International Journal of Naval Architecture and Ocean Engineering

The Society of Naval Architects of Korea (대한조선학회)
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A new method of predicting hotspot stresses for longitudinal attachments with reduced element sensitivities

  • Li, Chun Bao (Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education) ;
  • Choung, Joonmo (Department of Naval Architecture and Ocean Engineering, Inha University)
  • Received : 2021.01.01
  • Accepted : 2021.04.14
  • Published : 2021.11.30
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Abstract

For the complicated structural details in ships and offshore structures, the traditional hotspot stress approaches are known to be sensitive to the element variables of element topologies, sizes, and integration schemes. This motivated to develop a new approach for predicting reasonable hotspot stresses, which is less sensitive to the element variables and easy to be implemented the real marine structures. The three-point bending tests were conducted for the longitudinal attachments with the round and rectangular weld toes. The tests were reproduced in the numerical simulations using the solid and shell element models, and the simulation technique was validated by comparing the experimental stresses with the simulated ones. This paper considered three hotspot stress approaches: the ESM method based on surface stress extrapolation, the Dong's method based on nodal forces along a weld toe, and the proposed method based on nodal forces perpendicular to an imaginary vertical plane at a weld toe. In order to study the element sensitivities of each method, 16 solid element models and 8 shell element models were generated under the bending and tension loads, respectively. The element sensitivity was analyzed in terms of Stress Concentration Factors (SCFs) in viewpoints of two statistical quantities of mean and bias with respect to the reference SCFs. The average SCFs predicted by the proposed method were remarkably in good agreement with the reference SCFs based on the experiments and the ship rules. Negligibly small Coefficients of Variation (CVs) of the SCFs, which is measure of statistical bias, were drawn by the proposed method.

Keywords

Acknowledgement

This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Industrial Facilities & Infrastructure Research Program, funded by Korea Ministry of Environment(MOE)(146836). Authors appreciated Independent Innovation Foundation of Wuhan University of Technology (No. 3120621112) and Wuhan University of Technology Project (No. 612004951), China.

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