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Design analysis of neutron source based on low energy cyclotron used for small-scale accelerator-driven system
Syarip Syarip1, Isdandy Rezki Febrianto1, Djoko Slamet Pudjorahardjo1
1Research Center for Accelerator Technology, Research Organization for Nuclear Energy, National Research and Innovation Agency, Gd 720 KST B.J. Habibie, Tangerang Selatan, Banten, Indonesia.
None:
An appropriate target system is crucial for converting the incident proton beam from a cyclotron into a neutron beam, which drives a subcritical reactor in a small-scale accelerator-driven system (SS-ADS). The methodology for designing the target system was a combination of the Particle and Heavy Ion code Transport System (PHITS) simulation and the Response Surface Methodology (RSM) analysis. PHITS simulations applied the proton beam specifications from the DECY-13 cyclotron as the particle source model, and RSM incorporated the external neutron beam requirement of the SAMOP subcritical reactor into the optimization. Beryllium was chosen as the target material because it produces the highest neutron yield per incident proton, outperforming other candidates such as manganese, titanium, and vanadium. The heavy water moderated neutrons produced by the 9Be(p,n)9B reaction, while polyethylene functioned as a neutron reflector. The analysis result shows that the optimal target system consisted of the target material in a form of obstructed needle with radial thickness of 0.18 cm, cavity length of 2.56 cm, obstructed thickness and angle of 0.17 cm and 31.06 degree; the moderator in a shape of truncated cone with length of 3.26 cm, entrance and exit radius of 0.85 cm and 0.91 cm; and neutron reflector thickness 15.64 cm. Equipped with the target system, the DECY-13 cyclotron can deliver a thermal neutron flux of (2.7 ± 0.1)×108 n/cm2s, sufficient to drive the SS-ADS subcritical reactor. An additional PHITS simulation with the optimal configuration validated the RSM prediction by producing a thermal neutron flux of (2.81 ± 0.02)×108 n/cm2s. It indicates that RSM prediction is highly accurate in this case, with a deviation of 3.91 %.
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