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Lecture

Design Optimization: Principles and Applications

In course

ME-403: Applied mechanical design

Students will be exposed to hands-on design problems throughout the term. They will acquire methodologies to (1) address open ended engineering problems, (2) cultivate creativity, (3) support decision making and (4) develop problem solving abilities. ...

Description

This lecture introduces the fundamentals of design optimization, discussing the selection of the best design within given constraints and objectives. It covers virtual prototypes, Pareto-optimality, multi-objective optimization, and challenges faced in the field. The instructor presents real-world examples and tools like MATLAB and pymoo for optimization.

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Instructor

Jürg Alexander Schiffmann

After obtaining his diploma in mechanical engineering from EPFL in 1999 he co-founded a start-up company dedicated to the design of gas bearing supported rotors. In 2005 he joined Fischer Engineering Solutions where he led the development of small-scale, gas bearing supported high-speed turbomachinery for fuel cell air supplies and for domestic scale heat pumps. In parallel he worked on his PhD, which he obtained from EPFL in 2008 and for which he was awarded the SwissElectric Research Award. He then joined the Gas Turbine Lab at MIT as a postdoctoral associate where he worked on foil bearings and on the experimental investigation of radial diffusers. In 2013 he was nominated assistant professor at the Ecole Polytechnique Fédérale de Lausanne where he founds the Laboratory for Applied Mechanical Design. His current research interest are in gas lubricated bearings, in aerodynamics of small-scale compressors and turbines and in automated design and optimization methodologies.

Official source

Related concepts (32)

Multi-objective optimization

Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective is a type of vector optimization that has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

Multidisciplinary design optimization

Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines. It is also known as multidisciplinary system design optimization (MSDO), and Multidisciplinary Design Analysis and Optimization (MDAO). MDO allows designers to incorporate all relevant disciplines simultaneously. The optimum of the simultaneous problem is superior to the design found by optimizing each discipline sequentially, since it can exploit the interactions between the disciplines.

Metaheuristic

In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored.

Memetic algorithm

A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem. It uses a suitable heuristic or local search technique to improve the quality of solutions generated by the EA and to reduce the likelihood of premature convergence. Memetic algorithms represent one of the recent growing areas of research in evolutionary computation.

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