Analysis of the Preliminary Bunker Shielding Design with a Mix of Populated and Unpopulated Neutron Beamlines at the Spallation Neutron Source Second Target Station
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We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.
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The importance of actinides for science, technology, and medicine is widely recognized and does not require a lengthy introduction. However, the fundamental understanding of actinides and their chemical and physical properties lags behind the knowledge of most other elements in the periodic table. Most actinides (89 ≤ Z ≤ 103) are man-made and were produced in particle accelerators or nuclear reactors in the second half of the 20th century. Although trends in bonding and electronic structure are well characterized and documented across the periodic table, fundamental questions remain regarding the chemistry and physics of the actinides. This lack of knowledge is largely because most isotopes of actinides are intensely radioactive, which greatly increases the difficultly in handling them.
SOURCES-4C has some significant deficiencies that are mostly due to problems with the default data libraries that it uses. Some research groups have modified these libraries to fix most of the problems, but not all of them. Adding modern data libraries to SOURCES-4C is one of the most promising approaches to resolving these issues. Other promising options include using either NEDIS or a GEANT4 tool. Both of these codes approximately match the accuracy of the modified SOURCES-4C code. The NEDIS code is Russian. The GEANT4 tool might be computationally slow and it might also no longer be actively maintained. There are a handful of other (α,n) codes, but they are not accurate.
The SNS PPU project goals were to design, build, install and test the equipment necessary to double the accelerator power from 1.4 MW to 2.8 MW and to deliver a 2.0 MW qualified target. PPU also included the provision of a stub-out in the SNS accumulator-ring-to-target tunnel to facilitate a rapid connection to a new proton beamline for the Second Target Station (STS) project. The power capability was doubled by increasing the proton beam energy by 33% and the peak beam current by 50%, relative to pre-PPU accelerator performance. The project also included modifications to some buildings and services. The PPU project accomplished the energy upgrade by fabricating and installing new superconducting radiofrequency (RF) cryomodules, with supporting RF equipment, in the existing linac tunnel and klystron gallery, respectively. The High Voltage Converter Modulators (HVCM) and klystrons for some of the existing installed RF equipment were upgraded to handle the higher beam current. The increased beam power of 2 MW on the First Target Station (FTS) was enabled by the addition of a new high-volume gas injection system for pressure pulse and cavitation mitigation in the mercury target and a redesigned mercury target vessel.
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