Abstract

The standard master curve approach has the major limitation, which is only applicable to homogeneous datasets. In nature, steels are macroscopically inhomogeneous and thus the fracture toughness has larger scatters than expected by a conventional master curve approach. RPV steel has different fracture toughness with varying distance from the inner surface of the wall. Regarding this, a clear tendency was reported in that the toughness extracted near the surface had to be higher than in the center region due to the higher quenching rate at the surface (deterministic material inhomogeneity). On the other hand, the T0 value itself behaves like a random parameter when the datasets have a large scatter due to the datasets consisting of several different materials such as welding region (random inhomogeneity). In the present paper, four regions, the surface, 1/8T, 1/4T and 1/2T, were considered for fracture toughness specimens of KSNP (Korean Standard Nuclear Plant) SA508 Gr. 3 steel to provide deterministic material inhomogeneity and random inhomogeneity effect. Specimens were extracted from these four regions and fracture toughness tests were performed at various temperatures in the transition region. Several concepts were provided for the master curve of inhomogeneous materials such as a bimodal and random inhomogeneous master curve scheme, and among them, the bimodal master curve analyses were reviewed and compared with a conventional master curve approach to find the random inhomogeneity. The bimodal master curve considering inhomogeneous materials provides better description of scatter in fracture toughness data than conventional master curve analysis, but it is unclear to provide evidence that the bimodal analysis lines follow the data more closely than the conventional master curve analysis.

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