DisruptionPy: An open-source physics-based Scientific Framework for Disruption Analysis of Fusion Plasmas

Description

Abstract

A key element to ensure steady state operations in magnetically-confined tokamak devices is the prediction and avoidance of disruptions. These are sudden losses of the thermal and magnetic energy stored within the plasma, which can occur when tokamaks operate near stability boundaries or because of hardware anomalies. The energy stored in the plasma and released during disruptions over milliseconds can cause severe damage to plasma-facing components, limiting experimental operations and the device's lifespan [FST2023]. Disruptions still pose a serious challenge to next-generation fusion devices such as ITER or SPARC, which will have to operate near some of the limits of plasma stability to achieve intended performance and will do so at for long and frequent intervals. Fusion science currently lacks first-principle, theoretical solutions to fully predict and avoid disruptions. However, previous work [NF2019, NF2021] has shown the usefulness of machine-learning (ML) algorithms for disruption prevention for both DIII-D and EAST operations. DisruptionPy provides a standardized analysis pipeline across different fusion devices to build ML-ready datasets.

Technical info

Framework

• Fix extension check for IncludedShotlistSetting #518
• Fix erroneous sorting of validity ranges #519
• Sort datasets by shot/time #520
• Dump configuration next to the output #522

Automation

• Fix linting makefile #521

Documentation

• Add snippets for linting and testing #525
• Update references and citations #528
• Update REFERENCES.md with new conference details #530
• Add mdsplus warning to docs #529

Dependencies

• Update deps to Feb 2026 #526