A reduced-order model to estimate first wall particle and heat fluxes for systems codes

Abstract

Current systems codes (SCs) do not assess the impact of plasma filament parameters and dynamics in the SOL when performing systems-level analyses. Simulation tools based on simplified turbulent transport models could address such need, such as the TOKES code, but these tend to run in timescales that are prohibitive for incorporation in SCs. This work presents a technique to build surrogate models for SCs, obtained with the modes of Reduced-Order Models (ROMs), and is exemplified with results from TOKES. These surrogates were developed with Principal Components Analysis (PCA), multivariate regression against TOKES inputs (filament temperatures, densities, and ejection speeds) and k-fold cross-validation, applied to the results after transformations based on rational powers of the inputs to optimize the prediction capabilities of the surrogates. Selection was performed with a modified Kullback-Leibler Divergence (KLD), and validation, with withheld cases. Main results include validation of the methodology due to the relatively low number of modes needed to represent more than 90% of the data variance, and the data transformation exponents that optimize the regression.