physical modeling

7/29/2019 physical modeling

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PRESENTATIONon

Physical Modelling

BYDeeksha Porwal

(M.TECH.)

(MANUFACTURING SCIENCE &

TECHNOLOGY)



THE CONCEPT OF A SYSTEM

A system is:-

A set of components which are related by

some form of interaction and which act

together to achieve some objective or purpose.

Components are the individual parts or

elements that collectively make up the system.

Objective is the desired state or outcome

which the system is attempting to achieve.



ENTITIES ATTRIBUTES AND

ACTIVITIES

Entity : object of interest in the system

Attribute : property of an entity

Activity : process that causes change insystem

Example : An Elevator System

Entities : Elevators, people

Attributes: Elevators – capacity, speed,

destination, current location of each elevator



Entities Attributes Continue......

People – inter-arrival time at each floor,

destination of each people.

Activities

Load/Unloading passenger

Travel to next floor (speed and distance)

Persons travel to elevator



SYSTEM ENVIRONMENT

It can be defined as:-

A system is often affected by changes occurring outside

the system. Some system activities may also produce

changes that do not react on the system. Such changes

occurring outside the system are said to occur in system

environment.

Endogenous : activities occurring within thesystem.

Exogenous : activities in the environment

that affect the system. Closed system :for which there is no

exogenous activity.

Open system : which does have exogenous

activity.



Example: Environment of a business

system



CONTINUOUS AND DISCRETE SYSTEMS

Continuous System: The state variables change in

a continuous way, and not abruptly from one state

to another (infinite number of states).

Discrete System: The state variables change only

at a countable number of points in time.

These points in time are the ones at which the

event occurs/change in state.



SALES OF A CERTAIN PRODUCT

OVER TIME



LINEAR AND NON LINEAR SYSTEMS

Linear systems satisfy the properties of

superposition and homogeneity.

Linear systems have one equilibrium point at

the origin.

Any system that does not satisfy these

properties is nonlinear.

Nonlinear systems may have many equilibriumpoints



STOCHASTIC ACTIVITY

•When the effects of activity vary randomly overvarious possible outcomes, the activity is said to bestochastic.

•Stochastic activity is one whose behavior cannotbe entirely predicted i.e.; non-deterministic, inthat a system's subsequent state is determined

both by the process's predictable actions and by arandom element.



TYPES OF MODELS

MODELS

PHYSICAL MATHEMATICAL

STATIC DYNAMIC

NUMERICAL ANALYTICAL NUMERICAL

SYSTEM

SIMULATION

STATIC DYNAMIC



STATIC AND DYNAMIC MODELS

A static model:-

• takes a single snapshot of a situation.

• everything occurs in a single interval.

Dynamic models are:-

• time dependent.

• state variable change over time.



PRINCIPLES OF MODELLING

Block Building-: the description of the system should

be organised in a series of blocks.

The system may be described as the interconnection

between the blocks.

Block represents a part of a system that depends upon

few input variables and results in output variables.

Relevance-: Only those aspects of the system that arerelevant to objectives of studies should be included in

the model.



Accuracy-: The accuracy of the information

gathered for the model should be considered.

Aggregation-: A further factor to be

considered to which the number of individual

entities can be group together into lager entity.

Principles of Modelling Continue......



BASIC SIMULATION MODELLING

Simulation Modelling is therefore an experimental

and applied methodology which seeks to-:

Describe the behaviour of the system.

Construct theory or hypothesis that account for the

observed behaviour.

Use these theories to predict future behaviour i.e. the

effects that will be produced changes in the system orin its method of operation.



The steps involved in simulation analysis are-:

Step 1. Identify the problem.

Step 2. Formulate the problem.

Step 3. Collect and process real system data.

Step 4. Formulate and develop a model.

Step 5. Validate the model. Step 6. Document model for future use.

Step 7. Select appropriate experimental design.

Step 8. Establish experimental conditions for runs.

Step 9. Perform simulation runs.

Step 10. Interpret and present results.

Step 11. Recommend further course of action.



ADVANTAGES OF SIMULATION

Most complex, real-world systems with stochastic elements

can be investigated.

Simulation allows one to estimate the performance of an

existing system under some projected set of operating

conditions.

Alternative proposed system designs can be compared via

simulation to see which best meets a specified requirement.

We can maintain much better control over experimental

conditions than would generally be possible whenexperimenting with the system itself.

Relatively straight forward and flexible.



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