Optimization of mercury flow with microbubbles in the target-vessel design by means of machine learning

Kogawa, Hiroyuki; Futakawa, Masatoshi; Haga, Katsuhiro; Tsuzuki, Takayuki*; Murai, Tetsuro*

JAEA-Technology 2022-023, 128 Pages, 2022/11

JAEA-Technology-2022-023.pdf:9.0MB

In a mercury target of the J-PARC (Japan Proton Accelerator Research Complex), pulsed proton beams repeatedly bombard the flowing mercury which is confined in a stainless-steel vessel (target vessel). Cavitation damage caused by the propagation of the pressure waves is a factor of the life of the target vessel. As a measure to reduce damages, we developed a bubbler to inject the gas microbubbles into the flowing mercury, which can reduce the pressure waves. To operate the mercury target vessel stably with the 1 MW high-intensity proton beams, further reduction of the damage is required. The bubbler setting position should be closer to the beam window to increase the bubble population, which could enhance the reduction effect on the pressure waves and damage. However, the space at the beam window of the target vessel is restricted. The bubbler design and setting position as well as the vane design for the mercury flowing pattern are optimized by means of a machine learning technique to get more suitable bubble distribution, increasing in bubble population and optimizing bubble size nearby the beam window of the target vessel. The results of CFD analyses performed with 1000 cases were used for machine learning. Since the flow rate of mercury affects the temperature of the target vessel, this was used for the constraint condition. As a result, we found a design of mercury target vessel that can increase the bubble population by ca. 20% higher than the current design.