2023
DOI: 10.3390/app132011446
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Stress Intensity Factors for Pressurized Pipes with an Internal Crack: The Prediction Model Based on an Artificial Neural Network

Patchanida Seenuan,
Nitikorn Noraphaiphipaksa,
Chaosuan Kanchanomai

Abstract: During pipeline operation, internal cracks may occur. The severity around the crack tip can be quantified by the stress intensity factor (KI), which is a linear–elastic fracture mechanics parameter. For pressurized pipes featuring infinitely long internal surface cracks, KI can be interpolated from a function considering pressure, geometry, and crack size, as presented in API 579-1/ASME FFS-1. To enhance KI prediction accuracy, an artificial neural network (ANN) model was developed for such pressurized pipes. … Show more

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Cited by 2 publications
(2 citation statements)
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“…For example, Huang et al [33] used ANNs in the design and geometry selection process of microchannel heat exchangers. Seenuan et al [34] emphasized their usefulness in the structural assessment of pressurized pipes. On the other hand, Tlatelpa Becerro et al [35] drew attention to their usefulness for predicting temperatures in the solar dryer chamber.…”
Section: Introductionmentioning
confidence: 99%
“…For example, Huang et al [33] used ANNs in the design and geometry selection process of microchannel heat exchangers. Seenuan et al [34] emphasized their usefulness in the structural assessment of pressurized pipes. On the other hand, Tlatelpa Becerro et al [35] drew attention to their usefulness for predicting temperatures in the solar dryer chamber.…”
Section: Introductionmentioning
confidence: 99%
“…At present, there are a lot of studies on single cracks in pipelines [3][4][5][6][7][8][9][10][11][12][13][14][15], but pipeline cracks often appear in the form of multiple cracks or crack groups. For multiple cracks, the study of crack interactions is of great significance.…”
Section: Introductionmentioning
confidence: 99%