Investigação sobre procedimentos de identificação de cargas axiais em dutos submersos a partir de respostas vibratórias

• Autor(a) principal: Kitatani Júnior, Sigeo
• Data de Publicação: 2014
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UFU
• Texto Completo: https://repositorio.ufu.br/handle/123456789/14773; https://doi.org/10.14393/ufu.te.2014.85
• topic: Interação fluido-estrutura; Elementos finitos; Identificação paramétrica; Inferência bayesiana; Problemas inversos; Teoria das estruturas; Método dos elementos finitos; Análise modal; Fluid-structure interaction; Parameter identification; Inverse problems; Bayesian inference; CNPQ::ENGENHARIAS::ENGENHARIA MECANICA

In the present thesis it is proposed and evaluated, both numerically and experimentally, an inverse procedure for the indirect determination of axial loads applied to submersed pipe-like structures, based on their dynamic responses. The investigation is motivated by the existence of practical problems encountered in the oil industry. An experimental bench has been designed and built, consisting in a reservoir inside which a tubular stainless steel beam has been mounted and tested. Special fixtures have been designed in such a way to enable to apply controlled axial loads and represent different types of boundary conditions. In parallel, computational routines have been developed for the two-dimensional modeling of the structure accounting for the effects of axial loads, flexible supports and fluid-structure interaction, based on the finite element approach. Having in mind the difficulties which are expected to be encountered when the methodology be applied in real conditions, some special dynamic test procedures have been considered, including Operational Modal Analysis (OMA), which enables to identify modal parameters from output-only measurements. Numerous scenarios have been considered using either numerically simulated or experimentally measured responses. As for the resolution of the inverse problem, two strategies have been investigated: the first consists in the deterministic resolution of a constrained optimization problem based on evolutionary algorithms, and the second, which enables to account for the presence of uncertainties in the experimental data, is a stochastic approach based on Bayesian inference, combined with Markov chains and Metropolis-Hastings algorithm. The results obtained confirm the operational feasibility and satisfactory accuracy provided by the suggested identification approaches.