Application of Gaussian process regression to plasma turbulent transport model validation via integrated modelling

TitleApplication of Gaussian process regression to plasma turbulent transport model validation via integrated modelling
Publication TypeJournal Article
Year of Publication2019
AuthorsA. Ho, J. Citrin, F. Auriemma, C. Bourdelle, F.J Casson, H.T Kim, P. Manas, G. Szepesi, H. Weisen, JET Contributors
JournalNuclear Fusion
Volume59
Issue5
Pagination056007
Abstract

This paper outlines an approach towards improved rigour in tokamak turbulence transport model validation within integrated modelling. Gaussian process regression (GPR) techniques were applied for profile fitting during the preparation of integrated modelling simulations allowing for rigourous sensitivity tests of prescribed initial and boundary conditions as both fit and derivative uncertainties are provided. This was demonstrated by a JETTO integrated modelling simulation of the JET ITER-like-wall H-mode baseline discharge #92436 with the QuaLiKiz quasilinear turbulent transport model, which is the subject of extrapolation towards a deuterium–tritium plasma. The simulation simultaneously evaluates the time evolution of heat, particle, and momentum fluxes over  ~10 confinement times, with a simulation boundary condition at rho tor=0.85. Routine inclusion of momentum transport prediction in multi-channel flux-driven transport modelling is not standard and is facilitated here by recent developments within the QuaLiKiz model. Excellent agreement was achieved between the fitted and simulated profiles for n e , T e , T i , and omega tor within 2x, but the simulation underpredicts the mid-radius T i and overpredicts the core n e and T e profiles for this discharge. Despite this, it was shown that this approach is capable of deriving reasonable inputs, including derivative quantities, to tokamak models from experimental data. Furthermore, multiple figures-of-merit were defined to quantitatively assess the agreement of integrated modelling predictions to experimental data within the GPR profile fitting framework.

DOI10.1088/1741-4326/ab065a
Division

FP

Department

IMT

PID

9b651392bcad55886e0c0848df55a9f0

Alternate TitleNucl. Fusion

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