Modeling of Particle Transport, Neutrals and Radiation in Magnetically-Confined Plasmas with Aurora

TL;DR

Aurora is an open-source simulation package for modeling particle transport, neutrals, and radiation in magnetically confined fusion plasmas, enabling detailed analysis and inference of plasma behavior with a user-friendly interface.

Contribution

It introduces Aurora's multi-language, Python-based framework with advanced features like superstaging approximation and integration with experimental data for comprehensive plasma analysis.

Findings

Superstaging approximation reduces computational cost for complex ions.

Charge exchange unlikely affects ITER core radiated power during high performance.

Aurora successfully interfaces with SOLPS-ITER and supports transport inference across devices.

Abstract

We present Aurora, an open-source package for particle transport, neutrals and radiation modeling in magnetic confinement fusion plasmas. Aurora's modern multi-language interface enables simulations of 1.5D impurity transport within high-performance computing frameworks, particularly for the inference of particle transport coefficients. A user-friendly Python library allows simple interaction with atomic rates from the Atomic Data and Atomic Structure database as well as other sources. This enables a range of radiation predictions, both for power balance and spectroscopic analysis. We discuss here the superstaging approximation for complex ions, as a way to group charge states and reduce computational cost, demonstrating its wide applicability within the Aurora forward model and beyond. Aurora also facilitates neutral particle analysis, both from experimental spectroscopic data and other simulation codes. Leveraging Aurora's capabilities to interface SOLPS-ITER results, we demonstrate that charge exchange is unlikely to affect the total radiated power from the ITER core during high performance operation. Finally, we describe the ImpRad module in the OMFIT framework, developed to enable experimental analysis and transport inferences on multiple devices using Aurora.