Title:
Physics enabled Data-driven structural analysis for mechanical components and assemblies

dc.contributor.advisor Rimoli, Julian J.
dc.contributor.author Shah, Aarohi Bhavinbhai
dc.contributor.committeeMember Di Leo, Claudio V.
dc.contributor.committeeMember Kennedy, Graeme
dc.contributor.committeeMember Kardomateas, George
dc.contributor.committeeMember Tupek, Michael
dc.contributor.department Aerospace Engineering
dc.date.accessioned 2022-08-25T13:32:53Z
dc.date.available 2022-08-25T13:32:53Z
dc.date.created 2022-08
dc.date.issued 2022-05-20
dc.date.submitted August 2022
dc.date.updated 2022-08-25T13:32:53Z
dc.description.abstract Analyzing structures that exhibit nonlinear and history-dependent behaviors is crucial for many engineering applications such as structural health monitoring, wave management/isolation, and geometric optimization to name a few. However, current approaches for modeling such structural components and assemblies rely on detailed finite element formulations of each component. While finite element method serves to be versatile and well-established for nonlinear and history-dependent problems, it tends to be inefficient. Consequently, their computational cost, becomes prohibitive for many applications when time-sensitive predictions are needed. In the present work, we introduce a framework to develop data-driven dimensionally-reduced surrogate models at the component level, which we call smart parts (SPs), to establish a direct relationship between the input–output parameters of the component. Our method utilizes advanced machine learning techniques to develop SPs such that all the information pertaining to history and nonlinearities is preserved. Unlike other data-driven approaches, our method is not limited to any particular type of nonlinearity and it does not impose restrictions on the type of analysis to be performed. This renders its application straightforward for a diverse set of engineering problems, as we show through multiple case studies. We also propose a novel meta learning based approach to enable an extension of this approach to dynamic problems. In addition, we present several ways to enhance this approach in terms of precision and efficiency. Thus, the present work provides an approach that can dramatically boost the computational efficiency and simplicity to analyze large structures without sacrificing accuracy.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/67209
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Reduced order modeling
dc.subject Model order reduction
dc.subject Machine learning
dc.subject Artificial neural networks
dc.subject Finite element analysis
dc.subject Dynamic problems
dc.title Physics enabled Data-driven structural analysis for mechanical components and assemblies
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Rimoli, Julian J.
local.contributor.corporatename College of Engineering
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.relation.ispartofseries Doctor of Philosophy with a Major in Aerospace Engineering
relation.isAdvisorOfPublication 27a85786-1cd4-4655-97d0-ba2c66eccfbc
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isSeriesOfPublication f6a932db-1cde-43b5-bcab-bf573da55ed6
thesis.degree.level Doctoral
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