Quasilinear turbulent particle and heat transport modelling with a neural-network-based approach founded on gyrokinetic calculations and experimental data. (7th October 2021)

• Record Type: Journal Article
• Authors: Narita, E.; Honda, M.; Nakata, M.; Yoshida, M.; Hayashi, N.
• Is Part Of: Nuclear fusion. Volume 61:Number 11(2021)
• Publication Date: 2021-10-07
• Subjects: turbulent transport -- gyrokinetic modelling -- neural network -- quasilinear modelling -- integrated transport simulations; Nuclear fusion -- Periodicals; 621.48405
• DOI: 10.1088/1741-4326/ac25be
• Languages: English
• ISSNs: 0029-5515

Abstract

Abstract: 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.