132114 20240319081015.0 doi 10.3390/app12157458 sideral 130190 ART-2022-130190 eng Di Lorenzo, Daniele Data Completion, Model Correction and Enrichment Based on Sparse Identification and Data Assimilation 2022 Access copy available to the general public Unrestricted Many models assumed to be able to predict the response of structural systems fail to efficiently accomplish that purpose because of two main reasons. First, some structures in operation undergo localized damage that degrades their mechanical performances. To reflect this local loss of performance, the stiffness matrix associated with the structure should be locally corrected. Second, the nominal model is sometimes too coarse grained for reflecting all structural details, and consequently, the predictions are expected to deviate from the measurements. In that case, there is no small region of the model that needs to be repaired, but the entire domain needs to be repaired; therefore, the entire structure-stiffness matrix should be corrected. In the present work, we propose a methodology for locally correcting or globally enriching the models from collected data, which is, upon its turn, completed beyond the sensor''s location. The proposed techniques consist in the first case of an L1-minimization procedure that, with the support of data, aims at the same time period to detect the damaged zone in the structure and to predict the correct solution. For the global enrichment, instead, the methodology consists of an L2-minimization procedure with the support of measurements. The results obtained showed, for the local problem, a correction up to 90% with respect to the initially incorrectly predicted displacement of the structure, and for the global one, a correction up to 60% was observed (this results concern the problems considered in the present study, but they depend on different factors, such as the number of data used, the geometry or the intensity of the damage). The benefits and potential of such techniques are illustrated on four different problems, showing the large generality and adaptability of the methodology. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.7 2022 PHYSICS, APPLIED 78 / 160 = 0.488 2022 Q2 T2 ENGINEERING, MULTIDISCIPLINARY 42 / 90 = 0.467 2022 Q2 T2 CHEMISTRY, MULTIDISCIPLINARY 100 / 178 = 0.562 2022 Q3 T2 MATERIALS SCIENCE, MULTIDISCIPLINARY 208 / 343 = 0.606 2022 Q3 T2 0.492 2022 Fluid Flow and Transfer Processes 2022 Q2 Materials Science (miscellaneous) 2022 Q2 Engineering (miscellaneous) 2022 Q2 Instrumentation 2022 Q2 Process Chemistry and Technology 2022 Q3 Computer Science Applications 2022 Q3 4.5 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Champaney, Víctor Germoso, Claudia Cueto, Elias Universidad de Zaragoza (orcid)0000-0003-1017-4381 Chinesta, Francisco 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 12, 15 (2022), 7458 [20 pp.] Appl. sci. Applied Sciences (Switzerland) 2076-3417 6598545 http://zaguan.unizar.es/record/132114/files/texto_completo.pdf Versión publicada 2684075 http://zaguan.unizar.es/record/132114/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:132114 articulos driver 2024-03-18-15:31:09 ARTICLE