High Power Targetry (HPT) R&D is critical in the context of increasing beam intensity and energy for next generation accelerators. Many target concepts and novel materials are being developed and tested for their ability to withstand extreme beam environments; the HPT R&D Group at Fermilab is developing an electrospun nanofiber material for this purpose. The performance of these nanofiber targets is sensitive to their construction parameters, such as the packing density of the fibers. Lowering the density improves the survival of the target, but reduces the secondary particle yield. Optimizing the lifetime and production efficiency of the target poses an interesting design problem, and in this paper we study the applicability of Bayesian optimization to its solution. We first describe how to encode the nanofiber target design problem as the optimization of an objective function, and how to evaluate that function with computer simulations. We then explain the optimization loop setup. Thereafter, we present the optimal design parameters suggested by the algorithm, and close with discussions of limitations and future refinements.

During the Long Shutdown 2, the two internal dumps were replaced and successfully integrated into the CERN Proton Synchrotron operation to withstand the intense and bright beams for the High-Luminosity LHC. They function as safety devices, designed to swiftly intersect the beam’s trajectory and effectively stop the beam over multiple turns. A significant challenge arises from their limited energy absorption capacity. Previous studies indicate that at the maximum PS beam energy of 26 GeV, only about 7% of the energy is absorbed by the dumps upon their insertion. This study, employing a combination of the FLUKA and SixTrack simulation code chain, evaluates the absorbed dose in downstream elements in view of the projected increase of beam intensities, according to the LHC injector upgrade parameters, and explores the feasibility and potential benefits of implementing shielding as a mitigation measure.

The LBNF Absorber consists of thirteen 6061-T6 aluminum core blocks. The core blocks are water cooled with de-ionized (DI) water which becomes radioactive during beam operations. The cooling water flows through gun-drilled channels in the core blocks. A weld quality optimization was performed to produce National Aeronautical Standard (NAS) 1514 Class I [1] quality welds on the aluminum core blocks. This was not successful in all cases. An existing Gas Tungsten Arc Welding (GTAW) Welding Procedure Specification (WPS) was fine tuned to minimize, in most cases, and eliminate detectable tungsten inclusions in the welds. All the weld coupons, how-ever passed welding inspection as per the piping code: ASME B31.3 Normal Fluid Service [2]. Tungsten electrode diameter, type, and manufacturer were varied. Some of the samples were pre-heated and others were not. It was observed that larger diameter electrodes, 5/32 in., with pre-heated joints resulted in welds with the least number of tungsten inclusions. It is hypothesized that thinner electrodes breakdown easily and get lodged into the weld pool during the welding process. This breakdown is further enhanced by the large temperature differential be-tween the un-preheated sample and the hot electrode.

Increase of primary beam power for neutrino beam-lines leads to a reduced lifespan for production targets. New concepts for robust targets are emerging from the field of High Power Targetry (HPT); one idea being investigated by the HPT R&D Group at Fermilab is an electrospun nanofiber target. As part of their evaluation, samples with different densities were sent to the HiRadMat facility at CERN for thermal shock tests. The samples with the higher density, irradiated under a high intensity beam pulse, exhibit major damage at the impact site whereas those with the lower density show no apparent damage. The exact cause of this failure was unclear at the time. In this paper, we present the results of multiphysics simulations of the thermal shock experienced by the nanofiber targets that suggest the failure originates from the reduced permeability of the high density sample to air flow. The air present in the porous target expands due to heating from the beam, but is unable to flow freely in the high density sample, resulting in a larger back pressure that blows apart the nanofiber mat. We close with a discussion on how to further validate this hypothesis.

High Power Targetry (HPT) R&D is critical in the context of increasing beam intensity and energy for next generation accelerators. Many target concepts and novel materials are being developed and tested for their ability to withstand extreme beam environments; the HPT R&D Group at Fermilab is developing an electrospun nanofiber material for this purpose. The performance of these nanofiber targets is sensitive to their construction parameters, such as the packing density of the fibers. Lowering the density improves the survival of the target, but reduces the secondary particle yield. Optimizing the lifetime and production efficiency of the target poses an interesting design problem, and in this paper we study the applicability of Bayesian optimization to its solution. We first describe how to encode the nanofiber target design problem as the optimization of an objective function, and how to evaluate that function with computer simulations. We then explain the optimization loop setup. Thereafter, we present the optimal design parameters suggested by the algorithm, and close with discussions of limitations and future refinements.