The application of classification methods in a data driven investigation of the JET L–H transition

Understanding the processes which establish the H-mode edge transport barrier (ETB) and the scaling of those processes with the plasma properties local to the plasma edge is of critical importance for optimizing the performance of power-station scale fusion plasmas. In this paper, data from 67 JET pulses were assembled and classified by confinement mode. A neural network classification technique was applied to identify the nature of the dependence of the L–H boundary on plasma parameters local to the plasma edge. Strong dependences on Te, ne, B and a weak dependence on q80 were found. In applying the neural network model as a confinement mode identification tool, the correct operating mode was identified for 98.86% of the time slices in a test data set. Using an extended data set the boundary predictions from five theoretical ETB models were evaluated. These models consider a range of different physical processes for the ETB. One model was clearly rejected. While other models proved more favourable, none of the tested models robustly described the JET L–H boundary.