Microsc. Microanal. 23, 376–384, 2017 doi:10.1017/S1431927617000162
© MICROSCOPY SOCIETY OF AMERICA 2017
On the Analysis of Clustering in an Irradiated Low Alloy Reactor Pressure Vessel SteelWeld
Kristina Lindgren,1,* Krystyna Stiller,1 Pål Efsing,2 and Mattias Thuvander1
1Department of Physics, Chalmers University of Technology, Göteborg SE 412 96, Sweden 2Vattenfall Ringhals AB, Väröbacka SE 430 22, Sweden
Abstract: Radiation induced clustering affects the mechanical properties, that is the ductile to brittle transition temperature (DBTT), of reactor pressure vessel (RPV) steel of nuclear power plants. The combination of low Cu and high Ni used in some RPV welds is known to further enhance the DBTT shift during long time operation. In this study, RPV weld samples containing 0.04 at% Cu and 1.6 at% Ni were irradiated to 2.0 and 6.4×1023 n/m2 in the Halden test reactor. Atom probe tomography (APT) was applied to study clustering of Ni, Mn, Si, and Cu. As the clusters are in the nanometer-range, APT is a very suitable technique for this type of study. From APT analyses information about size distribution, number density, and composition of the clusters can be obtained. However, the quantification of these attributes is not trivial. The maximum separation method (MSM) has been used to characterize the clusters and a detailed study about the influence of the choice ofMSMcluster parameters, primarily on the cluster number density, has been undertaken.
Key words: reactor pressure vessel steel, clustering, atom probe tomography, irradiation damage, maximum separation method
INTRODUCTION
The reactor pressure vessel (RPV) of nuclear power plants is subjected to both heat and neutron irradiation, leading to gradual changes in properties, a phenomenon known as aging. The effects of the aging become important after long operating times as the RPV welds become more and more embrittled. The embrittlement is connected to the formation of various features on the nanometer scale, hindering dislocation glide when the material is subjected to stress (Odette & Lucas, 1998). Nanometer-sized clusters contain- ing Cu, Ni, Mn, and Si form, depending on the composition of the steel. For low Cu high Ni steels, clusters with small amounts of Cu, or even without Cu, form (Miller et al., 2009, 2013; Meslin et al., 2010b). The small scale of these features makes them suitable for atom probe tomography (APT) studies. This allows for quantitative measurements of important parameters, like number density, size distribution, and composition of the clusters that in turn influence the mechanical properties of the steel. Some different techniques to identify clusters using APT
have been developed during the last two decades, and a review of methods can be found in reference (Marquis & Hyde, 2010). The maximum separation method (MSM) is one method that identifies clusters based on the distances between solute atoms (Hyde & English, 2000; Heinrich et al., 2003; Vaumousse et al., 2003). The parameters might need to be chosen for each system, dependent on the cluster size and composition, and the matrix composition. The detection
*Corresponding author.
kristina.lindgren@
chalmers.se Received June 30, 2016; accepted January 14, 2017
efficiency and background noise might also affect the choice of parameters (Gault et al., 2012). The MSM is a widely used method for cluster identification due to it being fast and easy to use and that it is implemented in the most commonly used software. When applying the MSM, five parameters usually need
to be considered. The maximum distance between two solute atoms considered to form a cluster is denoted dmax. The parameter Nmin is the smallest number of solute atoms regarded to be a cluster and not random fluctuations. The order, N, denotes how many solute atoms that need to be located within the distance dmax. Most commonly, N = 1is used, that is only the nearest neighboring atom distance is taken into account. Higher orders can give more robust results, avoiding effects from small density fluctuations, but might not be suitable for small clusters (Stephenson et al., 2007). When measuring the composition of the cluster, the envelope distance L is the distance around the solute atoms defining which nonsolute atoms that are to be included in the cluster. Finally, E is the erosion distance; it removes the thin shell of atoms outside the cluster that would otherwise be counted as belonging to the cluster due to the introduction of L. As a consequence, the measured number density of clusters is only influenced by the choice of dmax,Nmin, and N, whereas the choice of E and L influences size and composi- tion of the clusters. The choice of the value for the parameter dmax is a trade-
off (Vaumousse et al., 2003). If dmax is too large, features that might be random matrix fluctuations are considered to be clusters, which increases the measured cluster number density. However, a too small value of dmax will not be able to identify all clusters.Nmin is also vital and the appropriate value
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