over time. Brief Explanation • The behavior of a system as it evolves over time is studied by developing a simulation model. • This model takes the form of a set of assumptions concerning the operation of the system. The assumptions are expressed in o Mathematical relationships o Logical relationships o Symbolic relationships Between the entities of the system. Measures of performance The model solved by mathematical methods such as differential calculus, probability theory, algebraic methods has the solution usually consists of one or more numerical parameters which are called measures of performance. 1.1 When Simulation is the Appropriate Tool • Simulation enables the study of and experimentation with the internal interactions of a complex system, or of a subsystem within a complex system. • Informational, organizational and environmental changes can be simulated and the effect of those alternations on the model’s behavior can be observer. • The knowledge gained in designing a simulation model can be of great value toward...
...Mangalore
System Simulation and
Modelling
8th SEMESTER COMPUTER SCIENCE and INFORMATION SCIENCE
SUBJECT CODE: 10CS43
Sushma Shetty
7th Semester
Computer Science and Engineering
sushma.shetty305@gmail.com
Units – 1,2,3,5,6,8
Text Books: 1. Discrete Event System Simulation – Jerry Banks, John S Carson,
Barry L Nelson, David M Nicol, P Shahabudeen – 4th Edition Pearson Education
Notes have been circulated on self risk. Nobody can be held responsible if anything is wrong or is improper information or insufficient
information provided in it.
Visit WWW.VTULIFE.COM for all VTU Notes
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1
UNIT 1
INTRODUCTION TO SIMULATION
SIMULATION is the imitation of the operation of a realworld process or system over
time.
Purpose : researchers, analyst, professors, so that they can infer something.
Can simulate globe, planetarium, bank or online sale transactions, study of
aerodynamics.
The behavior of a system as it evolves over time is studied by developing a SIMULATION
MODEL.
SIMULATIONGENERATED DATA is used to estimate the measures of performance of
the system.
WHEN SIMULATION IS THE APPROPRIATE TOOL
Simulation can be used for the following purposes:
1. Simulation enables the study of, and experimentation with, the internal interactions of
a complex system or of a subsystem within a complex system.
2. Informational, organizational,...
...Krannert Graduate School of Management
Purdue University
MGMT 57000
SPREADSHEET MODELING AND SIMULATION
Spring 2014
Instructor: Yanjun Li, office: KRAN 422, phone: 4944525, email: li14@purdue.edu
Office Hours: Tuesday 1:00 – 4:00 pm, or by appointment.
Course Packet: The course packet contains cases and readings from various sources.
Textbook: David Hartvigsen, SimQuick: Process Simulation with Excel, 2nd Edition,
Prentice Hall, 2004. (ISBN: 0131078801)
References: Law, A. M. and W. D. Kelton, SimulationModeling and Analysis, 3rd Edition, McGraw Hill, 2000.
Luenberger, D. G., Investment Science, Oxford University Press, 1998.
Winston, W. L., SimulationModeling Using @RISK, Duxbury, 2001.
Computer Software: Palisade Decision Tools, including @RISK, RISKOptimizer, and StatTools, are available in the computer labs in the Krannert building and Rawls Hall. A student version of the software and the installation guide can be downloaded for free at
https://intra.krannert.purdue.edu/admin/kcc/Pages/Resources.aspx.
SimQuick is included in the companion CD of our textbook.
Course Prerequisites: MGMT 67000
COURSE DESCRIPTION
In the past twenty years, Excel spreadsheets have become the standard tool that business people use to model and analyze quantitative problems. The latest versions of these spreadsheet...
...INVITED PAPER
Modeling and Simulation of Electric and Hybrid Vehicles
Tools that can model embedded software as well as components, and can automate the details of electric and hybrid vehicle design, need to be developed.
By David Wenzhong Gao, Senior Member IEEE, Chris Mi, Senior Member IEEE, and Ali Emadi, Senior Member IEEE
ABSTRACT
 This paper discusses the need for modeling and
simulation of electric and hybrid vehicles. Different modeling methods such as physicsbased Resistive Companion Form technique and Bond Graph method are presented with powertrain component and system modeling examples. The modeling and simulation capabilities of existing tools such as Powertrain System Analysis Toolkit (PSAT), ADvanced VehIcle SimulatOR (ADVISOR), PSIM, and Virtual Test Bed are demonstrated through application examples. Since power electronics is indispensable in hybrid vehicles, the issue of numerical oscillations in dynamic simulations involving power electronics is briefly addressed. KEYWORDS

ADVISOR; bond graph; electric vehicles; hybrid
electric vehicle (HEV); hybrid vehicles; modeling and simulation; physicsbased modeling; Powertrain System Analysis Toolkit (PSAT); PSIM; saber; simplorer; Virtual Test Bed (VTB)
I. INTRODUCTION
Compared to conventional vehicles, there are more...
