ASSIGNMENTOF

SIMULATION AND MODELLING

Submitted to: Mr. Mandeep Singh

Submitted by: Shah Imran

Roll no. : RE3703A21Sec.: E3703

Q1. Delineate various factors which we will consider for system

environment. Justify by giving suitable examples.

Ans: Need to pay attention to human factors in the workplace and work

environment and brought greater indirect attention to ergonomics by including

a Work Environment clause that points out the importance of human factors

on the quality of work produced.

The main factor for system simulation is:

1. Human factor

2. External factor

3. Area

A truck simulator provides an opportunity to reproduce the characteristics of

real vehicles in a virtual environment. It replicates the external factors and

conditions with which a vehicle interacts enabling a driver to feel as if they are

sitting in the cab of their own vehicle. Scenarios and events are replicated

with sufficient reality to ensure that drivers become fully immersed in the

experience rather than simply viewing it as an educational programs me.

The simulator provides a constructive experience for the novice driver and

enables more complex exercises to be undertaken by the more mature driver.

For novice drivers, truck simulators provide an opportunity to begin their

career by applying best practice. For mature drivers, simulation provides the

ability to enhance good driving or to detect poor practice and to suggest the

necessary steps for remedial action. For companies, it provides an

opportunity to educate staff in the driving skills that achieve reduced

maintenance costs, improved productivity and, most importantly, to ensure the

safety of their actions in all possible situations.

Q2. Why simulation is required to study real time systems. Elaborate

with the help of example.

Ans: Adequate testing of computer programs in a real-time environment can

be difficult, if not impossible, without the assistance of specialized hardware

and software test tools and techniques. Unfortunately, software is presently

tested by inadequate manual methods. The major shortcoming of this method

is that the human tester cannot be very thorough or efficient when he must

test a vast number of functions in a sophisticated software system. The

Integrated Simulator described in this paper provides for computer-aided

testing in an interactive real-time environment. The method employs real-time,

digital computer software simulators to assist the tester by collecting data,

exercising the system being tested under varying loads, and supplying test

stimuli to evoke predictable system responses, thus reducing the time,

manpower, and cost of program testing.

MPEG coding dramatically reduces the data amount for video. On the other

hand, its bursts and cyclic nature creates a challenge for transmission in ATM

networks. Various methods have been proposed to transmit MPEG streams,

such as priority coding, dynamic bandwidth allocation and smoothing. This

paper suggests another scheme to the problem. The original MPEG streams

are split into n sub streams and the sub streams are skewed a fixed amount

of time from each other. The sub streams are transmitted with the forward

error correction code. It provides a deterministic bound on the delay and has

the implementation simplicity needed for the high-speed video applications.

We have tested the scheme on five real MPEG-coded video/movie stream

traces. It significantly reduces the peak cell losses at the price of longer delay.

Q3. Differentiate between:

a) Continuous and discrete systems

Ans: Continuous System Simulation describes systematically and

methodically how mathematical models of dynamic systems, usually

described by sets of either ordinary or partial differential equations possibly

coupled with algebraic equations, can be simulated on a digital computer.

Modern modeling and simulation environments relieve the occasional user

from having to understand how simulation really works. Once a mathematical

model of a process has been formulated, the modeling and simulation

environment compiles and simulates the model, and curves of result

trajectories appear magically on the user’s screen. Yet, magic has a tendency

to fail, and it is then that the user must understand what went wrong, and why

the model could not be simulated as expected.

Continuous System Simulation is a highly software-oriented text, based on

MATLAB. Homework problems, suggestions for term project, and open

research questions conclude every chapter to deepen the understanding of

the student and increase his or her motivation.

Continuous System Simulation is written by engineers for engineers,

introducing the partly symbolical and partly numerical algorithms that drive the

process of simulation in terms that are familiar to simulation practitioners with

an engineering background, and yet, the text is rigorous in its approach and

comprehensive in its coverage, providing the reader with a thorough and

detailed understanding of the mechanisms that govern the simulation of

dynamical systems.

