No description available.

No description available.

[pt] As tubulações de aços utilizadas pela indústria do petróleo estão constantemente expostas à ação do ácido sulfídrico (H2S) que é o veículo para a difusão do hidrogênio no aço, provocando o surgimento de trincas induzidas pela fragilização por hidrogênio e/ou trincas de corrosão sob tensão. A junta soldada, que é considerada a região crítica dos dutos, foi o objeto de avaliação neste trabalho mediante ensaios de tração sob baixa taxa de deformação (BTD) e através da norma NACE TM0177/96 - Método A (Standard Tensile Test), verificando o seu comportamento quanto à fragilização por hidrogênio e à corrosão sob tensão em aços da classe API grau X70 e X80. Uma solução de tiossulfato de sódio foi utilizada no ensaio BTD para avaliação da fragilização por hidrogênio e corrosão sob tensão, reduzindo custos e atendendo aos ítens de segurança. Os resultados mostraram que os aços API 5L X70 e X80 são susceptíveis à fragilização por hidrogênio e à corrosão sob tensão. / [en] The steel tubes used in the Oil Industry are constantly exposed to hydrogen sulphide (H2S) which leads to the diffusion of hydrogen into the steel, provoking hydrogen induced embrittlement cracks and/or stress corrosion cracking. Welded joints, generally considered to be the critical region in pipelines, were evaluated in this study, using slow strain rate tensile testing (SSRT) and NACE TMO177/96 - Method A (standard tensile test) norm, verifying the behaviour of joints in API X-70 and X-80 grade steels, with regard to hydrogen embrittlement and stress corrosion cracking. Sodium thiosulphate was used in the slow strain rate tests permit the evaluation of hydrogen embrittlement and stress corrosion cracking while reducing testing costs and maintaining safety standards. The results show that the API X-70 and X-80 grade steels are susceptible to hydrogen embrittlement and stress corrosion cracking.

No description available.

No description available.

The time reversal (TR) process manipulates a system's impulse response in order to focus a peak of acoustic energy at a specific location in space and time. This technique has been implemented in both fluid and solid media for purposes ranging from communications to source localization. This thesis will examine both the implementation and processing of TR for nondestructive evaluation in steel, specializing in nonlinear detection methods. A series of steel samples are inspected for stress corrosion cracking (SCC) using TR focusing to excite nonlinearities inherent in cracks. It is determined that SCC exists in the expected regions of the steel samples and that an induced increase in SCC corresponds to an increase in detected nonlinearity. In addition to this, a study is shown wherein TR signal processing is optimized for the detection of cracks. The TR impulse response is modified in a number of ways with the primary goal of increasing the amplitude of the TR focus. Each of these modifications is experimentally scrutinized for characteristics necessary for application to nondestructive evaluation, and ultimately one is chosen that amplifies TR focusing without increasing system nonlinearity. The optimized technique, decay compensation TR, is employed in the detection of SCC and is found to be as or perhaps even more successful than typical TR nondestructive evaluation methods.

Pitting corrosion is a prevalent form of corrosive damage that can weaken, damage, and initiate failure in corrosion-resistant metallic materials. For instance, 304 stainless steel is commonly utilized in various structures (e.g., miter gates, heat exchangers, and storage tanks), but is prone to failure through pitting corrosion and stress corrosion cracking under mechanical loading, regardless of its high corrosion resistance. In this study, to better understand the pitting corrosion damage development, controlled corrosion experiments were conducted to generate pits on 304 stainless steel specimens with and without mechanical loading. The pit development over time was characterized using a high-resolution laser scanner. In addition, to achieve scalable and automatic assessment of pitting corrosion conditions, two convolutional neural network-based computer vision algorithms were adopted and implemented to evaluate the efficacy of networks to identify existence of pitting damage. One was a newly trained convolutional neural network (CNN) using MATLAB software, while the other one was a retrained version of GoogLeNet. Overall, the experimental results showed that time is the dependent variable in predicting pit depth. Meanwhile, loading conditions significantly influence pit morphology. Under compression loading, pits form with larger surface opening areas, while under tension loading, pits have smaller surface opening areas. Deep pits of smaller areas are dangerous for structural members, as they can lead to high stress concentrations and early stress corrosion cracking (SCC). Furthermore, while the training library was limited and consisted of low-resolution images, the retrained GoogLeNet CNN showed promising potential for identifying pitting corrosion based on the evaluation of its performance parameters, including the accuracy, loss, recall, precision, and F1-measure.

No description available.

No description available.

No description available.