A Preliminary Study on the CLAM Steel Composition Optimization Based on Extreme Learning Machine

Abstract

Material problem is one of the key issues for the realization of fusion energy. Reduced Activation Ferritic/Martensitic steels have been considered to be the primary candidate structural material for fusion DEMO reactors and the first fusion power plants. China low activation martensitic steel (CLAM) has been chosen as the primary structure material in the designs of FDS series PbLi blankets for fusion reactors, CN helium cooled ceramic breeder test blanket module for ITER, liquid blanket of China fusion engineering test reactor (CFETR). In this work, optimization of CLAM steel composition was performed based on extreme learning machine method. The results showed that CLAM steel with Ta content of 0.18–0.20 % had better tensile property at the temperature of 350–550 °C, which provided references for the optimization of CLAM steel composition.