Ders Planı

CLASS 1

Lesson plan /

Lesson Information

Course Credit
Course ECTS Credit
Teaching Language of Instruction	İngilizce
Level of Course	Bachelor's Degree, TYYÇ: Level 6, EQF-LLL: Level 6, QF-EHEA: First Cycle
Type of Course
Mode of Delivery	Face-to-face
Does the course require compulsory or optional work experience?
Course Coordinator	Prof. Dr. RAFET AKDENİZ
Instructor (s)
Course Assistant

Purpose and Content

The aim of the course	Modeling and simulation are important tools supporting engineers in the development of complex systems, from early study of the system concept (when the system possibly does not exist yet) to model-based control design and optimization of system performance. Application areas where modeling and simulation are fundamental tools are, just to mention a few, control, automotive, biomedical, mechanical, chemical engineering. The aim of the course is to provide solid theoretical basis and practical approaches to systematically develop mathematical models of engineering systems from basic physical laws and from experimental data and to use them for simulation purposes.
Course Content	The course covers the following topics: Background on dynamic systems and differential equations Lagrange Modeling (principles and forms) Differential-Algebraic equations (definition, treatment, differential index and index reduction) The Newton method System identification: Max-likelihood and least-squares estimation Parameter estimation for dynamics systems Numerical methods for solving differential equations Explicit Runge-Kutta methods. Stability and order. Implicit Runge-Kutta methods. Stability and order. Advanced topics: sensitivity of simulations, hybrid systems

The aim of the course

Modeling and simulation are important tools supporting engineers in the development of complex systems, from early study of the system concept (when the system possibly does not exist yet) to model-based control design and optimization of system performance. Application areas where modeling and simulation are fundamental tools are, just to mention a few, control, automotive, biomedical, mechanical, chemical engineering. The aim of the course is to provide solid theoretical basis and practical approaches to systematically develop mathematical models of engineering systems from basic physical laws and from experimental data and to use them for simulation purposes.

Course Content

The course covers the following topics: Background on dynamic systems and differential equations Lagrange Modeling (principles and forms) Differential-Algebraic equations (definition, treatment, differential index and index reduction) The Newton method System identification: Max-likelihood and least-squares estimation Parameter estimation for dynamics systems Numerical methods for solving differential equations Explicit Runge-Kutta methods. Stability and order. Implicit Runge-Kutta methods. Stability and order. Advanced topics: sensitivity of simulations, hybrid systems

Learning Outcomes

Adequate knowledge in mathematics, physical sciences and the computer engineering subjects of individual branches; the ability to implement theoretical and practical knowledge in the respective areas to modeling and solving computer engineering problems.
Ability to reflect the developments in Information and Communication Technologies to the applications of Information Technology.
Ability to identify, define, formulate and solve complex computer engineering problems; ability to select and implement the appropriate analysis and modeling methods in this respect.
Ability to design experiments for analysing computer engineering problems, to conduct experiments, to collect data, to analyse and interpret the results.
Ability to design a complex informtacis system, process, device or product to meet specific requirements under realistic restrictions and conditions; ability to implement the modern design methods for this purpose.
Ability to use, choose and develop modern techniques and means required for computer engineering applications; ability to use information technologies effectively.
Ability to work effectively in interdisciplinary and multi-disciplinary teams; ability to work individually..
Knowledge of applications in business life like project management, risk management and change management; awareness about entrepreneurship, innovativeness and sustainable development.
Awareness of the necessity of lifelong learning; ability to reach information, follow the latest developments in science and technology; ability to ensure self-renewal perpetually.
Ability to communicate efficiently both in oral and written ways in Turkish; knowledge of at least one foreign language; ability to effectively prepare reports and understand written reports, prepare design and production reports, effective presentation, giving and receiving clear and understandable instructions.
Knowledge on the effects of informatics applications on health, environment and safety in terms of universal and social aspects and information about the problems of the era; awareness on the legal results of informatics solutions.
Acting in line with ethical principles, sense of professional and ethical responsibility; knowledge on the standards of engineering applications.

Weekly Course Subjects

1	Introduction. Types of Simulation. Static simulation examples.
2	Advantages and disadvantages of simulation. Steps in simulation. Dynamic simulation examples.
3	Components of discrete event simulation. Collection of statistics. Hand simulation
4	Probability rewiev
5	Simulation of a Single-Server Queueing System.
6	Random Number Generators Generators Used by Simulation Languages.
7	Tests for Random Numbers. Frequency for tests. Tests for autocorrelation. Generating Random Variates. Inverse-Transform Technique. Acceptance-Rejection Technique. Special Properties.
8	Midterm Exam
9	Input Distribution Fitting: Histogram, PP, and QQ chart. Input Distribution Fitting: Goodness of fit tests: Chi-square test, KS test.
10	Verification and Validation of Simualtion Models. Output Analysis: Confidence Interval, Terminating simulations.
11	Output Analysis: Warm-up period, autocorrelation. Non-terminating simulations. Output Analysis: Comparison and Evaluation of Alternative System Designs.
12	Variance Reduction Techniques: Indirect measures, control variants.
13	Variance Reduction Techniques: Common random numbers, antithetic random numbers.
14	Simulation of Manufacturing and Material-Handling Systems.

Resources

T. Glad, L. Ljung: Modellbygge och simulering (Studentlitteratur). English version available. - Supplementary material.
Griffiths, Higham: Numerical Methods for Ordinary Differential Equations, Springer, 2010 (freely available for download from Chalmers online Library)

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Lesson Information

Purpose and Content

Learning Outcomes

Weekly Course Subjects

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