Combined ANN-FEM approach for spatial-temporal structural response prediction: Method and experimental validation
| dc.contributor.author | Drieschner, Martin | |
| dc.contributor.author | Wolf, Christoph | |
| dc.contributor.author | Seiffarth, Friedrich | |
| dc.contributor.author | Petryna, Yuri | |
| dc.date.accessioned | 2021-10-22T09:21:53Z | |
| dc.date.available | 2021-10-22T09:21:53Z | |
| dc.date.issued | 2021-10-21 | |
| dc.description.abstract | The 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.sponsorship | DFG, 273721697, SPP 1886: Polymorphe Unschärfemodellierungen für den numerischen Entwurf von Strukturen | en |
| dc.description.sponsorship | DFG, 312928137, Mehrskalige Versagensanalyse unter polymorphen Unsicherheiten für den optimalen Entwurf von Rotorblättern | en |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/13732 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-12508 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten | de |
| dc.subject.other | aleatory uncertainty | en |
| dc.subject.other | epistemic uncertainty | en |
| dc.subject.other | artificial neural networks | en |
| dc.subject.other | ANN | en |
| dc.subject.other | carbon fiber reinforced plastic | en |
| dc.subject.other | CFRP | de |
| dc.subject.other | global stability failure | de |
| dc.subject.other | aleatorische Unschärfe | de |
| dc.subject.other | epistemische Unschärfe | de |
| dc.subject.other | künstliche neuronale Netzwerke | de |
| dc.subject.other | KNN | en |
| dc.subject.other | carbonfaserverstärkter Kunststoff | en |
| dc.subject.other | CFK | en |
| dc.subject.other | globales Stabilitätsversagen | en |
| dc.title | Combined ANN-FEM approach for spatial-temporal structural response prediction: Method and experimental validation | en |
| dc.type | Preprint | en |
| dc.type.version | draft | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 6 Planen Bauen Umwelt::Inst. Bauingenieurwesen::FG Statik und Dynamik | de |
| tub.affiliation.faculty | Fak. 6 Planen Bauen Umwelt | de |
| tub.affiliation.group | FG Statik und Dynamik | de |
| tub.affiliation.institute | Inst. Bauingenieurwesen | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |
| tub.series.issuenumber | 2021-02 | en |
| tub.series.name | Preprint-Reihe des Fachgebiets Statik und Dynamik, Technische Universität Berlin | en |