• Modeling human performance in complex systems • Studying the human element in air traffic control.
5.4 Summary
Simulation is generally more adequate because it involves fewer approximations than conventional methods. Simulation allows adjustment for change, which conventional methods cannot do effectively. In any case, if the time and cost are measured against the quality and completeness of the results, simulation is far ahead of the conventional techniques. Even though the available data are limited, simulation can still be useful because the data are used in a physically rational computational program.
5.5 Key Words
Manufacturing Systems Design,Artificial evolution,Virtually Reality ,Soft Science, Automatic Incident Detection,Hydrology, Continuous Simulation.
5.6 Self-Assessment Questions
Q.1 In a chemical reaction one molecule of a substance X is produced for one molecule each of substance A and B. The initial concentrations of A and B are a and b, respectively. Let x be the concentration of X and assume that it is initially zero. The are at which x increases is 0.1 times the product of the current concentration of A and B. Assume a and b initially 0.8 and 0.4, respectively, simulate the production of X.
Q.2 Identify six different problems from your own experience that you think should be solved using continuous simulation.
Q.3 Describe the use of simulation in education area.
Q.4 Describe the use of simulation in area of weather forecasting. Q.5 Describe the use of simulation in Medical Science?.
Q.6 In a field there are four animals – a dog, a mongoose, a snake and a mouse. Dogs kill mongooses, mongooses kill snakes, and snakes kill mice. The speeds of the mouse snake mongoose are, respectively, 8,12,18 and 30 km/hr. Simulate the chase with the different starting position to see which animal gets kill first. Q.7 Write the benefits of simulation in daily life.
5.7 References/ Suggested Readings
8. Axel rod, R. (1997) ‘Advancing the art of simulation in the social sciences’ in R. Conte, R.
9. Ray K. Linsley and Norman H. Crawford ‘Earliest hydrologic simulation models’ 10. Emshoff, J.R.and R.L.Sisson, Desing and Use of Computer Simulation Models,
Macmillan Co. New York, 1970. 11. G.Gordon. ‘System Simulation’
Subject : System Simulation and Modeling Author : Jagat Kumar Paper Code: MCA 504 Vetter : Prof. Dharminder Kumar Lesson : Simulation Language
Lesson No. : 06 Structure 6.0 Objective 6.1 Introduction
6.2 Continuous Simulation Language
6.2.1 Classification of Continuous Simulation Languages 6.3 Discrete Simulation Language
6.3.1 Classification of Discrete Simulation Languages 6.4 Other Simulation Languages
6.5 Introduction of SIMULA
6.5.1 Basic Building Block of SIMULA 6.5.2 Declarations
6.5.3 Statements 6.5.4 Syntax Rules 6.5.5 Expressions 6.5.6 Type Cast Actors 6.5.7 Conditional Statements
6.5.8 Compound Statements and Blocks as Statements 6.5.9.Declaring Procedures
6.5.10 File Handling in SIMULA 6.5.11 Arrays in SIMULA 6.5.12 Sub-Classes (Inheritance) 6.6 Case Study I 6.7 Case Study II 6.8 Disadvantage of SIMULA 6.9 Summary 6.10 Key Words
6.11 Self Assessments Questions 6.12 References/Suggested Reading
6.0 Objective
As we learn from previous units, computer simulation is useful technique, to study a wide variety of problems related to almost all fields, ranges from science and engineering to business and social science. The abstract model of the system converted into suitable algorithm and finally converted into an executable computer program in a suitable programming language, then after only the system behaviour is understood by analyzing the output results, as the program runs. General-purpose high-level language can be used for simulation but now a day specific simulation languages are also available in market. In this unit we will learn the overview of different simulation language with detailed study of SIMULA 67.
6.1 Introduction
As we learn in previous units about the continuous and discrete event simulation. These are two important type of simulation. The simulation language depending around these two types where one is continuous simulation languages and another one is discrete simulation language. Continuous simulation languages developed in late fifties as simulators of analogue computers. Continuous and discreet simulation language can be classified in several ways.
6.2 Continuous Simulation Language
Continuous simulation languages developed in late fifties as simulators of Analogue computers. Simulation on analogue computers is based on creating an analogue electronic system whose behavior is described by the same mathematical model (set of differential equations) as the system being investigated. The main problem of analogue computers is an analogue implementation of certain operations like multiplication, generation of some functions, generation of delays and others. Digital computers perform all these functions very easily and today continuous simulation is performed only on them. Nevertheless there is one operation where the analogue computers are better, which is integration. Digital computers use numerical integration that is generally slower and less accurate compared with the integration of an analogue integrator. Some special applications based on fast response use therefore the so-called hybrid computers that contain analogue and digital parts connected by Analog/Digital and Digital/Analog converters. The digital part does everything except integration. It computes inputs of integrators that converted by Digital/Analog converters to analogue signals inputted to analogue integrators. Their outputs are treated in the opposite way. The digital part also controls the interconnection of the analogue part that might thus change during computation.
6.2.1 Classification of Continuous Simulation Languages Continuous simulation language can be classified in two ways, as
1. Block oriented simulation languages