Combined ANN-FEM approach for spatial-temporal structural response prediction: Method and experimental validation

dc.contributor.authorDrieschner, Martin
dc.contributor.authorWolf, Christoph
dc.contributor.authorSeiffarth, Friedrich
dc.contributor.authorPetryna, Yuri
dc.date.accessioned2021-10-22T09:21:53Z
dc.date.available2021-10-22T09:21:53Z
dc.date.issued2021-10-21
dc.description.abstractThe prediction of system outcomes like strains or displacement fields in real technical systems is demanding due to the presence of unavoidable uncertainties. These uncertainties should be considered, for example by different uncertainty models either based on probabilistic, possibilistic or other approaches. In this contribution, a non-linear stability analysis of a three-dimensional carbon fiber reinforced plastic (CFRP) considering aleatory and epistemic uncertainties is conducted. For the realistic incorporation of the uncertainties in the finite element model, thickness variations and geometrical inaccuracies have been detected in advance by non-destructive testing on a real structure made of CFRP. Additionally, the material parameters have been defined as stochastic variables based on reference studies in the literature. If the underlying deterministic model itself is also time-consuming, it can be useful to surrogate the overall numerical simulation. Strains and displacement fields have been measured in a symmetric three-point bending test and compared to the numerical predictions produced by artificial neural networks (ANN). A sensitivity analysis is finally conducted which clarifies the strong dependence of the outcomes on the fiber volume content, the structural thicknesses and the stiffness in fiber direction.en
dc.description.sponsorshipDFG, 273721697, SPP 1886: Polymorphe Unschärfemodellierungen für den numerischen Entwurf von Strukturenen
dc.description.sponsorshipDFG, 312928137, Mehrskalige Versagensanalyse unter polymorphen Unsicherheiten für den optimalen Entwurf von Rotorblätternen
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/13732
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-12508
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otheraleatory uncertaintyen
dc.subject.otherepistemic uncertaintyen
dc.subject.otherartificial neural networksen
dc.subject.otherANNen
dc.subject.othercarbon fiber reinforced plasticen
dc.subject.otherCFRPde
dc.subject.otherglobal stability failurede
dc.subject.otheraleatorische Unschärfede
dc.subject.otherepistemische Unschärfede
dc.subject.otherkünstliche neuronale Netzwerkede
dc.subject.otherKNNen
dc.subject.othercarbonfaserverstärkter Kunststoffen
dc.subject.otherCFKen
dc.subject.otherglobales Stabilitätsversagenen
dc.titleCombined ANN-FEM approach for spatial-temporal structural response prediction: Method and experimental validationen
dc.typePreprinten
dc.type.versiondraften
tub.accessrights.dnbfreeen
tub.affiliationFak. 6 Planen Bauen Umwelt>Inst. Bauingenieurwesen>FG Statik und Dynamikde
tub.affiliation.facultyFak. 6 Planen Bauen Umweltde
tub.affiliation.groupFG Statik und Dynamikde
tub.affiliation.instituteInst. Bauingenieurwesende
tub.publisher.universityorinstitutionTechnische Universität Berlinen
tub.series.issuenumber2021-02en
tub.series.namePreprint-Reihe des Fachgebiets Statik und Dynamik, Technische Universität Berlinen
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