Aurora Simulation Suite for Fusion Research

• Aurora Simulation Suite is an open-source package for modeling particle transport, neutral dynamics, and radiation in magnetically confined plasmas.
• It integrates multi-language interfaces and a Python library to facilitate atomic rate calculations, radiation predictions, and experimental diagnostics.
• The suite employs superstaging for computational efficiency and interfaces with SOLPS-ITER and OMFIT to validate and benchmark fusion models.

Aurgia Simulation Suite refers to Aurora, an open-source package designed for the modeling of particle transport, neutral dynamics, and radiation phenomena in magnetically confined plasma environments, principally within the context of magnetic confinement fusion research. Aurora facilitates computationally intensive simulations of impurity transport, radiation power balance, and spectroscopic analysis, offering a modern interface and integration capabilities suitable for high‐performance computing workflows. The package encompasses mechanisms to streamline complex atomic physics calculations and links with experimental and modeling tools for expanded analysis capabilities.

1. Particle Transport Modeling in Magnetically Confined Plasmas

Aurora enables detailed simulation of 1.5D impurity transport processes in magnetically confined fusion plasmas. This class of modeling is crucial for predicting the behavior of non-fuel species, optimizing confinement, and supporting the inference of transport coefficients. The impurity transport modeling incorporates atomic rates and radiative processes, interfacing with both experimental datasets and complementary simulation frameworks. A plausible implication is that Aurora can be employed for transport coefficient inference via synthetic diagnostic routines across multiple plasma regimes.

2. Multi-Language Interface and Integration with High-Performance Computing

A distinguishing feature of Aurora is its contemporary multi-language interface, which permits the suite to be embedded within diverse computational environments. This cross-language design allows for seamless connection to high-performance computing infrastructures, facilitating large-scale simulations necessary for fusion research. This suggests Aurora can leverage compiled languages for computational kernels while offering accessibility through scripting languages for workflow management.

3. Python Library for Atomic Rates and Radiation Predictions

Aurora’s user-oriented Python library provides direct access to atomic rates sourced from the Atomic Data and Atomic Structure database and additional repositories. The library simplifies the interaction required for both equilibrium and time-dependent radiation predictions, supporting practical analysis for power balance calculations and spectroscopic diagnostics. The capacity to access multiple databases enhances flexibility and scientific utility across fusion devices and impurity species. A plausible implication is that routine spectroscopic analysis and radiative power predictions can be executed programmatically using Aurora’s Python API.

4. Superstaging Approximation for Computational Efficiency

Aurora implements the superstaging approximation, grouping charge states of complex ions to significantly reduce computational cost—an essential strategy for simulations involving high-$Z$ impurities with numerous ionization states. The demonstrated wide applicability of this approach within Aurora’s forward modeling makes it viable for large-scale studies where detailed atomic resolution would be prohibitive. This suggests that users can balance between model accuracy and computational tractability by tuning superstaging granularity according to the physics regime under investigation.

5. Neutral Particle Analysis and SOLPS-ITER Interfacing

Aurora contains tools to facilitate analysis of neutral particles using both experimental spectroscopic data and outputs from other simulation codes. Its interfacing capabilities extend to integration with SOLPS-ITER, a leading multi-fluid edge plasma solver, enabling comparative studies and validation. Notably, Aurora has been leveraged to assess the role of charge exchange mechanisms, establishing that such processes are unlikely to substantially alter total radiated power from the ITER core during high-performance operation. A plausible implication is that Aurora can be applied for cross-validation and benchmarking studies among leading edge and core modeling codes.

6. ImpRad Module and OMFIT Framework Integration

Aurora’s functionality is extended by the ImpRad module within the OMFIT framework, supporting experimental analysis and transport inference across diverse devices. Through this integration, Aurora is embedded into broader plasma modeling workflows, facilitating the analysis of impurity transport and radiation across tokamaks and other fusion experiments. A plausible implication is that users of OMFIT can transparently employ Aurora routines within multi-code analysis chains, enhancing the suite’s adoption and specificity of diagnostic predictions.

7. Scope, Applicability, and Computational Considerations

The suite provides a consistent platform for both forward modeling and experimental analysis, adaptable to a wide variety of device configurations and scientific inquiries in fusion plasma research. By supporting parallel workflows and interfacing with canonical atomic data sources, Aurora addresses computational scalability and reproducibility. A plausible implication is that Aurora embodies an extensible foundation for future diagnostic, theoretical, and predictive studies in high‐performance magnetically confined plasma systems, subject to further advancements in multi-physics integration and data handling.