Multidisciplinary Optimization 7,5 Credits

Course Contents

The course enables the students to apply optimization on top of automated engineering process to optimize aspects of product design. This is achieved by using both classical optimization algorithms and software for modelling objective functions by using one software as input to another.

The course includes the following elements:
• Introduction to optimization driven design: Parameter optimization, Structural optimization
• The steepest descent method, Newton’s method, Karush-Kuhn-Tucker conditions (KKT), linear programming, the Simplex method.
• Optimization algorithms: deterministic: gradient based algorithms, direct methods, stochastic: heuristic and meta-heuristic algorithms
• Multi-objective non-linear optimization and their industrial applications
• Topology optimization using commercial software
• Computer supported modelling of optimization problems

Prerequisites

Passed courses 180 credits in first cycle, at least 90 credits within the major subject Mechanical Engineering, Industrial Engineering and Management or Civil Engineering, and 15 credits in Mathematics. Proof of English proficiency is required. CAD course or equivalent is required (or the equivalent).

Level of Education: Master
Course code/Ladok code: TMOR23
The course is conducted at: School of Engineering Last modified 2023-05-22 11:27:23