Welded joints have always been critical elements of industrial mechanical structures, often being the source of failures related to the presence of fatigue loads. Although the academic world has presented advanced methodologies for the assessment of local fatigue, such as the Strain Energy Density (SED) approach, which offers high accuracy, their high computational requirements hinder their adoption by the industrial world. This paper introduces a new hybrid methodology, called ENLO-SED, which integrates the SED approach by calculating the Strain Energy Density using the element Nodal load approach (ENLO), with the aim of maintaining high accuracy while significantly reducing the computational effort. The proposed method is validated on a complex case study, representative of a real industrial case, demonstrating a prediction error within 8% compared to the application of the classic SED method. Furthermore, the innovative ENLO-SED approach reduces the meshing and solution times by 15 and 5 times, respectively. These results confirm the robustness, efficiency, and scalability of the method, making it suitable for large-scale industrial applications.

ENLO-SED: an innovative method for large-scale Strain Energy Density (SED) estimation in welded joints using structural stresses derived from Element Nodal LOads (ENLO)

Morettini, Giulia;Lucertini, Simone;Cianetti, Filippo
2025

Abstract

Welded joints have always been critical elements of industrial mechanical structures, often being the source of failures related to the presence of fatigue loads. Although the academic world has presented advanced methodologies for the assessment of local fatigue, such as the Strain Energy Density (SED) approach, which offers high accuracy, their high computational requirements hinder their adoption by the industrial world. This paper introduces a new hybrid methodology, called ENLO-SED, which integrates the SED approach by calculating the Strain Energy Density using the element Nodal load approach (ENLO), with the aim of maintaining high accuracy while significantly reducing the computational effort. The proposed method is validated on a complex case study, representative of a real industrial case, demonstrating a prediction error within 8% compared to the application of the classic SED method. Furthermore, the innovative ENLO-SED approach reduces the meshing and solution times by 15 and 5 times, respectively. These results confirm the robustness, efficiency, and scalability of the method, making it suitable for large-scale industrial applications.
2025
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1609036
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