Abstract

In engineering design, nature has often been the source of inspiration. It is easy to point out solutions in nature that are optimal in some sense. One example is the roughness of the surface of a sharkâs skin. This is designed by nature to minimize the resistance when the shark swims in the water. Another example is the shape of an egg shell. This is an optimal load carrying structure which often is found in engineering design applications. An even more fascinating question is how nature has found these optimal solutions? The answer to this question is evolution. Instead of just analyzing and copying optimal structures invented by nature it seems reasonable to mimic the process how nature has came up with these solutions. Research on how these ideas can be interpreted and used in engineering design started in the early seventies and has now become a large field known as Evolutionary Algorithms (EAs). During the past decade these methods have emerged as potent tools for engineering design optimization. Some of these methods are especially suited for problems which involve multiple objectives such as almost all real engineering design problems. Just until recently, these methods have seldom been used in the area of rotordynamical design. This thesis deals with the question how these methods can be adapted and applied in order to improve the design and design process of large rotor-bearing system. A hypothesis for this work is that EAs are suitable to use in the late design process of these systems. The aim of this work is to evaluate this hypothesis by studying real applications found in industry. This thesis comprises an introductory part and five appended papers. The introductory part is divided into four different chapters. In the second chapter the concept of engineering design optimization is introduced. In the third chapter Genetic Algorithms (GAs) is presented. Finally, the analysis and design of rotor-bearing systems are introduced and discussed. The purpose with the introductory part is to introduce and prepare the reader to the concepts presented in the papers. The introductory part may serve as a start point for newcomers interested in these areas. The appended papers deal with different rotor-bearing system optimization problems and how these can be formulated and solved with GAs. Paper A introduces a constraint handling technique based on concepts found in multiobjective GAs. In Paper B the multiobjective optimization of a generator is presented and discussed. In Paper C and Paper D the constraint handling technique introduced in Paper A is used for two different rotor- bearing system where the actual bearing geometry parameters are used as design variables in the optimizations. In Paper E the feasibility of site balancing rewinded turbo generators is investigated by the use of a multiobjective GA.