Proton therapy is a rapidly increasing modality to treat cancerous tumors, but large-scale implementation, and therefore widespread availability for patients, is hindered by the size and upfront investment for treatment facilities. Superconducting technology can enable more compact, and therefore more affordable treatment systems, by increasing the magnetic field in the magnets for the proton accelerator (typically a cyclotron) and in the beam guidance up, over, and into the patient (the gantry). In this article, we discuss research at Varian Medical Systems Particle Therapy GmbH on various superconducting technologies for potential application in future, more compact cyclotrons and gantries. We discuss which technologies are feasible, and to what extent. We demonstrate why certain conductor choices are made, and show the development of novel new conductor and magnet technologies that will be required to enable the next generation of cryogen-free, conduction-cooled compact treatment systems. We conclude that superconductivity is certainly required for the next generation of proton treatment systems, but also that the amount of compactness that can eventually be achieved is not solely determined by the magnetic field strength that is generated in the magnets.
Cable-in-Conduit Conductors (CICC) are made of several hundreds of superconducting and copper strands twisted together and gathered into multiple stages. To ensure safe and reliable operation of tokamaks, it is essential to take into account AC losses occurring in such conductors. Recently developed at CEA, the fully analytical model named COLISEUM (COupling Losses analytIcal Stages cablEs Unified Model) aims at predicting the coupling losses at various cable scales using only geometrical and electrical parameters. The most recent version of the COLISEUM model addresses the coupling losses for a full CICC, accounting contributions from the strand to the last stage of the cable. In this paper, we investigate the COLISEUM model in nontangential conditions. On the other hand, we present a methodology to derive geometrical model inputs from tomographic images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.