ECTS - Numerical Methods for Engineers
Numerical Methods for Engineers (MATH380) Course Detail
Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
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Numerical Methods for Engineers | MATH380 | 5. Semester | 3 | 1 | 0 | 3 | 5 |
Pre-requisite Course(s) |
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(MATH275 veya MATH231) |
Course Language | English |
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Course Type | Compulsory Departmental Courses |
Course Level | Bachelor’s Degree (First Cycle) |
Mode of Delivery | Face To Face |
Learning and Teaching Strategies | Lecture, Experiment, Problem Solving. |
Course Lecturer(s) |
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Course Objectives | This undergraduate course is designed for engineering students. The objective of this course is to introduce some numerical methods that can be used to solve mathematical problems arising in engineering that can not be solved analytically. The philosophy of this course is to teach engineering students how methods work so that they can construct their own computer programs. |
Course Learning Outcomes |
The students who succeeded in this course;
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Course Content | Solution of nonlinear equations, solution of linear systems, eigenvalues and eigenvectors, interpolation and polynomial approximation, least square approximation, numerical differentiation, numerical integration. |
Weekly Subjects and Releated Preparation Studies
Week | Subjects | Preparation |
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1 | 1. Preliminaries: Approximation, Truncation, Round-off errors in computations. | pp. 2 - 41 |
2 | 2. Solution of Nonlinear Equations 2.1. Fixed Point 2.2. Bracketing Methods for Locating a Root | pp. 41 - 51 |
3 | 2.3. Initial Approximation and Convergence Criteria 2.4. Newton-Raphson and Secant Methods | pp. 62 - 70 |
4 | 2.6. Iteration for Non-Linear Systems (Fixed Point for Systems) 2.7. Newton Methods for Systems | pp. 167 - 180 |
5 | 3. Solution of Linear Systems 3.3. Upper-Triangular Linear Systems (Lower-Triangular) 3.4. Gaussian Eliminatian and Pivoting | pp. 120 - 137 |
6 | 3.5. Triangular Factorization (LU) | pp. 141 - 153 |
7 | Midterm | |
8 | 3.7. Doğrusal sistemler için iteratif metotlar (Jacobi / Gauss Seidel Metotları) | pp. 156 - 165 |
9 | 11. Eigenvalues and Eigenvectors 11.2. Power Method (Inverse Power Method) | pp. 588 – 592 pp. 598 - 608 |
10 | 4. Interpolation and Polynomial Approximation 4.2. Introduction to Interpolation 4.3. Lagrange Approximation and Newton Approximation | pp. 199 - 228 |
11 | 5. Curve Fitting 5.1. Least-squares Line | pp. 252 - 259 |
12 | 5.3. Spline fonksiyonları ile interpolasyon | pp. 279 - 293 |
13 | 6. Numerical Differentiation 6.1. Approximating the Derivative 6.2. Numerical Differentiation Formulas | pp. 320 - 348 |
14 | 7. Numerical Integration 7.1. Introduction to Quadrature 7.2. Composite Trapezoidal and Simpson’s Rule | pp. 352 - 374 |
15 | Review | |
16 | Genel Sınav |
Sources
Course Book | 1. J. H. Mathews, K. D. Fink, Numerical Methods Using Matlab, 4th Edition, Prentice Hall, 2004. |
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Other Sources | 2. S. C. Chapra, Applied Numerical Methods with MATLAB for Engineers and Scientists, 3rd Edition, Mc Graw Hill Education, 2012. |
3. A. Gilat, V. Subramaniam, Numerical Methods for Engineers and Scientists: An introduction with Applications Using MATLAB, 3rd Edition, John Wiley & Sons, Inc. 2011. |
Evaluation System
Requirements | Number | Percentage of Grade |
---|---|---|
Attendance/Participation | - | - |
Laboratory | 2 | 10 |
Application | - | - |
Field Work | - | - |
Special Course Internship | - | - |
Quizzes/Studio Critics | - | - |
Homework Assignments | - | - |
Presentation | - | - |
Project | - | - |
Report | - | - |
Seminar | - | - |
Midterms Exams/Midterms Jury | 2 | 50 |
Final Exam/Final Jury | 1 | 40 |
Toplam | 5 | 100 |
Percentage of Semester Work | 0 |
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Percentage of Final Work | 100 |
Total | 100 |
Course Category
Core Courses | |
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Major Area Courses | |
Supportive Courses | X |
Media and Managment Skills Courses | |
Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
# | Program Qualifications / Competencies | Level of Contribution | ||||
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1 | 2 | 3 | 4 | 5 | ||
1 | Applies knowledge in mathematics, science, and computing to solve engineering problems related to manufacturing technologies. | X | ||||
2 | Analyzes and identifies problems specific to manufacturing technologies. | X | ||||
3 | Develops an approach to solve encountered engineering problems, and designs and conducts models and experiments. | X | ||||
4 | Designs a comprehensive manufacturing system (including method, product, or device development) based on the creative application of fundamental engineering principles, within constraints of economic viability, environmental sustainability, and manufacturability. | X | ||||
5 | Selects and uses modern techniques and engineering tools for manufacturing engineering applications. | X | ||||
6 | Effectively uses information technologies to collect and analyze data, think critically, interpret, and make sound decisions. | X | ||||
7 | Works effectively as a member of multidisciplinary and intra-disciplinary teams or individually; demonstrates the confidence and necessary organizational skills. | |||||
8 | Communicates effectively in both spoken and written Turkish and English. | |||||
9 | Engages in lifelong learning, accesses information, keeps up with the latest developments in science and technology, and continuously renews oneself. | X | ||||
10 | Demonstrates awareness and a sense of responsibility regarding professional, legal, ethical, and social issues in the field of Manufacturing Engineering. | |||||
11 | Effectively utilizes resources (personnel, equipment, and costs) to enhance national competitiveness and improve manufacturing industry productivity; conducts solution-oriented project and risk management; and demonstrates awareness of entrepreneurship, innovation, and sustainable development. | |||||
12 | Considers the health, environmental, social, and legal consequences of engineering practices at both global and local scales when making decisions. |
ECTS/Workload Table
Activities | Number | Duration (Hours) | Total Workload |
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Course Hours (Including Exam Week: 16 x Total Hours) | |||
Laboratory | 16 | 1 | 16 |
Application | |||
Special Course Internship | |||
Field Work | |||
Study Hours Out of Class | 14 | 2 | 28 |
Presentation/Seminar Prepration | |||
Project | |||
Report | |||
Homework Assignments | |||
Quizzes/Studio Critics | |||
Prepration of Midterm Exams/Midterm Jury | 2 | 10 | 20 |
Prepration of Final Exams/Final Jury | 1 | 13 | 13 |
Total Workload | 77 |