Optimization Methods: from theory to design Scientific and technological aspects in mechanics

Many words are spent on optimization nowadays, since it is a powerful instrument to be applied in design. However, there is the feeling that it is not always well understood and the focus still remains on creating new algorithms more than on understanding the way these can be applied to real-life problems. This book is about optimization techniques and is subdivided into two parts. In the first part a wide overview on optimization theory is presented. This is needed for the fact that having knowledge on how the algorithms work is important in order to understand the way they should be applied, since it is not always straightforward to setup an optimization problem correctly. Moreover, a better knowledge of the theory allows the designer to understand which are the pros and cons of the algorithms, so that he will be able to choose the better ones depending on the problem at hand. The optimization theory is introduced and the main ideas in optimization theory are discussed. Optimization is presented as being composed of five topics, namely: design of experiments, response surface modelling, deterministic optimization, stochastic optimization, and robust engineering design. Each chapter, after presenting the main techniques for each part, draws application-oriented conclusions including didactic examples. In the second part, some applications are presented to guide the reader through the process of setting up a few optimization exercises, analyzing critically the choices which are made step-by-step, and showing how the different topics that constitute the optimization theory can be used jointly in an optimization process. The applications which are presented are mainly in the field of thermodynamics and fluid dynamics due to the author’s background. In particular, we deal with applications related to heat and mass transfer in natural and in forced convection, and to Stirling engines. Notwithstanding this, it must be reminded that optimization is an inherently interdisciplinary and multidisciplinary topic and the discussion which is made is still valid for other kind of applications. Summarizing, the idea of the book is to guide the reader towards applications in the optimization field because looking at the literature and at industry there is a clear feeling that a link is missing and optimization risks to remain a nice theory but with not many chances for application after all, while instead it would be a very powerful instrument in industrial design. This is probably enhanced by the fact that the literature in the field is clearly divided into various sub-fields of interest (e.g. gradient-based optimization or stochastic optimization) that are treated as worlds apart and no book or paper has been found trying to put the things together and give a wider overview over the topic. This is limiting optimization application to often ineffective one-shot applications of an algorithm. It could be argued that the book also discusses many techniques that are not properly optimization methods in themselves, such as design of experiments and response surface modelling. However, in the author’s opinion, it is important to include also these methods since in practice they are very helpful in the optimization of real-life industrial application. A practical and effective approach in solving an optimization problem should be an integrated process involving techniques from different subfields. Every technique has its particular features to be exploited knowledgeably, and no technique can be self-sufficient.