Speaker
Description
This study presents a data-driven methodology aimed at enhancing the performance and reliability of the injector at ALS. We show a data acquisition system for capturing and analyzing the parameters affecting the injection process to find patterns and improve reliability. We analyze the recorded injection parameters to find key correlations and patterns within the multidimensional parameter space, gaining insights into injector dynamics and potential areas for optimizing the injection process. Furthermore, we present first steps towards a parametric digital twin of the ALS injector based on the recorded data to enable more precise predictions of injector behavior, facilitate rapid troubleshooting, and support the development of advanced control strategies.
| Region represented | America |
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| Paper preparation format | LaTeX |
