GMÜ634 - NUMERICAL METHODS

Course Name Code Semester Theory
(hours/week)		 Application
(hours/week)		 Credit ECTS
NUMERICAL METHODS GMÜ634 Any Semester/Year 3 0 3 7
Prequisites
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Discussion
Question and Answer
Other: Homeworks  
Instructor (s)Department academic staff 
Course objectiveTo teach mathematical modeling of engineering problems, modeling methods, dimensional analysis, numerical methods. To teach the use of numerical methods to solve ordinary and partial differential equations.  
Learning outcomes
  1. At the end of this course, student will have the ability; To use matrix equations for the solution of engineering problems and choose appropriate methods and practices for the problem.
  2. To solve linear and non-linear equations.
  3. To apply different methods of interpolation and curve fitting to engineering problems.
  4. To use numerical differentiation and integration
  5. To use numerical methods to solve ordinary differential equations.
  6. To use numerical methods to solve partial differential equations.
Course ContentIntroduction to modeling. Shell balance. Dimensional analysis. Numerical methods to solve ordinary and partial differential equations. Modeling and numerical solutions in momentum, heat and mass transfer. 
ReferencesApplied Mathematics in Chemical Engineering, Mickley, Sherwood, Reed, McGraw Hill.
Mühendisler İçin Sayısal Yöntemler Steven C. Chapra & Raymond P. Canal Çevirenler: Hasan Heperkan & Uğur Kesgin Literatür Yayınevi, 2003.
 

Course outline weekly

WeeksTopics
Week 1Mathematical modeling
Week 2Shell balance
Week 3Dimensional analysis
Week 4Matrix and determinant
Week 5Systems of linear equations
Week 6Non-linear systems of equations
Week 7Midterm exam 1
Week 8Function placement
Week 9Numerical differentiation
Week 10Numerical integration
Week 11Numerical solutions of differential equations
Week 12Numerical solutions of differential equations
Week 13Midterm exam 2
Week 14Numerical solution of partial differential equations
Week 15Preparation for final exam
Week 16FINAL EXAM

Assesment methods

Course activitiesNumberPercentage
Attendance100
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments630
Presentation00
Project00
Seminar00
Midterms220
Final exam150
Total100
Percentage of semester activities contributing grade succes050
Percentage of final exam contributing grade succes050
Total100

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)7321
Presentation / Seminar Preparation000
Project000
Homework assignment61272
Midterms (Study duration)22550
Final Exam (Study duration) 12525
Total Workload3068210

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. The graduates have acquired extensive and profound knowledge from the scientific work being carried out in their field. They are able to evaluate data critically and to draw conclusions from it.    X
2. The graduates have understanding of applicable techniques and methods and their limits.   X 
3. They are aware of new developments in their field and familiarise themselves with new tasks systematically and without taking too long.    X
4. The graduates are able to formulate engineering problems and find solutions which require very considerable competence as far as methods are concerned.    X
5. The graduates are able to develop new and/or original idea and methods and apply innovative methods in solving the products or processes design problems.   X 
6. The graduates have ability to use their powers of judgment as engineers in order to work with complex and possibly incomplete information, to recognise discrepancies and to deal with them.  X  
7. The graduates are able to understand the impact of engineering solutions in an environmental and societal context.   X 
8. - The graduates have ability to design and implement the analytical modelling and experimental research, and deal with complexity and evaluate data critically.X    
9. The graduates have ability to understand professional, social and ethical responsibility and to act responsibly in the collection, integration, analysis, interpretation and communication of data.  X  
10. The graduates have made a contribution through the written or oral presentation of original research results in the national and international scholarly community.    X 

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest