<p>Materials to be used in thermal power plants have to resist creep deformation for time periods up to 30 years. The role of alloying elements for creep strength of 9-12% Cr steels is analysed. The creep strength in these steels relies on minor additions of alloying elements. Precipitates give rise to the main strengthening and remaining elements produce solid solution hardening. Nucleation, growth and coarsening of particles are predicted by thermodynamic modelling. Phase fractions and size distributions of M₂₃C₆ carbides, MX carbonitrides and Laves phase are presented. The size distributions are needed in order to determine the particle hardening during creep. At elevated temperatures the climb mobility is so high that the dislocations can climb across particles instead of passing by making Orowan loops.</p><p>By solving Fick's second law the concentration profile around a moving dislocation can be determined. The results show an accumulation of solutes around the dislocation that slows down dislocation movement. When Laves phase grows a decrease in creep strength is observed due to a larger loss in solid solution hardening than strength increase by particle hardening. Solid solution hardening also gives an explanation of the low dislocation climb mobility in 9-12% Cr steels.</p><p>Three different dislocation types are distinguished, free dislocations, immobile dislocation and immobile boundary dislocations. This distinction between types of dislocations is essential in understanding the decreasing creep with strain during primary creep. The empirical relation with subgrain size inversely proportional to stress has been possible to predict. The total creep strength can be predicted by adding the contribution from individual mechanisms.</p>

Explaining the work-hardening behavior of crystalline materials and the size dependent plasticity has been a long lasting problem. Plastic deformation mainly arises from the collective motion of dislocations. Although individual dislocation processes are well studied, the study of the overall effects of these processes was challenging before the emergence of computer modeling. Of the computer simulation techniques, discrete dislocation dynamics (DDD) is the most suitable method to model thin films at the micron scale and below. This method allows us to study the quantitative effects of certain mechanisms. / Engineering and Applied Sciences

Materials to be used in thermal power plants have to resist creep deformation for time periods up to 30 years. The role of alloying elements for creep strength of 9-12% Cr steels is analysed. The creep strength in these steels relies on minor additions of alloying elements. Precipitates give rise to the main strengthening and remaining elements produce solid solution hardening. Nucleation, growth and coarsening of particles are predicted by thermodynamic modelling. Phase fractions and size distributions of M23C6 carbides, MX carbonitrides and Laves phase are presented. The size distributions are needed in order to determine the particle hardening during creep. At elevated temperatures the climb mobility is so high that the dislocations can climb across particles instead of passing by making Orowan loops. By solving Fick's second law the concentration profile around a moving dislocation can be determined. The results show an accumulation of solutes around the dislocation that slows down dislocation movement. When Laves phase grows a decrease in creep strength is observed due to a larger loss in solid solution hardening than strength increase by particle hardening. Solid solution hardening also gives an explanation of the low dislocation climb mobility in 9-12% Cr steels. Three different dislocation types are distinguished, free dislocations, immobile dislocation and immobile boundary dislocations. This distinction between types of dislocations is essential in understanding the decreasing creep with strain during primary creep. The empirical relation with subgrain size inversely proportional to stress has been possible to predict. The total creep strength can be predicted by adding the contribution from individual mechanisms. / QC 20101112