polito.it
Politecnico di Torino (logo)

Investigation of a saturation rule for experimental fluxes using a Neural Network approach

Michele Domenico Fasciana

Investigation of a saturation rule for experimental fluxes using a Neural Network approach.

Rel. Fabio Subba. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Energetica E Nucleare, 2019

[img]
Preview
PDF (Tesi_di_laurea) - Tesi
Document access: Anyone
Licenza: Creative Commons Attribution Non-commercial No Derivatives.

Download (4MB) | Preview
Abstract:

An investigation on the saturation rule of the Trapped Gyro-Landau Fluid (TGLF) quasi-linear turbulent transport model has been performed. The turbulent fluxes obtained with TGLF was compared with the experimental fluxes based on experiment performed with DIII-D tokamak, at DIII-D National Fusion Facility. The gyrokinetic transport solvers are powerful models that solves non-linear simulations to inform and help the researchers involved in thermonuclear fusion development. One of the limitations of these code is the computational cost. To face this problem, reduced models have been developed. TGLF is a reduced model that compute a saturation rule calibrated to fit a set of nonlinear gyrokinetic simulation. A neural network based model for the TGLF saturation rule that has been developed to links the quasi-linear quantities computed with TGLF to the experimental fluxes. A New model of the saturation rule was find that better reproduce the experimental fluxes.

Relators: Fabio Subba
Academic year: 2018/19
Publication type: Electronic
Number of Pages: 67
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Energetica E Nucleare
Classe di laurea: New organization > Master science > LM-30 - ENERGY AND NUCLEAR ENGINEERING
Aziende collaboratrici: Officina Meccanica Prina snc
URI: http://webthesis.biblio.polito.it/id/eprint/10228
Modify record (reserved for operators) Modify record (reserved for operators)