Stress intensity factors for external corrosions and cracking of buried cast iron pipes

Akhi, Atika Hossain; Dhar, Ashutosh Sutra

doi:10.1016/j.engfracmech.2021.107778

Cited by 15 publications

(7 citation statements)

References 21 publications

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“…Ten years later, Wang and Lamber [35] validated the 3D FEA results of the K factor obtained by Raju and Newman [34] for axial outside surface cracks in pressurized cylinders and developed closed-form solutions of the K factor for a thin-wall pipe with an inside radius to wall thickness ratio Ri/t = 10 based on the FEA results and the weight function method. Recently, with the modern FEA technique, Akhi and Dhar [36] obtained more accurate FEA results of the K factor that verified the accuracy of the FEA outcomes of the K factor obtained by Raju and Neman [34].…”

Section: Introductionsupporting

confidence: 63%

Fatigue Life Models for Pipelines Containing Dents and Gouges

Zhu¹,

WIERSMA²

2022

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This report is furnished to Pipeline Research Council International, Inc. (PRCI) under the terms of PRCI contract PR-244-213803, between PRCI and Savannah River National Laboratory (SRNL) managed by Battelle Savannah River Alliance (BSRA). The contents of this report are published as received from SRNL. The opinions, findings, and conclusions expressed in the report are those of the authors and not necessarily those of PRCI, its member companies, or their representatives. Publication and dissemination of this report by PRCI should not be considered an endorsement by PRCI or SRNL or BSRA, or the accuracy or validity of any opinions, findings, or conclusions expressed herein.In publishing this report, PRCI and SRNL (or BSRA) make no warranty or representation, expressed or implied, with respect to the accuracy, completeness, usefulness, or fitness for purpose of the information contained herein, or that the use of any information, method, process, or apparatus disclosed in this report may not infringe on privately owned rights. PRCI and SRNL assume no liability with respect to the use of, or for damages resulting from the use of, any information, method, process, or apparatus disclosed in this report. By accepting the report and utilizing it, you agree to waive any and all claims you may have, resulting from your voluntary use of the report, against PRCI and SRNL.

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Section: Introductionsupporting

confidence: 63%

Fatigue Life Models for Pipelines Containing Dents and Gouges

Zhu¹,

WIERSMA²

2022

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“…Ten years later, Wang and Lamber [9] validated the 3D FEA results of the K factor obtained by Raju and Newman [8] for axial outside surface cracks in cylinders and developed a closed-form solution of the K factor for a thin-wall pipe with one inside radius to wall thickness ratio Ri/t=10 in reference to the FEA results and using the weight function method. Recently, with the modern FEA simulations, Akhi and Dhar [10] obtained more accurate FEA results of the K factor and verified the accuracy of the FEA data of the K factor obtained by Raju and Neman [8].…”

Section: Introductionsupporting

confidence: 61%

Data-Driven Stress Intensity Factor Solutions for Axial Outside Surface Cracks in Thick-Wall Cylinders

Zhu¹,

Duncan²,

Zhu³

2022

Volume 4A: Materials and Fabrication

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Crack assessment relies on the linear elastic or elastic-plastic fracture mechanics that requires calculation of stress intensity factor, K, in the fitness for service codes, such as API 579 and ASME BPVC Section XI. For a surface crack in a cylinder, the K calculation becomes calculating the influence coefficients G0 and G1 of K in those codes. API 579 provided accurate tabular data of G0 and G1 for selected cylinder sizes (t/Ri), crack aspect ratios (a/c), crack depths (a/t), and crack tip locations. Recently, the curve-fit solutions of G0 and G1 were obtained for surface cracks at the deepest and surface points. For an arbitrary cylinder size, however, three-parameter interpolations are still needed to estimate the G0 and G1. To avoid performing the complex interpolation, this paper adopts the state-of-the-art machine learning technology to develop data-driven K solutions based on the tabular data of G0 and G1 given in API 579 for axial outside semi-elliptical surface cracks in thick-wall cylinders at the deepest and surface points. The machine learning method utilizes an artificial neural network (ANN), activation function, and optimal learning algorithm to learn and to determine G0 and G1 as a function of the cylinder size (t/Ri), aspect ratio (a/c), and crack depth (a/t) for axial outside surface cracks at the deepest and surface points. The proposed data-driven solutions of G0 and G1 are validated by available curve-fit solutions for the axial outside surface cracks.

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“…n+1 n reflects the singularity of stress field at the crack tip, where PR/t is the average hoop stress of an uncracked vessel under internal pressure, the standard atmospheric pressure P 0 0.1MPa. Q denotes the shape factor for an elliptical crack, which is the square of the complete elliptic integral of the second kind and is approximated by Raju and Newman, and Akhi and Dhar (Raju and Newman, 1982;Akhi and Dhar, 2021)…”

Section: Basic Concepts 3d J-integralmentioning

confidence: 99%

“…Among the many kinds of surface flaws on pressure vessels, a surface crack is the most harmful and common, which seriously affects the safety of the equipment (China Special Equipment Inspection & Research Institute, 2021). To evaluate the influence of cracks on structural strength and to quantify the strength of stress fields at the crack tip, fracture parameters such as critical load (Ge et al, 2005) and stress intensity factor K (Nabavi and Shahani, 2008;Akhi and Dhar, 2021) are employed to establish the corresponding fracture criteria, which can deal well with the brittle fracture of containers made of high-strength materials.…”

Section: Introduction Safety Assessment Of Pressure Vessels With Crackmentioning

confidence: 99%

A safety assessment approach to pressure vessels based on machine learning

Zhang

Shi

et al. 2022

Front. Mater.

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The safety assessment of a pressure vessel with a surface crack is an important part of the safety assessment of engineering equipment. However, the existing methods are mostly based on the assumption of plane specimens and the K criterion applicable to brittle fracture, which may lead to unacceptable errors when applied to a fracture problem in an elastoplastic pressure vessel. In this article, based on the finite element method (FEM) and artificial neural network (ANN), the elastic-plastic three-dimensional J-integral of a crack tip in a pressure vessel with an axial semi-elliptic crack on the surface under the loading of internal pressure is studied. First, the influence of the vessel geometry, the crack size, and internal pressure on the three-dimensional J-integral is analyzed. Second, the machine learning dataset is constructed based on the results of 1,200 cases of FEM calculation; then ANNs are used to discover the potential relationship between multiple parameters and the three-dimensional J-integral. The results show that the neural network constructed in this article can well predict the elastoplastic three-dimensional J-integral of a pressure vessel surface crack.

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Stress intensity factors for external corrosions and cracking of buried cast iron pipes

Cited by 15 publications

References 21 publications

Fatigue Life Models for Pipelines Containing Dents and Gouges

Fatigue Life Models for Pipelines Containing Dents and Gouges

Data-Driven Stress Intensity Factor Solutions for Axial Outside Surface Cracks in Thick-Wall Cylinders

A safety assessment approach to pressure vessels based on machine learning

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