Quasilinear turbulent particle and heat transport modelling with a neural-network-based approach founded on gyrokinetic calculations and experimental data

A novel quasilinear turbulent transport model DeKANIS has been constructed founded on the gyrokinetic analysis of JT-60U plasmas. DeKANIS predicts particle and heat fluxes fast with a neural network (NN) based approach and distinguishes diffusive and non-diffusive transport processes. The original model only considered particle transport, but its capability has been extended to cover multi-channel turbulent transport. To solve a set of particle and heat transport equations stably in integrated codes with DeKANIS, the NN model embedded in DeKANIS has been modified. DeKANIS originally determined turbulent saturation levels semi-empirically based on JT-60U experimental data, but now it can also estimate them using a theory-based saturation rule. The new saturation model is still partly connected to experimental data, but it offers the potential for applying DeKANIS independently of the device.

Automated summary

The novel quasilinear turbulent transport model DeKANIS is based on the gyrokinetic analysis of JT-60U plasmas, predicting particle and heat fluxes fast with a neural network (NN) approach and distinguishing diffusive and non-diffusive transport processes. Originally focusing only on particle transport, the model has been extended to cover multi-channel turbulent transport. With the new saturation model, DeKANIS can now be applied independently of the device.