07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8
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Item Open Access The physics behind systems biology(2016) Radde, Nicole; Hütt, Marc-ThorstenSystems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’) experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from ist Theoretical Physics foundation.Item Open Access Inverse fuzzy arithmetic for the quality assessment of substructured models(2015) Iroz, Igor; Carvajal, Sergio; Hanss, Michael; Eberhard, PeterThe dynamical analysis of complex structures often suffers from large computational efforts, so that the application of substructuring methods has gained increasing importance in the last years. Substructuring enables dividing large finite element models and reducing the resulting multiple bodies, yielding a reduction of, in this case, complex eigenvalue calculation time. This method is used to predict the appearance of friction-induced vibrations such as squeal in brake systems. Since the method is very sensitive to changes in parameter values, uncertainties influencing the results are included and identified. As uncertain parameters, standard coupling elements are considered and modeled by so-called fuzzy numbers, which are particularly well suited to represent epis- temic uncertainties of modeled physical phenomena. The influence of these uncertainties is transferred to undamped and damped eigenfrequencies of a substructured model by means of direct fuzzy analyses. An inverse fuzzy arithmetical approach is applied to identify the uncertain parameters that optimally cover the undamped reference eigenfrequencies of a non-substructured, full model. If a validity criteria is defined, a positive decision in favor of the most adequate model can be performed.