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

TL;DR

This paper demonstrates how Gaussian process regression improves tokamak turbulence transport model validation by providing rigorous profile fitting and sensitivity analysis, leading to better integration of experimental data with simulation models.

Contribution

It introduces a GPR-based approach for profile fitting and sensitivity testing in integrated tokamak modelling, including momentum transport prediction, which is rarely incorporated.

Findings

GPR provides uncertainties for profile fits and derivatives.

Simulation achieved good agreement with experimental profiles within 2σ.

The approach enables deriving reasonable model inputs from experimental data.

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 $\sim10$ 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 $2\sigma$, 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.