A discrete system is a system with a countable number of states. Discrete

systems may be contrasted with continuous systems, which may also be

called analog systems. A discrete system is often modeled with a directed

graph (mathematics) and is analyzed for correctness and complexity

according to computational theory. Because discrete systems have a

countable number of states, they may be described in precise mathematical

models. A computer is a finite state machine that may be viewed as a

discrete system. Discrete data only take on particular values and no values

in between. Data like the number of siblings a person has or the number of

cars a person owns is discrete because you can either have 0 cars or 1 car or

2 cars and so on, but you can't own 1.5 cars.

Continuous data can take on any value on a range. Temperature and height

are continuous because you can be 69.32894... Inches tall. You can be any

fraction of an inch tall in that case.

b) Static Physical and Dynamic Physical Models

Ans: A physical model is a smaller or larger physical copy of an object. The

object being modeled may be small (for example, an atom) or large (for

example, the Solar System). Static simulation models can be used for

analyzing relations of different process input and output variables. We have

made the following static simulation models:

Fiber line

Brown stock screening

Post screening

Control of the TMP process

The effect of filler on paper quality

Quality control of LWC paper

Calendar control

Dynamic simulation models can be used for analyzing and learning process

delays and dynamics in addition to all usages of static simulation models. We

have made the following dynamic simulation models:

Pulp mill liquor and steam balance

Continuous digester dynamics

Pulp mill sulfur / sodium balance

Grinding plant (PGW)

Paper mill short circulation

Paper machine dynamics and grade change

Power plant boilers and steam network

In dynamic simulation models of social systems the desired data may be

unavailable, in an inappropriate form, or incorrect. There may be elements

that are not usually quantified, but that are critical to the system being

modeled.

Criticism of dynamic simulation models aimed at boundary issues frequently

reflects different notions about the model's intended use or purpose.

Dynamic simulation models are often used to search for parameters that can

effect behavior changes. Dynamic simulation models are especially useful in

predicting how a system would behave if various policies of interest were

implemented.

c) Static Mathematical Models and Dynamic Mathematical Models

Ans: mathematical model uses mathematical language to describe a

system. Mathematical models are used not only in the natural sciences and

engineering disciplines (such as physics, biology, earth science, meteorology,

and engineering) but also in the social sciences (such as economics,

psychology, sociology and political science); physicists, engineers, computer

scientists, and economists use mathematical models most extensively. The

process of developing a mathematical model is termed 'mathematical

modeling' (also modeling).

Mathematical model are two type and difference between them:

1. A static model does not account for the element of time, while a

dynamic model does.

2. Dynamic models typically are represented with difference equations or

differential equations.

Part B

Q4. Delineate the Monte Carlo method for simulation in detail. How this

method is useful in system simulation.

Ans: Monte Carlo simulation is a method for iteratively evaluating a

deterministic model using sets of random numbers as inputs. This method is

often used when the model is complex, nonlinear, or involves more than just a

couple uncertain parameters. A simulation can typically involve over 10,000

evaluations of the model, a task which in the past was only practical using

super computers.

The Monte Carlo method is just one of many methods for analyzing

uncertainty propagation, where the goal is to determine how random

variation, lack of knowledge, or error affects the sensitivity, performance, or

reliability of the system that is being modeled. Monte Carlo simulation is

categorized as a sampling method because the inputs are randomly

generated from probability distributions to simulate the process of sampling

from an actual population. So, we try to choose a distribution for the inputs

that most closely matches data we already have, or best represents our

current state of knowledge. The data generated from the simulation can be

represented as probability distributions (or histograms) or converted to error

bars, reliability predictions, tolerance zones, and confidence intervals.

Q.5. By taking an example, analyse and design a system for Simulation.

How will you use the concept of subsystems in it?

Ans: Let's consider an industrial manufacturing example where we will build a

model which has numerical, not graphical, output. Terms such as ``computer-

integrated manufacturing" (CIM) and ``flexible manufacturing" guide the

development of more productive plant configurations for building products

from raw material. We introduce the following categories and definitions:

1. Material. Plants are built to process material---often called raw material

stock---and shape the material into a product. As raw material goes

through its changes, it turns into a part to be processed.

