Практикум составлен в соответствии с требованиями к коммуникационной подготовки выпускников инновационной образовательной программы
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IV. Read, translate and suggest the title of the text Some of the reasons to use models may be presented as follows. To fly a simulator is safer and cheaper than the real airplane. By precisely this reason models are used in industry, commerce and military: it is very costly, dangerous or often impossible to make experiments with real systems. Provided that models are adequate descriptions of reality, (they are valid) experimenting with them can save money, suffering and time. Systems which change with time such as a gas station where cars come and go (called dynamic systems) and involve randomness (nobody can guess at exactly which time the next car should arrive at the station) are good candidates to simulation. Modeling complex dynamic systems theoretically need too many simplifications and the emerging models may not be therefore valid. Simulation does not require that many simplifying assumptions, making it the only tool even in absence of randomness. In order to describe the simulation mechanism, suppose we are interested in a gas station. We may describe the behavior of this system graphically by plotting the number of cars in the station, the state of the system. Every time a car arrives the graph increases by one unit while a departing car causes the graph to drop one unit. This graph, called sample path, could be obtained from observation of a real station, but could also be artificially constructed. Such artificial construction and the analysis of the resulting sample path (or more sample paths in more complex cases) consist of the simulation. The above sample path consisted only horizontal and vertical lines, as car arrivals and departures occurred at distinct points of time what we refer as events. Between two consecutive items nothing happens – the graph is horizontal. When number of events is finite, we call simulation discrete event. In some systems the state changes all the time, not just at time of some discrete events. For example water level in a reservoir with given in and outflow may change all the time. In such cases continuous simulation is more appropriate, although discrete event simulation can serve as an approximation. Simulations may be performed manually. Most often, however, the system model is written either as a computer code or as some kind of input into simulator software.
V. Find in the text some sentences in Passive Voice and make them active. VI. Translate the following attribute constructions: complex dynamic systems;resulting sample path;simulation discrete event;continuous simulation;automatic data process;event-driven programming;mechanical analog computer. VII. According to the given information in the text complete the table with missing phrases then make your own sentences
VIII. Choose the right answer on the text information 1. Systems which change with time are called … .
2. Simulation requires … .
c) not so many simplifying assumptions 3. Sample path could be obtained … .
c) by combining both observation and construction 4. Simulation discrete events takes place when … .
c) the number of events is finite 5. We can perform simulation … .
c) as some kind of input into simulator software IX. Pair or group work. Discuss the ways of the system behavior description. Use the completed graph from exercise VII. ------------------------------------------------------------------------------------------ Unit Six Text. TYPES OF COMPUTER SIMULATION Key words: Stochastic or deterministic type, steady-state or dynamic type, discrete event simulation, continuous dynamic simulation, agent-based simulation, distributed models Computer models can be classified according to several criteria including: - Stochastic or deterministic (and as a special case of deterministic, chaotic). These models use random number generators to model chance or random events; they are also called Monte Carlo simulations. - Steady-state or dynamic. Steady-state models use equations defining the relationships between elements of the modeled system and attempt to find a state in which the system is in equilibrium. Such models are often used in simulating physical systems, as a simpler modeling case before dynamic simulation is attempted. Dynamic simulations model changes in a system in response to (usually changing) input signals. Continuous or discrete (and as an important special case of discrete, discrete event or DE model). A discrete event simulation (DES) manages events in time. Most computer, logic-test and fault-free simulations are of this type. In this type of simulation, the simulator maintains a queue of events sorted by the simulated time they should occur. The simulator reads the queue and triggers new events as each event is processed. It is not important to execute the simulation in real time. It’s often more important to be able to access the data produced by the simulation, to discover logic defects in the design, or the sequence of events. A continuous dynamic simulation performs numerical solution of differential-algebraic equations or differential equations (either partial or ordinary). Periodically, the simulation program solves all the equations, and uses the numbers to change the state and output of the simulation. Applications include flight simulators, simulation games, chemical process modeling and simulations of electrical circuits. Originally, these kinds of simulations were actually implemented