Lecture 1-2 Introduction to mechnical.pptx

Energy Systems Modeling design and simulation

1. ESE-711 Energy Systems Modelling and Simulations Lecture 1: Introduction Dept. Energy Systems Engineering 1

2. Goals Of This Course Introduce Modeling Introduce Simulation Develop an Appreciation for the Need for Simulation Develop Facility in Simulation Model “Learn by Doing”--Lots of Case Studies Dept. Energy Systems Engineering 2

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4. What Is A Model ? A Representation of an object, a system, or an idea in some form other than that of the entity itself. A three-dimensional representation of a person or thing or of a proposed structure, typically on a smaller scale than the original A model may be exactly the same as the original system or sometimes approximations make it deviates from the real system. As an example, a computer model of a ship may provide the 3D visualization of the ship so that user can rotate and zoom to get a clear idea of the dimensions of the ship. Dept. Energy Systems Engineering 4

5. Model types Physical (Scale models, prototype plants,…) Mathematical (Analytical queueing models, linear programs, simulation) Dept. Energy Systems Engineering 5 A mathematical model is something different from a 3D model. A mathematical model describes a system with equations

6. Physical model Dept. Energy Systems Engineering 6 A physical model is a simplified material representation, usually on a reduced scale, of an object or phenomenon that needs to investigated.

7. Queueing models Dept. Energy Systems Engineering 7 Queuing theory deals with problems which involve queuing (or waiting). Typical examples might be: banks/supermarkets - waiting for service computers - waiting for a response failure situations - waiting for a failure to occur e.g. in a piece of machinery public transport - waiting for a train or a bus

8. Analytical model Dept. Energy Systems Engineering 8

9. What is Simulation? A Simulation of a system is the operation of a model, which is a representation of that system. The model is amenable to manipulation which would be impossible, too expensive, or too impractical to perform on the system which it portrays. Dept. Energy Systems Engineering 9

10. Dept. Energy Systems Engineering 10 Simulation is a technique of studying and analyzing the behavior of a real world or an imaginary system by mimicking it on a computer application.  Benefit: Simulations help designers to optimize their systems by doing necessary changes and obtain good results.

11. Overall A  model  of this system is  "a set of instructions, rules, equations, or constraints for generating [input/output] behavior"     simulator  is any agent that can take this recipe and carry it out, so that it produces the modeled system behavior. In this context, computer modeling is when you create the model in the form of a computer program, while computer simulation is when you run it on a computer, to obtain the system behavior data in the form of a database, spreadsheet, file, or whatever format the model program produces. Dept. Energy Systems Engineering 11

12. SYSTEMS , MODELS, AND SIMULATION System : A collection of entities (people, parts, messages, machines, servers, …) that act and interact together toward some end State of a system: Collection of variables and their values necessary to describe the system at that time

13. Systems , Models, and Simulation (cont’d.) Types of systems Discrete State variables change instantaneously at separated points in time Bank model: State changes occur only when a customer arrives or departs Continuous State variables change continuously as a function of time Airplane flight: State variables like position, velocity change continuously

14. Ways to study a system Systems , Models, and Simulation (cont’d.)

15. Introduction 15 Steps In Simulation and Model Building 1. Define an achievable goal 2. Put together a complete mix of skills on the team 3. Involve the end-user Choose the appropriate simulation tools Develop a plan for adequate model verification (Did we get the “right answers ?”) Develop a plan for model validation (Did we ask the “right questions ?”) Develop a plan for statistical output analysis

16. Introduction 16 Define An Achievable Goal “To model the…” is NOT a goal! “To model the…in order to select/determine feasibility/…is a goal .

17. Introduction 17 Involve the end user -Modeling is a selling job! -Does anyone believe the results? -Will anyone put the results into action? -The End-user (your customer) can (and must) do all of the above BUT, first he must be convinced!

18. Introduction 18 Choose The Appropriate Simulation Tools Assuming Simulation is the appropriate means, three alternatives exist: 1. Build Model in a General Purpose Language (C, java, Visual Basic) 2. Build Model in a General Simulation Language (Discrete simulation, Auto MOD, Arena) 3. Use a Special Purpose Simulation Package (chemical process, electrical circuits, transportation)

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21. Stochastic vs deterministic model In  deterministic models , the output of the  model  is fully determined by the parameter values and the initial conditions initial conditions.  Stochastic models possess some inherent randomness. The same set of parameter values and initial conditions will lead to an ensemble of different outputs. Dept. Energy Systems Engineering 21 In  deterministic models , the output of the model is fully determined by the parameter values and the initial conditions Stochastic models possess some inherent randomness. The same set of parameter values and initial conditions will lead to an ensemble of different outputs.

22. Examples The material properties are well known, i.e. deterministic. none of them is random. The applied load are also deterministic Dept. Energy Systems Engineering 22

23. Random properties, e.g. the Young's modulus is a random variable with uniform distribution [E1, E2]; or normal distribution (of a given mean or standard deviation ) The applied load is random variable, e.g. Wind Load, earthquake (vibration of random amplitude and displacement) Dept. Energy Systems Engineering 23

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25. Dept. Energy Systems Engineering 25 Descriptive  Analytics ( Insight into the past ) : “What has happened?” reports that provide historical insights  regarding the company’s production, financials, operations, sales, finance, inventory and customers . Predictive  Analytics: ( Understanding the future ) “What could happen?” Typical business uses include, understanding how sales might close at the end of the year, predicting what items customers will purchase together Prescriptive  Analytics,( Advise on possible outcomes ) “What should we do? Larger companies are successfully using prescriptive analytics to  optimize production, scheduling and inventory in the supply chain  to make sure that are delivering the right products at the right time and optimizing the customer experience.

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27. Dept. Energy Systems Engineering 27 The steady state is the state that is established after a certain time in your system. The transient state is basically between the beginning of the event and the steady state . C ome to real life : When you open the shower, the water is suddenly released and the temperature is in a  transient state . The temperature will first be cold, then too hot, then finally it will reach the right temperature (around 37ºC),   thus…the steady state .

28. Dept. Energy Systems Engineering 28 For instance, consider a machine learning algorithm that is being trained to recognize handwritten letters of the alphabet, using 1000 examples of handwritten letters and their labels ("A" to "Z") as a training set. One way to modify this training set is to leave out an example, so that only 999 examples of handwritten letters and their labels are available. A stable learning algorithm would produce a similar  classifier  with both the 1000-element and 999-element training sets.  if the world is changing we need to adapt ourselves to the new discoveries