2. Machines. Plants are composed of machines of all kinds which process

material and parts. Some examples are ovens, lubricators, flame

cutters, lathes, and robots.

3. Transportation. Material flows through a network of machines. The

method of transport is affected by devices such as conveyors and

automated guide vehicles (AGVs). During this transit, it encounters

storage areas and accumulators which buffer parts until the machines

can operate upon them.

Figure 3 shows a sample manufacturing system containing nine parts. This

type of drawing is essentially a schematic defining the overall structure of the

system but lacking details on dynamics and geometry. The raw stock arrives

from the left via a central conveyor. At this point, the material stock is a

cylinder shape. The cylinder parts are loaded into a spiral accumulator (A)

which holds parts for the pick-and-place robot (R) until both it and the lathe (L)

are ready. Once both are ready to work with the part, the cylinder is turned

into a barbell shape by the lathe and sent on toward a second spiral

accumulator using a conveyor belt. A second robot also performs a pick-and-

place operation and hands the barbell part to a drill machine (D) which

punches a longitudinal hole through the part. That is the final product part,

which proceeds to a small storage bin taken by the AGV which runs around a

closed track while dropping the bin contents into longer-term storage. This

type of application involves discrete parts flowing through a network of

resources. The resource constraints and network flow suggests the use of a

Petri net to model the system as in Fig. 4.

  

Figure 3: Manufacturing line with two robots and two machines..

  

Figure 4: Petri net model for manufacturing line.

Figure 4 is the mathematical model for the system and is categorized as a

declarative model (i.e., the Petri net sub-states and events are visible and

emphasized in the model structure). In a nutshell, a Petri net operates by

having tokens (the black circles) flow through the network while encountering

resources (lathe, drill press, robot arm, AGV). Each resource operates or

``processes" a token as it passes by. This is the specification that we need to

encode in the form of a program and then execute on a computer. There are

many Petri net simulators to be found. One such simulator is a tool within

SimPack (See section SIMPACK SIMULATION TOOLKIT), which is a toolkit

for exploring mathematical modeling and simulation. Once simulated, this

Petri net can yield data which is subject to analysis (the third sub-field of

computer simulation). The types of analysis methods for simulations are

plentiful. For our manufacturing example, we may simply want to analyze the

throughput of the system as a whole to determine how many parts can be

processed in one hour. Actually, we pre-determined our use of a Petri net

model because we knew ahead of time that we wanted throughput

information. If we had wanted, say, information on the stability of the robot

arm controller then a Petri net would not have served our purpose. Moreover,

if our Petri net model has a stochastic element (i.e., it uses random variants)

then it is vital to make many simulation runs of the same model but with

different samples; otherwise, we will not know the accuracy (measured by a

confidence interval) associated with the simulation output.

Q.6. Elaborate the advantages of system simulation by taking Suitable

examples.

Ans:

1. A simulation can give you results that are not experimentally

measurable with our current level of technology. A simulation can give

these results when problems such as it's too small to measure, the

probe is too big and is skewing the results, and any instrument would

turn to a gas at those temperatures come into the conversation. You

can set the simulation to run for as many time steps you desire and at

any level of detail you desire the only restrictions are your imagination,

your programming skills, and your CPU.

2. Most of the time the simulation testing is cheaper and faster than

performing the multiple tests of the design each time.

For example In the case of electric thrusters the test must be run inside

of a vacuum tank. Vacuum tanks are very expensive to buy, run, and

maintain. One of the main tests of electric thrusters is the lifetime test,

which means that the thrusters is running pretty much constantly inside

of the vacuum tank for 10,000+ hours. This is pouring money down a

drain compared to the price of the simulation.

3. Permit system designers to study a problem at several different levels

of abstraction.

4. Allows the designer to determine the correctness and efficiency of a

design before the system is actually constructed.

5. Simulators dynamically show the behavior and relationship of all the

simulated system's components, thereby providing the user with a

meaningful understanding of the system's nature.