|Name||Analysis and Optimization of Machines, Structures and Technological Processes|
|Status||Compulsory/Courses of Limited Choice; Courses of Free Choice|
|Level and type||Post-graduate Studies, Academic|
|Field of study||Mechanics, Mechanical Engineering, Machine Building|
|Academic staff||Jānis Auziņš, Olga Kononova, Oļegs Jakovļevs|
|Credit points||3.0 (4.5 ECTS)|
Strategy of experiment organization. Basic statistical concepts. Classical experimental designs (Factorial design, Box-Behnken, D-optimal). Space filling designs. V.Eglajs experimental design. Latin Hypercube Design. Regression analysis. Parametric and non-parametric approximation methods. Radial basis functions. Response surface methodology. Experimental Designs for fitting of Response surfaces. Filtration of Outliers. Classification of optimization problems. Handling of nonlinear constraints. Deterministic and stochastic global optimization methods (Taboo search, simulated annealing, genetic algorithms, multistart methods). Virtual prototyping of mechanical systems. Metamodelling and optimization by using EDAOpt, ANSYS and ADAMS programs..
Goals and objectives
of the course in terms
of competences and skills
|1. Understand the principles and mathematical basis of analysis and optimization of complex machines, structures and technological processes. 2. Learn the methodology of parametric and nonparametric metamodeling. 3. Learn, how to use global optimization software for machine, technology and structural optimization. 4. Learn, how to formulate and solve typical engineering optimization problems: performance, durability, stability, mass, cost and other. Understand the complexity and labor intensiveness of different optimization problems.|
Students will be competent in different optimization problem statements. - Exam tasks
Students will be able to apply analytical methods of optimization to the simplest tasks. - Questions in the practical works.
Students will be guided in the offer of widely available optimization software. - Questions in the practical works.
Students will be able to formulate and solve nonlinear optimization tasks of medium complexity, using the commercial software packages. - Course work 1.
Students will be competent in the use of physical and numerical experiments for practical optimization tasks. - Appropriate questions in the practical works.
Students will be able to use the metamodeling and response surface methodology for solving of practical optimization problems. - Course work 1.
Students will be able to evaluate the complexities and difficulties of different optimization task statement and solution. - Exam questions. - Exam
|Course prerequisites||Students must have knowledge of physics, higher mathematics, informatics.|