on analog computers, where the differential equations could be represented directly by various electrical components such as op-amps. By the late 1980s, however most “analog” simulations were run on conventional digital computers that emulate the behavior of an analog computer. A special type of discrete simulation which does not rely on a model with an underlying equation, but can nonetheless be represented formally, is agent-based simulation. In agent-based simulation the individual entities (such as molecules, cells, trees or consumers) in the model are represented directly (rather than by their density or concentration) and possess an internal state and set of behaviors or rules which determine how the agent’s state is updated from one time-step to the next. Local or distributed. They run on a network of interconnected computers, possibly through the Internet. Simulation dispersed across multiple host computers like this are often referred to as “distributed simulation”. There are several standards for distributed simulation, including Aggregate Level Simulation Protocol (ALSP), Distributed Interactive Simulation (DIS), the High Level Architecture (simulation) (HLA) and the Test and Training Enabling Architecture (TENA). The increasing size of the systems and designs requires more efficient simulation strategies to accelerate the simulation process. Parallel and distributed simulation approaches seem to be a promising approach in this direction. Current topics under extensive research are: - synchronization, memory management, partitioning and load balancing; - synchronization in multi-user distributed simulation; - system modeling for parallel simulation, re-use of models/code; - language and implementation issues, models of parallel simulation; - theoretical and empirical studies; - computer architectures, telecommunications networks, dynamic systems; - Web based distributed simulation such as multimedia and real time applications, fault tolerance. Comprehensive text-related glossary
Activities I. Choose the right answer
a) analog computers b) conventional digital computers c) a network of interconnected computers II. Translate the following attribute constructions: simulation testing problem; parallel simulation approach; electrical circuits simulation; simulation coding language; multi-user distributed simulation III. According to the given information in the text complete the graph with missing phrases and say about different simulation model Computer models … Continuous … Stochastic agent-based n … use equations defining the relationships between elements of the modeled system perform numerical solution of differential-algebraic equations or differential equations IV. Questions for discussion: 1. What type of discrete simulation represents individual entitiesdirectly?2. What are the main criteria of computer models classification? 3. Where and how can steady-state models be used? 4. What do applications in dynamic simulation include? Where were they implemented? 5. What approaches seem to be promising in acceleration of the simulation process? V. Prepare a short report about types of simulation using both the information from the text and some additional information on the problem known to you before reading the text. Make use of the scheme from exercise 14 given above as a general plan of your report. The following phrases may be useful for your presentation: 1. I’d like to say a few words about…… to explain the main features of……. to describe the operation of…….. 2. To illustrate my talk I intend to show you some diagrams (slides, transparencies).
The subject can be looked at under … main headings. During my talk I’ll be looking at … main areas. First (ly)……..second (ly)…….third (ly)…….. 4. If you have questions, please ask me. I’ll be glad to answer them at the end of my talk. VI. Read and translate the following text. Find the key words in the text System’s terminology Sample: A sample is a group of units selected from a larger group. By studying the sample it is hoped to draw valid conclusions about the larger group that is too large to study in its entirety. Parameter: A parameter is a value, usually unknown (and which therefore has to be estimated), used to represent a certain characteristic. State: A variable characterizing the system such as level of stock in inventory or number of jobs waiting for processing. Event: An occurrence at a point of time which may change the state of system such as arrival of a customer or start of working on a job. Entity: An object which passes through the system such as cars in an intersection or orders in a factory. Often an event (e.g., arrival) is associated with an entity (e.g., customer). Queue: It is not a physical queue of people, it can also be a task list, a buffer of finished goods waiting for transportation or any place where entities are waiting for something to happen for any reason. Creating is causing an arrival of a new entity to the system in some future time. Scheduling is to assign a new future event to an existing entity. Random variable is a quantity which is uncertain such as interarrival time between two incoming flights or number of defective parts in a shipment. Random variate is an artificially generated random variable. Distribution is the law which governs the probabilistic features of a random variable. Comprehensive text-related glossary
VII. Match the terms from the left column with the definitions in the right column
VIII. Write a summary of the text (8-10 sentences) using the key words and the information from Internet. |