Título: Evaluation of the nominal thickness of pipelines using neural networks through the dispersion curves drawn with the SAFE method
Autores: Daniel Boechat, Barbara V. A. Lavor, Paula A. Sesini, Helon V. H. Ayala, Arthur M. B. Braga
Resumo: Inspection through non-destructive testing is important for the analysis of oil well structures in different phases of the project, whether during execution, during the production period, or at closure. In this context, the propagation of ultrasonic waves is an important ally to assessing the integrity of structural components, through the tracing of dispersion curves, which show the pair frequency x wavelength for a specific waveguide. This study presents a neural network framework for estimating the percentage of nominal pipeline thickness. The neural network inputs are dispersion curves obtained by simulation, using the semi-analytical finite element method (SAFE), that presents a computational cost reduced compared with the totally numerical simulation. In this way, 100 samples were generated. The case studied consists of a hollow cylinder with different thickness sizes. From the results, it is concluded that the adopted methodology is efficient to predict the percentage of nominal pipeline thickness
Palavras-chave: Non-destructive testing, pipelines, SAFE, Machine Learning, Neural Networks.
Páginas: 7
Código DOI: 10.21528/CBIC2023-032
Artigo em pdf: CBIC_2023_paper032.pdf
Arquivo BibTeX: CBIC_2023_032.bib