2010
DOI: 10.1016/j.jnucmat.2009.08.009
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Analysis of tensile deformation and failure in austenitic stainless steels: Part II – Irradiation dose dependence

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Cited by 22 publications
(12 citation statements)
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“…50 Figure 4-4 shows some engineering stress-strain curves for irradiated 316 SS and EC316LN SS to analyze irradiation dose dependence. 40 As shown in this figure, most engineering stress-strain curves are impacted by irradiation, exhibiting increases in strength and decreases in elongation. The one exception being 316 SS tested at 288°C, for which radiation effects were minimal.…”
Section: Ss316mentioning
confidence: 85%
“…50 Figure 4-4 shows some engineering stress-strain curves for irradiated 316 SS and EC316LN SS to analyze irradiation dose dependence. 40 As shown in this figure, most engineering stress-strain curves are impacted by irradiation, exhibiting increases in strength and decreases in elongation. The one exception being 316 SS tested at 288°C, for which radiation effects were minimal.…”
Section: Ss316mentioning
confidence: 85%
“…Fig. 14a shows stress-strain curves for SA 316 stainless steel irradiated to several doses <1 dpa in the mixed spectrum HFIR test reactor at 60 8C and tested at room temperature [81]. In addition to the strength increases and loss of ductility and work-hardening, a yield point drop was observed at doses of 0.01 dpa and higher.…”
Section: Radiation Hardening and Embrittlementmentioning
confidence: 98%
“…Stress-strain curves for (a) a solution-annealed 316 stainless steel irradiated to several doses <1 dpa in the mixed spectrum HFIR test reactor at 60 8C and tested at room temperature[81] and (b) a solution-annealed J316 both irradiated in HFIR and tested at 330 8C[82].…”
mentioning
confidence: 99%
“…Fitting this unsaturated damage model to the experimental SS316 tensile data in [22] provides a K value of approximately 990 MPa/ √ dpa, which the authors have rounded up to 1000 MPa/ √ dpa for simplification. The fitting of the data was carried out in MATLAB [17] using the non-linear least squares fit which solves for…”
Section: Finite Element Modellingmentioning
confidence: 99%