STUDY ON COLLAPSE CHARACTERISTICS OF CYLINDER-TO-CYLINDER INTERSECTIONS BY INELASTIC FINITE ELEMENT ANALYSIS
It is known that the collapse strength of complex three-dimensional structures cannot be evaluated accurately with elastic analysis, and more accurate results require the use of inelastic analysis. A typical example is cylinder-to-cylinder intersection. In this study, the relationship of collapse lo...
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Veröffentlicht in: | Atsuryoku gijutsu 2010/05/25, Vol.48(3), pp.114-121 |
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Format: | Artikel |
Sprache: | eng ; jpn |
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Zusammenfassung: | It is known that the collapse strength of complex three-dimensional structures cannot be evaluated accurately with elastic analysis, and more accurate results require the use of inelastic analysis. A typical example is cylinder-to-cylinder intersection. In this study, the relationship of collapse loads and local primary membrane stresses of cylinder-to-cylinder intersections was examined. First, elastic analysis on the cylinder-to-cylinder intersections with various combinations of diameter and thickness under internal pressure was conducted. The local primary membrane stress (PL) obtained from the analysis was normalized by the general primary membrane stress (Pm) . The ratio of PL⁄Pm is defined as Stress Intensification Factor (SIF) which was directly influenced by the geometries. Secondly, limit load analysis was conducted on the same structures as elastic analysis and collapse pressure was obtained. The Collapse Strength Reduction Factor (CSRF) defined as the ratio of the run pipe collapse pressure to the cylinder-to-cylinder collapse pressure was proposed. The CSRF was also directly influenced by the geometries. Comparing the results of SIF with CSRF, it is clear that the evaluation by method is overly-conservative and the proposed concept of CSRF method provides more accurate evaluation for the cylinder-to-cylinder intersections. Furthermore, these analysis results can be extended to the reinforced intersections. SIF or CSRF values on the reinforced intersection were predicted from the basic data. |
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ISSN: | 0387-0154 1347-9598 |
DOI: | 10.11181/hpi.48.114 |