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000132114 001__ 132114 000132114 005__ 20240319081015.0 000132114 0247_ $$2doi$$a10.3390/app12157458 000132114 0248_ $$2sideral$$a130190 000132114 037__ $$aART-2022-130190 000132114 041__ $$aeng 000132114 100__ $$aDi Lorenzo, Daniele 000132114 245__ $$aData Completion, Model Correction and Enrichment Based on Sparse Identification and Data Assimilation 000132114 260__ $$c2022 000132114 5060_ $$aAccess copy available to the general public$$fUnrestricted 000132114 5203_ $$aMany 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. 000132114 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000132114 590__ $$a2.7$$b2022 000132114 591__ $$aPHYSICS, APPLIED$$b78 / 160 = 0.488$$c2022$$dQ2$$eT2 000132114 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b42 / 90 = 0.467$$c2022$$dQ2$$eT2 000132114 591__ $$aCHEMISTRY, MULTIDISCIPLINARY$$b100 / 178 = 0.562$$c2022$$dQ3$$eT2 000132114 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b208 / 343 = 0.606$$c2022$$dQ3$$eT2 000132114 592__ $$a0.492$$b2022 000132114 593__ $$aFluid Flow and Transfer Processes$$c2022$$dQ2 000132114 593__ $$aMaterials Science (miscellaneous)$$c2022$$dQ2 000132114 593__ $$aEngineering (miscellaneous)$$c2022$$dQ2 000132114 593__ $$aInstrumentation$$c2022$$dQ2 000132114 593__ $$aProcess Chemistry and Technology$$c2022$$dQ3 000132114 593__ $$aComputer Science Applications$$c2022$$dQ3 000132114 594__ $$a4.5$$b2022 000132114 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000132114 700__ $$aChampaney, Víctor 000132114 700__ $$aGermoso, Claudia 000132114 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elias$$uUniversidad de Zaragoza 000132114 700__ $$aChinesta, Francisco 000132114 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est. 000132114 773__ $$g12, 15 (2022), 7458 [20 pp.]$$pAppl. sci.$$tApplied Sciences (Switzerland)$$x2076-3417 000132114 8564_ $$s6598545$$uhttps://zaguan.unizar.es/record/132114/files/texto_completo.pdf$$yVersión publicada 000132114 8564_ $$s2684075$$uhttps://zaguan.unizar.es/record/132114/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000132114 909CO $$ooai:zaguan.unizar.es:132114$$particulos$$pdriver 000132114 951__ $$a2024-03-18-15:31:09 000132114 980__ $$aARTICLE
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