MARINE ENGINEERING
Qualification Awarded Length of Program Toplam Kredi (AKTS) Mode of Study Level of Qualification & Field of Study
4 240 FULL TIME TQF, TQF-HE, EQF-LLL, ISCED (2011):Level 6
QF-EHEA:First Cycle
TQF-HE, ISCED (1997-2013): 52

General Course Description Information

Course Code: SMME412
Course Name: NUMERICAL ANALYSIS FOR ENGINEERS
Course Semester: Fall
Course Credits:
Theoretical Uygulama Credit ECTS
1 1 1.5 4
Language of instruction: English
Condition of Course: CE111 - BİLGİSAYAR TEKNOLOJİLERİ ve PROGRAMLAMAYA GİRİŞ | MF111 - COMPUTER TECHNOLOGIES AND PROGRAMMING
Does the Course Work Experience Require?: No
Course Type : Zorunlu
Course Level:
Bachelor TQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree
Mode of Delivery: Face to face
Name of Coordinator: Dr. Öğr. Üyesi Azime ÇETİNKAYA
Course Lecturer(s): Dr. Öğr. Üyesi Azime ÇETİNKAYA
Asst. Prof. Dr. Orhan Özgür AYBAR
Course Assistants:

Objective and Contents of the Course

Course Objectives: 1. To provide a basis on numerical techniques used in engineering problems,
2. To prepare students for solving engineering problems and mathematical
models by numerical calculation devices effectively,
3. To acquaint students with the right sense of selecting appropriate solution
technique for the problem in hand.
Course Content: Introduction to Numerical Methods, Errors and Their Sources, Roots of Equations: Bracketing Methods (Bisection and False Position Methods), Open Methods (Fixed-Point Iteration, Newton-Raphson and Secant Methods), Solutions of Linear Systems of Equations: Gauss Elimination, LU Factorization, Matrix Inversion, Solutions of Linear and Nonlinear Systems of Equations: Jacobi, Gauss-Seidel and Newton Raphson Methods, Interpolation and Extrapolation of Tabulated Data, Curve Fitting by Least Squares, Numerical Differentiation, Numerical Integration: The Trapezoidal Rule, Simpson’s Rules , Romberg Integration, Numerical Solutions of Differential Equations: Picard’s and Taylor Series Method , Numerical Solutions of Differential equations: Euler’s, Modified Euler’s and Runge-Kutta Methods, Finite Difference Methods

Learning Outcomes

The students who have succeeded in this course;
1) Defining errors in a computer system and number representation
2) Finding roots of functions, various root finding methods
3) Solving linear systems
4) Optimization
5) Understanding the difference between regression and interpolation
6) Understanding various numerical integration schemes
7) Understanding numerical differentiation and solving ODE’s
8) Finite difference and PDE’s

Ders Akış Planı

Week Subject Related Preparation
1) Mathematical Modeling, Engineering Problem Solving,
2) Approximations, Round-Off Errors, Truncation Errors and the Taylor Series
3) Bracketing Methods, Open Methods in root finding
4) Roots of Polynomials
5) Gauss Elimination, LU Decomposition and Matrix Inversion
6) Special Matrices and Gauss-Seidel
7) One-Dimensional Unconstrained Optimization, Multidimensional Unconstrained Optimization
8) Least-Squares Regression, Interpolation
9) Newton-Cotes Integration Formulas
10) Numerical Differentiation, Runge-Kutta Methods
11) Stiffness and Multistep Methods
12) Boundary-Value and Eigenvalue Problems
13) Finite Difference, Elliptic Equations, Parabolic Equations
14) General Overview

Sources

Course Notes / Textbooks:
References: 1. Applied numerical analysis1 Curtis F. Gerald, Patrick O. Wheatey, Pearson
Education, Inc.
2. Numerical Analysis, Richard L. Burden and J. Douglas Faires, Brooks/Cole
Buchanan, J.L. and Turner, P.R.,
3. Numerical Methods and Analysis, McGraw-Hill Gilat, A. and
Subramaniam, V.
4. Numerical Methods for Engineers and Scientists 3rd Edition, Wiley
5.Numerical Methods for Engineers, Chapra, S.C., Canale, R.P., McGraw-Hill

Contribution of The Course Unit To The Programme Learning Outcomes

Course Learning Outcomes

1

2

3

4

5

6

7

8

Program Outcomes
1) An ability to apply knowledge of mathematics, science, and engineering
2) An ability to design and conduct experiments, as well as to analyze and interpret data
3) An ability to design a system, component or process to meet desired needs
4) Ability to function on multi-disciplinary teams
5) An ability to identify, formulate, and solve engineering problems
6) An understanding of professional and ethical responsibility
7) An ability to communicate effectively
8) The broad education necessary to understand the impact of engineering solutions in a global and societal context
9) A recognition of the need for, and an ability to engage in life-long learning
10) A knowledge of contemporary issues
11) An ability to use the techniques, skills and modern engineering tools necessary for engineering practice
12) An ability to apply legal, societal and environmental knowledge in maritime transport and in all respective modes of transport operations
13) An ability to interpret and analysis of the data regarding maritime management and operations, recognition and solution of problems for decision making process

Course - Learning Outcomes

No Effect 1 Lowest 2 Average 3 Highest
       
Program Outcomes Level of Contribution
1) An ability to apply knowledge of mathematics, science, and engineering 2
2) An ability to design and conduct experiments, as well as to analyze and interpret data 1
3) An ability to design a system, component or process to meet desired needs 3
4) Ability to function on multi-disciplinary teams 2
5) An ability to identify, formulate, and solve engineering problems 3
6) An understanding of professional and ethical responsibility 1
7) An ability to communicate effectively 2
8) The broad education necessary to understand the impact of engineering solutions in a global and societal context 2
9) A recognition of the need for, and an ability to engage in life-long learning 2
10) A knowledge of contemporary issues 2
11) An ability to use the techniques, skills and modern engineering tools necessary for engineering practice
12) An ability to apply legal, societal and environmental knowledge in maritime transport and in all respective modes of transport operations
13) An ability to interpret and analysis of the data regarding maritime management and operations, recognition and solution of problems for decision making process

Learning Activities and Teaching Methods

Assessment & Evaluation Methods of the Course Unit

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Attendance 28 % 0
Midterms 20 % 40
Semester Final Exam 20 % 60
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

Workload & ECTS Credits of The Course Unit

Aktiviteler Number of Activities Duration (Hours) Workload
Course 14 2 28
Midterms 20 1 20
Semester Final Exam 20 1 20
Total Workload 68