Speaker
Description
Understanding plasma behavior throughout the entire duration of a pulse is critical for achieving the objectives of ITER and future fusion devices. Plasma parameters such as temperature, density, current profiles, and impurity content evolve dynamically during a discharge, influencing key aspects of performance including energy confinement, stability, and fusion power production. Analyzing plasma performance during the pulse—rather than relying solely on post-pulse data—provides essential insights into transient phenomena, transport processes, and the interplay of heating and fueling. This in-pulse analysis is vital for guiding control strategies for sustained, high-performance fusion plasmas during an experimental session.
In this work, we present the performance evaluation of Apache Airflow as a workflow management tool for in-pulse analysis of plasma performance at ITER. Given the complexity and interdependence of data analysis tasks required to extract meaningful insights from plasma discharges—such as data extraction, pre-processing, profile reconstruction, and physics-based computations—there is a clear need for a robust and scalable orchestration framework. To this end, we have developed a mock workflow composed of artificial, interdependent actors that emulate a realistic plasma analysis pipeline. This synthetic workflow mimics key features of an ITER plasma performance analysis chain, including task dependencies, data flow, and execution timing. By executing and monitoring this mock workflow using Apache Airflow, we assess key performance metrics such as task scheduling efficiency, failure handling, scalability, and resource management. Our results provide insights into the potential of workflow orchestration tools like Airflow to support the demanding in-pulse analysis requirements of ITER, offering a pathway toward more automated, reproducible, and robust analysis pipelines in future fusion experiments.
We also demonstrate the usage of ParaView as a key visualization and data exploration tool, to support both static and interactive visualization of plasma parameters. By embedding ParaView scripts and tasks directly into the workflow, we can enable the generation of detailed visual outputs—such as 3D renderings of magnetic flux surfaces, current and temperature profiles, and diagnostic signal mappings—at various stages of the analysis. In addition, we can also leverage ParaView to ensure that complex, high-dimensional plasma data can be explored dynamically rather than only through static plots, empowering researchers to uncover subtle features such as asymmetries, localized instabilities, and temporal evolutions that might otherwise be overlooked. Users can manipulate views, apply filters, and adjust color maps interactively to investigate the spatial and temporal structure of plasma events, providing timely feedback on the quality and relevance of the analysis results. In addition, ParaView's ability to handle large, multi-dimensional datasets is critical in the context of ITER, where vast amounts of data must be processed, visualized, and interpreted quickly to inform operational decisions and advance scientific understanding.
By integrating ParaView as a modular, scriptable actor, we ensure that visualization tasks can be executed in parallel with other analysis tasks without introducing bottlenecks. This integration facilitates a workflow where data exploration is not an afterthought but a core component of the analysis pipeline, supporting transparent, reproducible, and actionable plasma performance insights for ITER and future fusion devices.
| Speaker's email address | paulo.abreu@iter.org |
|---|---|
| Speaker's Affiliation | ITER Organization |
| Member State or International Organizations | ITER Organization |
