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System model.Chapter One(GEOFFREY GORDON)


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System model.Chapter One(GEOFFREY GORDON)

  1. 1. Chapter One: SYSTEM MODELS Prepared By: Towfiqur Rahman Jessore university of Science and Technology BAngladesh 1
  2. 2. The Concepts of a System A system is an aggregation of objects where objects has regular interaction and interdependence to perform a certain task. Example: FABRICATIO N DEPT PURCHASIN G DEPT ASSEMBL Y DEPT SHIPPIN G DEPT PRODUCTION CONTROL DEPT COUSTOMER ORDER RAW MATERIALS FINISHING GOOD Fig: A factory System 2
  3. 3. BASIC COMPONENTS Entity: An object “component” in the system Attribute: A property of an entity Activity: A process that cause changes in the system System State: Description of system “entities, attributes, and activities” at any point in time. Example: If system is a class in a school, then students are entities, books are their attributes and to study is their activity. 3
  4. 4. System Environment The changes that occurring outside the system and affecting the system called system environment. Two term are used in system environment. i. Endogenous: activities occurring within the system. ii. Exogenous: activities in the environment that affect system. If a system has no exogenous activities that called closed system and if has exogenous activities that called open system. Example: In the factory system the factors controlling the arrival of orders may be considered to be outside the influence of the factory. So it is the part of the environment. 4
  5. 5. Stochastic & Deterministic Activities When the outcome of an activity can be described completely in terms of its input, the activity is said to be deterministic . Where the effects of the activity vary randomly over various possible outcomes , the activity is said to be stochastic. Example : Among the 52 cards, if we pick exactly the card containing the number 3,then it is said to be deterministic . Otherwise, if we pick any card by not looking to the cards, then we can get any card and that is said to be stochastic. 5
  6. 6. Continuous and Discrete Systems Continuous System: In the system in which change are predominantly smooth are called continuous system. Example: In the factory machining proceeds are continuous system. Discrete System: In the system in which change are predominantly discontinuous are called discrete system. Example: In the factory the start and finish of a job are discrete changes. 6
  7. 7. System Modeling System modeling: System modeling as the body of information about a system gathered for the purpose of studying the system. The task the model of a system may be divided into two subtasks. i. Establishing the model structure: determines the system boundary, identifies the entities, attributes and activates the system. ii. Supplying the data: provide the values the attributes and define the relationships involved in the activates. 7
  9. 9. Discussion of Model Physical models: In a physical model the system attributes are represented by such measurements as a voltage . Physical model are based on some analogy between such system as mechanical or electrical Example: The rate at which the shaft of a direct current motor turns depends upon the voltage applied to the motor. Mathematical models: In the mathematical models use symbolic notation and mathematical equation to represent a system. Attributes are represented by variables and the activates are represented by mathematical function. Static models: show the value of attributes take when system in balance. Dynamic models: follow the changes over the time that result from the system activates. Analytical models: To finding the model that can solved and best fits the system being studied. Example: linear differential equation. Numerical methods: involve applying computational procedures to solve equations. Example: the solution derived from complicated integral which need a power series. System simulation: considered to be a numerical computation technique used with dynamic mathematical models. 9
  10. 10. Static Physical Models Static physical models: Static physical model is a scaled down model of a system which does not change with time. Example: An architect before constructing a building, makes a scaled down model of the building, which reflects all it rooms, outer design and other important features. This is an example of static physical model. 10
  11. 11. Dynamic Physical Models Dynamic physical models :Dynamic physical models are ones which change with time or which are function of time. Example: In wind tunnel, small aircraft models are kept and air is blown over them with different velocities. Here wind velocity changes with time and is an example of dynamic physical model. 11
  12. 12. Static mathematical model: A static model gives the relationships between the system attributes when the system is in equilibrium. Example: Here we give a case of static mathematical model from industry. Generally there should be a balance between the supply and demand of any product in the market. Supply increases if the price is higher. But on the other hand demand decreases with the increase of price. Aim is to find the optimum price with which demand can match the supply. If we denote price by P, supply by S and demand by Q, and assuming the price equation to be linear we have Q = a – bP S = c + dP S = Q In the above equations, a, b, c, d are parameters computed based on previous market data. Let us take values of a = 600, b = 3000, c = –100 and d =2000. Value of c is taken negative, since supply cannot be possible if price of the item is zero. In this case no doubt equilibrium market price will be P=a-c/ b+d =0.14,s=180 12
  13. 13. Static mathematical model: The relationship between demand denoted by Q, and price, denoted by P, are represented here by the straight line marked “Demand” in fig:1.6 and supply, denoted by S, is plotted against price & the relationship is the straight line marked “supply”. Supply equals price where the two line cross. Fig 1.5:Market model fig 1.6:Non-linear market model. More usually, the demand and supply are depicted by curves with slopes downward and upward respectively (Fig. 1.6). It may not be possible to express the relationships by equations that can be solved easily. Some numerical or graphical methods are used to solve such relations. 13
  14. 14. Dynamic mathematical model: Dynamic mathematical model allow the change of system attributes to be derived as a function time. Fig: graph shows displacement vs. time 14
  15. 15. Principles Used in Modeling a) Block-building: The system should be organized in a series of blocks to simplify of the interactions within the system. Example: Here each department has been treated as a separate block with input output begin the work passed from department to department. FABRICATIO N DEPT PURCHASIN G DEPT ASSEMBL Y DEPT SHIPPIN G DEPT PRODUCTION CONTROL DEPT Fig: A factory System RAW MATERIALS FINISHING GOOD COUSTOMER ORDER 15
  16. 16. b) Relevance: The model should only include those aspects of the system that are relevant to the study objectives. while irrelevant information may do not any harm, it should be excluded because it increase the complexity and need doing more work to do solve. Example: If the factory system aim to compare the effects of different operating rules on efficiency, it should not to do consider the hiring employees. c)Accuracy: The accuracy of the information for the model should be considered. In the aircraft system the accuracy which the movement of the aircraft depends on the representation of the airframe. If the airframe regard as a rigid body then it necessary to recognize the flexibility of the airframe. An engineer responsible for estimating the fuel consumption satisfied with the simple representation. Another engineer responsible for considering the comfort the passenger, vibrations and will want the description of airframe. d)Aggregation: Aggregation to be considered is the extent to which the number of individual entities can be grouped together into larger entities. Example: The production manager will want to consider the shops of the departments as individual entities. 16
  17. 17. References System Simulation-Geoffrey Gordon 17
  18. 18. THANK YOU 18