Course objective:
Introduce students to the basics of programming, the Matlab software package, the application of numerical methods on computers and the use of databases for the research and academic community
Course implementation program
1. Introduction. Software package- Matlab: Basics of use, variables, basic functions, arrays, matix and basic arithmetic and logic operations.
2. Software package - Matlab: basics of programming, algorithms, program loops, branching
3. Software package - Matlab: overview of functions, creation of own functions, examples of solving logical tasks and graphical functions. Flowchart.
4. Basic sources of errors in numerical computing.
5. Numerical solution of nonlinear algebraic equations: ITERATION method. NEWTON-RAPHSON method (tangent method), BISECTION method, SECANTE method and REGULA FALSI method.
6. Numerical integration methods: Trapezoidal rule. SIMPSONS rules and ROMBERG method.
7. Numerical solution of differential equations: TAYLOR method, system of two and three differential equations, second and third order differential equations.
8. Numerical solution of differential equations: EULER's method, system of two and three differential equations, second and third order differential equations. RUNGE-KUTTA II method
9. RUNGE-KUTTA IV method, system of two and three differential equations, second and third order differential equations. Review and comparison of other methods of numerical solution of differential equations.
10. Regression analysis. Linear regression equation. Least squares method. Residual deviations, variance, standard deviation, coefficient of variation. Examples of applications in chemistry.
11. Basics of modeling and simulation of dynamic systems in SIMULINK.
12. Basics of databases.
13. Scientific and technical resources on the Internet.
Developing general and specific student competencies:
Acquisition of basic knowledge about programming. Applying numerical methods for solving engineering problems. Get acquainted with the possibilities of accessing and using data for the scientific
Special competencies:
Using Matlab. Numerical methods for solving nonlinear algebraic equations, numerical integration, numerical solution of differential equations. Application of regression analysis over a data set. Application of SIMULINK to solve simpler dynamic process simulations.
Teaching methods (ex cathedra) laboratory exercises (independent practical work under the supervision of assistants and demonstrators). Consultations.
Method of testing knowledge and taking exams:
Oral exam on laboratory exercises. Written reports made in laboratory exercises. 2 written tests (60% on each). The total grade consists of: 65% grade from written test, 25% grade from laboratory exercises and 10% attendance at lectures and homework.
The way of monitoring the quality and success of the course: Student survey
Course learning outcomes:
1. Students will be able to use the Matlab program in solving problems
2. students will be able to find procedural algorithm to solve simpler problems
3. students will distinguish and use methods for: numerical solution of nonlinear algebraic equations, numerical integration and numerical solution of differential equations
4. students will be able to choose the appropriate method for: numerical solution of nonlinear algebraic equations with one unknown, numerical integration, numerical solution of differential equations
5. Students use scientific resources on the Internet
|
-
MATLAB, The Language of Technical Computing, The MathWorks, Inc., 2002.,
-
D.M. Etter, D.C. Kuncicky, H. Moore, Introduction to MATLAB 7, Pearson Prentice Hall, 2005.,
-
W.J. Palm, Introduction to MATLAB 7 for Engineers, McGraw-Hill, New York, 2005.,
-
D.M. Etter, Engineering Problem Solving with MATLAB, Prentice-Hall, New Jersey, 1993.,
-
I. Ivančić, Numerička matematika, Element, Zagreb, 1998.,
-
Steven C. Chapra, Raymond P. Canale, Numerical Methods for Engineers, 6th ed., McGraw-Hill, 2010,
|