02461nam 2200433 450 991079515760332120230803211758.03-8325-9163-X(CKB)4910000000017336(MiAaPQ)EBC5850403(Au-PeEL)EBL5850403(OCoLC)11124223585a8e86f0-e608-4bb4-8b7b-66c5b0dd2d03(EXLCZ)99491000000001733620190917d2014 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierApproximation methods for high dimensional simulation results parameter sensitivity analysis and propagation of variations for process chains /Daniela Steffes-laiBerlin :Logos Verlag,[2014]©20141 online resource (ix, 219 pages) illustrationsPublicationDate: 201406303-8325-3696-5 Includes bibliographical references.Long description: This work addresses the analysis of a sequential chain of processing steps, which is particularly important for the manufacture of robust product components. In each processing step, the material properties may have changed and distributions of related characteristics, for example, strains, may become inhomogeneous. For this reason, the history of the process including design-parameter uncertainties becomes relevant for subsequent processing steps. Therefore, we have developed a methodology, called PRO-CHAIN, which enables an efficient analysis, quantification, and propagation of uncertainties for complex process chains locally on the entire mesh. This innovative methodology has the objective to improve the overall forecast quality, specifically, in local regions of interest, while minimizing the computational effort of subsequent analysis steps. We have demonstrated the benefits and efficiency of the methodology proposed by means of real applications from the automotive industry.Sensitivity theory (Mathematics)Simulation methodsSensitivity theory (Mathematics)Simulation methods.003.5Steffes-lai Daniela1561693MiAaPQMiAaPQMiAaPQBOOK9910795157603321Approximation methods for high dimensional simulation results3828664UNINA