Analysis of polymorphic data uncertainties in engineering applications. Issue 2 (28th March 2019)

• Record Type: Journal Article
• Authors: Drieschner, Martin; Matthies, Hermann G.; Hoang, Truong‐Vinh; Rosić, Bojana V.; Ricken, Tim; Henning, Carla; Ostermeyer, Georg‐Peter; Müller, Michael; Brumme, Stephan; Srisupattarawanit, Tarin; Weinberg, Kerstin; Korzeniowski, Tim F.
• Other Names: Kaliske Michael guestEditor.; Graf Wolfgang guestEditor.
• Is Part Of: Mitteilungen der Gesellschaft für Angewandte Mathematik und Mechanik. Volume 42:Issue 2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-03-28
• Subjects: data acquisition -- data assimilation -- data modeling -- polymorphic uncertainties; Mathematics -- Periodicals; Mechanics, Applied -- Periodicals; 510.5
• DOI: 10.1002/gamm.201900010
• Languages: English
• ISSNs: 0936-7195

Abstract

Abstract : In this contribution, several case studies with data uncertainties are presented which have been performed in individual projects as part of the DFG (German Research Foundation) Priority Programme SPP 1886 "Polymorphic uncertainty modelling for the numerical design of structures." In all case studies numerical models with uncertainties are derived from engineering problems describing concepts for handling and incorporating measurement data, either of model input parameters or of the system response. The first case study deals with polymorphic uncertain data based on computer tomographic scans with respect to air voids which are acquired, simplified and integrated in numerical models of adhesive bonds. In the second case study, the variation sensitivity analysis is presented to provide suitable prior knowledge for numerical soil analyses, for example, in order to reduce required input data. The uncertainty in friction processes is treated in case study 3 whereby measurement data are used in data driven methods to improve the numerical predictions. In case study 4, the failure behavior of die‐cast window hinges, which is affected by an uncertain initial pore distribution, is investigated by means of a Markov chain approach. In the last two case studies, mathematical methods of statistical inference and updating algorithms for uncertainty models are shown. Due to the heterogeneous spectrum of problems, a generalized strategy for data modeling, acquisition, and assimilation is developed and applied on each case study.