Speaker
Carrie Elliott
(ORNL)
Description
C. Elliott, W. Blokland, D. Brown, B. Cathey, B. Maldonaldo Puente, C. Peters, K. Rajput, J. Rye, M. Schram, S. Thomas, A. Zhukov
Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
*Thomas Jefferson National Laboratory, Newport News, VA, 23606, USA
The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL), a high-power H- linear accelerator, is increasing its power capability from 1.4 to 2.8 MW and beam energy from 1 to 1.3 GeV through a long-term project called the Proton Power Upgrade (PPU). With an increase in power and energy, there is even more emphasis on reducing errant beam loss and residual activation of accelerator equipment. The Automated Beam Loss Tuning (ABLT) application aims to outperform the operators’ by-hand tuning for changes from the upgrades and for typical day-to-day variances. Included in the renewal of the Machine Learning for Improving Accelerator and Target Performance Grant (FWP: LAB-20-22), the Beam Loss Optimization (BLO) use case will employ a newly acquired DGX system and Reinforcement Learning (RL) methods to tune accelerator beam losses during neutron production. Utilizing machine learning (ML) for production use has many operational requirements, including regular testing and training, monitoring tools and safeguards, and high-level configuration control approvals. For a collaboration like this to succeed involves many different skill sets and groups: Operations, Accelerator Physics, configuration control management, and ML experts. This talk aims to highlight how the project started, the operational perspective on ML in the control room, the infrastructure needed moving forward, the progress so far, and the outlook towards full deployment at the SNS.
*ORNL is managed by UT-Battelle, LLC, under contract DE-AC05- 00OR22725 for the U.S. Department of Energy.
| Primary Keyword | ML-based optimization |
|---|
Primary author
Carrie Elliott
(ORNL)
