The thermoelasticity problem for pressure vessels with protective coatings, operating under conditions of mechanochemical corrosion

2022 ◽  
Vol 170 ◽  
pp. 103589
Author(s):  
Olga Sedova ◽  
Yulia Pronina
Author(s):  
Jesse J. French ◽  
John M. Henshaw

ASME PCC-2, Repair of Pressure Equipment and Piping, Article 4.1, addresses the repair of high-pressure systems with nonmetallic composite systems and generally permits the repair of dents and gouges in paragraph 1.2(b) with certain substrate preparation requirements. Research done at The University of Tulsa along with meta-analysis of the work preformed over the last three decades at PRCI, GRI and Battelle has shown that classification of mechanical damage must be further refined and delineated to adequately address all applicable facets of non-metallic repair. This paper covers the author’s work with the Nonmetallic Repair Subgroup (SG NMR) to classify types of mechanical damage and the associated permissible repair methods. These damage classifications are set to be included in future editions of the PCC-2. The types of mechanical damage that were considered for this research were dents, cracks, gouges and certain repairable combinations of the three. Specific definitions are laid forth for the damage types and their subclasses along with geometric nomenclature in an attempt to standardize classification across the differences in the international standards. Other forms of mechanical damage and associated amplifying factors exist and are issues that pipeline and refinery assurance managers are concerned with. Additional examples of mechanical damage that are often considered for non-metallic repair are wrinkles and wrinkle bends, creases, buckling points and seismic folds. A common amplifying factor for mechanical damage is that increased corrosion rates often appear at the site of damage due to breeching of protective coatings or creation of extreme geometries that facilitate crevice corrosion, or the development of residual stresses.


Author(s):  
T. Imura ◽  
S. Maruse ◽  
K. Mihama ◽  
M. Iseki ◽  
M. Hibino ◽  
...  

Ultra high voltage STEM has many inherent technical advantages over CTEM. These advantages include better signal detectability and signal processing capability. It is hoped that it will explore some new applications which were previously not possible. Conventional STEM (including CTEM with STEM attachment), however, has been unable to provide these inherent advantages due to insufficient performance and engineering problems. Recently we have developed a new 1250 kV STEM and completed installation at Nagoya University in Japan. It has been designed to break through conventional engineering limitations and bring about theoretical advantage in practical applications.In the design of this instrument, we exercised maximum care in providing a stable electron probe. A high voltage generator and an accelerator are housed in two separate pressure vessels and they are connected with a high voltage resistor cable.(Fig. 1) This design minimized induction generated from the high voltage generator, which is a high frequency Cockcroft-Walton type, being transmitted to the electron probe.


2020 ◽  
Vol 64 (1-4) ◽  
pp. 47-55
Author(s):  
Takuma Tomizawa ◽  
Haicheng Song ◽  
Noritaka Yusa

This study proposes a probability of detection (POD) model to quantitatively evaluate the capability of eddy current testing to detect flaws on the inner surface of pressure vessels cladded by stainless steel and in the presence of high noise level. Welded plate samples with drill holes were prepared to simulate corrosion that typically appears on the inner surface of large-scale pressure vessels. The signals generated by the drill holes and the noise caused by the weld were examined using eddy current testing. A hit/miss-based POD model with multiple flaw parameters and multiple signal features was proposed to analyze the measured signals. It is shown that the proposed model is able to more reasonably characterize the detectability of eddy current signals compared to conventional models that consider a single signal feature.


Author(s):  
Alexander D. Pogrebnyak ◽  
Marharyta A. Lisovenko ◽  
Amanzhol Turlybekuly ◽  
Vladimir V. Buranich

2018 ◽  
Vol 40 (2) ◽  
pp. 106-112
Author(s):  
S.N. Kuzmenko ◽  
◽  
N.Ya. Kuzmenko ◽  
N.N. Laskovenko ◽  
V.A. Gumenyuk ◽  
...  
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