Design of specimen holders for flow accelerated corrosion experiments in molten lead with numerical evaluation of pressure losses

2021 ◽  
Vol 385 ◽  
pp. 111522
Author(s):  
Khaled Talaat ◽  
Md Mehadi Hassan ◽  
Cemal Cakez ◽  
Shuprio Ghosh ◽  
Brandon Bohanon ◽  
...  
2021 ◽  
Vol 122 ◽  
pp. 105127
Author(s):  
P. Madasamy ◽  
M. Mukunthan ◽  
P. Chandramohan ◽  
T.V. Krishna Mohan ◽  
Andrews Sylvanus ◽  
...  

Author(s):  
Dong-Jin Kim ◽  
Sung-Woo Kim ◽  
Jong Yeon Lee ◽  
Kyung Mo Kim ◽  
Se Beom Oh ◽  
...  

Author(s):  
Ali Keshavarz ◽  
Andrew K. Ali ◽  
Randy K. Lall

Flow-accelerated corrosion (FAC) is a phenomenon that results in metal loss from piping, vessels and equipment made of carbon steel. This metal loss can lead to stress to occur at the steam inlet nozzle side, where it is located at the side of the deaerator. This paper presents a method to find the thickness critical of the steam inlet nozzle. A Finite Element (FE) model of the pressure vessel head was created to perform a stress analysis using NX Nastran 5.0. By applying materials properties, loads and constraints to the model, the results obtained are required to satisfy the following criterion: vonMises≥SySy=YieldStrength The results obtained from the stress analysis were analyzed to obtain a corrosion allowance and it was compared to the recommended value from a normal deaerator design, which is roughly 0.25 inches. From the FE model, and by continuously reducing the thickness of the nozzle, it was determined that the corrosion allowance is 0.229 inches, and that the percentage error was 8.4%.


2018 ◽  
Vol 25 (7) ◽  
pp. 779-787 ◽  
Author(s):  
Yong Li ◽  
Min-dong Chen ◽  
Jian-kuan Li ◽  
Long-fei Song ◽  
Xin Zhang ◽  
...  

Author(s):  
Yogendra S. Garud

Wall thinning in pressure retaining components, especially due to the flow-accelerated corrosion, has been a significant factor affecting the safety and unplanned system downtimes. On the other hand, overestimating the impact of possible wall thinning often leads to unnecessary or expensive inspections and replacements. The simplified or quick (short-cut) methods of analysis and prediction often lack the requisite degree of accuracy and confidence. This paper presents a few techniques for better analysis of the wall thinning data to address these issues. These techniques make use of the statistical methods, pattern recognition, and optimization to perform a robust data filtering and thinning rate estimation that accounts for measurement uncertainty. The techniques are discussed with application to a large database and an inspection program. The impact of these analytical improvements is presented in comparison with results of the simplified method of analysis. The results include both the margin on remaining life and the projected wall thinning rates, with implications for inspections.


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