Aplicación de métodos de correlación digital de imágenes y enfoques probabilísticos en el diseño de soluciones

Zusammenfassung

Science and Technology are indispensable tools in modern Society. Nowadays we are immersed in a dynamic world of Science and Technology that aims to find solutions to daily problems, to continuously advance knowledge, meet new challenges, find new solutions and to improve Society’s Technological level. This philosophy is the base to the present Ph.D. Years ago, people thought that some Fields were reaching their ‘technological ceilings’ but when this happened in Engineering, there was a new revolution thanks to the creation of composites. Although nowadays, it is true that engineering is supported by great tools and techniques, we still tend to apply traditional methods and adquired knowledge to new situations. In the current work, such traditional methods do not always give reliable results, given the complex behaviour of, and the lack of experience in using these new materials. This work aims to progress by looking for methods and solutions which are better adapted to the current context in Engineering, concerning to composites. The first objective is to achieve a synergy with Geomatic technologies; specifically Digital Image Correlation. This technique will give a better understanding of materials as a whole, as well as other economical advantages. Initially, this work focuses on comparing and applying the cited geomatic technique to improve the characterization of composites appropriate for pressurised solutions (containers, piping, etc). Also the data obtained allows for characterising of the variable behaviour of the material through a probabilistic technique. The numerical calculus processes are adapted to this new technique and at the same time are combined with sensitive analysis techniques to obtain design critical parameters. Also there is an advance in the analysis of the results, that constitutes the next evolutionary step in engineering; that is, to move from a deterministic focus to a probabilistic one. This is supported by what is known as Robust Engineering, assisted by surrogate methods to enable viable procedures to be applied to the industrial environment.