In:
Frontiers in Materials, Frontiers Media SA, Vol. 9 ( 2022-11-2)
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
2296-8016
DOI:
10.3389/fmats.2022.1051890
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2022
detail.hit.zdb_id:
2759394-0
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