...
Managerial Decision Modeling w/ Spreadsheets, 3e (Balakrishnan/Render/Stair)
Chapter 10 SimulationModeling
10.1 Chapter Questions
1) John Smith is planning to refinance his home mortgage to take advantage of the lower current interest rates. As part of the refinancing application, the bank needs to appraise Mr. Smith's home. Mr. Smith expects an appraisal of at least $175,000 but no more than $250,000. All values between $175,000 and $250,000 have the same probability of being the actual appraised value. What is the appropriate distribution for simulating appraisal values?
A) continuous uniform
B) triangular
C) binomial
D) discrete uniform
E) normal
Answer: A
Page Ref: 415
Topic: Role of Computers in Simulation
Difficulty: Moderate
2) What is the correct distribution for simulating the outcome of a single coin?
A) continuous uniform
B) triangular
C) binomial
D) exponential
E) normal
Answer: C
Page Ref: 416
Topic: Role of Computers in Simulation
Difficulty: Moderate
3) The technique of randomly generating values for unknown elements in a model using random sampling is known as ________.
A) optimization
B) Markov analysis
C) discreteevent simulation
D) simulation gaming
E) Monte Carlo simulation
Answer: E
Page Ref: 410
Topic: Monte Carlo Simulation
Difficulty: Easy
4) What distribution is appropriate for simulating the event...
...Simulation Modelling Practice and Theory 18 (2010) 712–731
Contents lists available at ScienceDirect
Simulation Modelling Practice and Theory
journal homepage: www.elsevier.com/locate/simpat
Singularityfree dynamic equations of vehicle–manipulator systems
Pål J. From a,*, Vincent Duindam b, Kristin Y. Pettersen a, Jan T. Gravdahl a, Shankar Sastry b
a b
Department of Engineering Cybernetics, Norwegian University of Science and Technology, Norway Department of EECS, University of California, 253 Cory Hall, Berkeley, CA 947201770, USA
a r t i c l e
i n f o
a b s t r a c t
In this paper we derive the singularityfree dynamic equations of vehicle–manipulator systems using a minimal representation. These systems are normally modeled using Euler angles, which leads to singularities, or Euler parameters, which is not a minimal representation and thus not suited for Lagrange’s equations. We circumvent these issues by introducing quasicoordinates which allows us to derive the dynamics using minimal and globally valid nonEuclidean conﬁguration coordinates. This is a great advantage as the conﬁguration space of the vehicle in general is nonEuclidean. We thus obtain a computationally efﬁcient and singularityfree formulation of the dynamic equations with the same complexity as the conventional Lagrangian approach. The closed form formulation makes the proposed approach well suited for system analysis and modelbased control....
...“COMPUTER SIMULATION OF HUMAN THOUGHT”
What is Computer Simulation?
A computer simulation or a computer model is a computer program that attempts to simulate an abstract model of a particular system.
Computer simulations have become a useful part of mathematical modeling of many natural systems in physics, chemistry and biology, human systems in economics, psychology, and social science and in the process of engineering new technology, to gain insight into the operation of those systems. Traditionally, the formal modeling of systems has been via a mathematical model, which attempts to find analytical solutions to problems which enables the prediction of the behavior of the system from a set of parameters and initial conditions. Computer simulations build on, and are a useful adjunct to purely mathematical models in science, technology and entertainment. The reliability and the trust people put in computer simulations depends on the validity of the simulation model.
Computer simulation is used to reduce the risk associated with creating new systems or with making changes to existing ones. More than ever, modern organizations want assurance that investments will produce the expected results. For instance, an assembly line may be required to produce a particular number of autos during an eight hour shift. Complex,...
...SIMULATION
• WHAT is Simulation ?
• WHY is Simulation required ?
• HOW is Simulation applied ?
• WHERE is Simulation used ?
DEFINITION
• Simulation is a representation of reality through the use of model or
other device, which will react in the same manner as reality under a
given set of conditions.
• Simulation is the use of system model that has the designed
characteristic of reality in order to produce the essence of actual
operation.
• According to Donald G. Malcolm, simulation model may be defined
as one which depicts the working of a large scale system of men,
machines, materials and information operating over a period of time
in a simulated environment of the actual real world conditions.
NEED FOR SIMULATION
• Many real world problems which cannot be represented by a
mathematical model.
• Tool for tackling the complicated problem of managerial decisionmaking.
• Utilizes a computerized model.
• To represent actual decisionmaking under conditions of uncertainty
for evaluating alternative courses of action based upon facts and
assumptions.
MONTE CARLO TECHNIQUE
STEPS:
1. Setting up a probability distribution for variables to be analyzed.
2. Building a cumulative probability distribution for each random
variable.
3. Generate random numbers .
4. Conduct the simulation experiment by means of...