Statistical inference of anomalous thermal transport with uncertainty quantification for interpretive 2-D SOL models

TL;DR

This paper develops a Bayesian workflow for inferring anomalous thermal transport coefficients in boundary plasma simulations, integrating uncertainty quantification to improve interpretability and match experimental data.

Contribution

It introduces a novel Bayesian inference workflow with uncertainty quantification for parameter estimation in 2-D plasma heat transport models.

Findings

Successfully infers electron thermal diffusivity from synthetic and experimental data.

Generates 2-D profiles consistent with 1-D measurements.

Efficiently quantifies uncertainty in transport coefficient estimates.

Abstract

The critical task of inferring anomalous cross-field transport coefficients is addressed in simulations of boundary plasmas with fluid models. A workflow for parameter inference in the UEDGE fluid code is developed using Bayesian optimization with parallelized sampling and integrated uncertainty quantification. In this workflow, transport coefficients are inferred by maximizing their posterior probability distribution, which is generally multidimensional and non-Gaussian. Uncertainty quantification is integrated throughout the optimization within the Bayesian framework that combines diagnostic uncertainties and model limitations. As a concrete example, we infer the anomalous electron thermal diffusivity $\chi_\perp$ from an interpretive 2-D model describing electron heat transport in the conduction-limited region with radiative power loss. The workflow is first benchmarked against synthetic data and then tested on H-, L-, and I-mode discharges to match their midplane temperature and divertor heat flux profiles. We demonstrate that the workflow efficiently infers diffusivity and its associated uncertainty, generating 2-D profiles that match 1-D measurements. Future efforts will focus on incorporating more complicated fluid models and analyzing transport coefficients inferred from a large database of experimental results.