Ⅱ-3. Nuclear Data

RIKEN Accel. Prog. Rep. 51 (2018)

Monte-Carlo simulation of transmutation based on experimental
nuclear data
S. Ebata,∗1 D. Ichinkhorloo,∗1 and M. Aikawa∗1,∗2∗1∗2

Faculty of Science, Hokkaido University
RIKEN Nishina Center- 105 -

the preparation of the Frag Data ﬁle, we can perform
PHITS simulations of a more realistic situation basedon the experimental data.
A simulation using the Frag Data function for
proton-induced reactions on a thin 107 Pd target wasperformed for conﬁrmation. The simulation result ofproduction cross sections of Ag isotopes (open squares)
is shown in Fig. 1, which is compared with the experimental data (ﬁlled squares) and the result without theFrag Data function (ﬁlled circles). We can conﬁrm thatthe result with the Frag Data function reproduces thesame result as the experiments.
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H.Wang Exp.
PHITS w/ Frag DataPHITS w/o Frag Data2520

[mb]The management of long-lived ﬁssion products(LLFPs) in nuclear reactors is one of the most important tasks in nuclear engineering. The transmutationof LLFPs is a promising technology to reduce and reusehigh-level radioactive nuclear wastes. Reaction crosssections of the transmutation are fundamental and essential information in nuclear applications. The crosssections of proton- and deuteron-induced reactions on
the LLFPs 93 Zr and 107 Pd were measured using theinverse kinematics technique in RIBF.1,2)
Furthermore, macroscopic simulations based on a realistic condition are essential for the feasibility studyof the transmutation. A Monte-Carlo simulation code
PHITS3) is adopted and used to design the nuclear
applications for the transmutation of LLFPs in ourproject. However the transmutation reaction cross sections calculated using PHITS are partially diﬀerent
from the experiment data.1,2) To avoid the discrepancies, we need to induce the recent experimental data
directly in simulations. A function named Frag Data
is implemented in PHITS to use external cross section
data. By using this function, we can reﬂect the experimental data in the macroscopic simulations.
The Frag Data function requires an external dataﬁle of cross sections for each reaction system consisting of a target and projectile. The ﬁle includes thetotal, production and double diﬀerential cross sectionsof each outgoing particle for several incident energies.
The production cross sections of heavy residual nuclei in a target could be obtained in previous experiments.1,2) The double diﬀerential cross sections of secondary light particles (neutron, proton, deuteron and4He) are also required because the data are expected
to have large eﬀects on the transmutation. The crosssections of such light particles, however, were not measured in the experiments. Therefore, we adopt results
for the secondary particles calculated using the default
PHITS simulation as inputs of the ﬁle.
For the simulation of the proton-induced reaction,
the process to prepare the Frag Data input ﬁles is
as follows. First, simulations for 100- and 200-MeV
proton-induced reactions on thin 107 Pd and 93 Zr targets (5 µm thickness) were performed with the trial
number set to 106 . From the simulations, the double
diﬀerential cross sections of the secondary light particles were obtained and inserted into the ﬁle. Next, theexperimental data, e.g. production cross section of theresidual nuclei (49 isotopes from Ag to Mo for 107 Pd
and 44 from Nb to Br for 93 Zr), were inserted. After

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MASSFig. 1. Production cross sections of Ag isotopes in the107
Pd+p reaction at 200 MeV.

The Frag Data function in PHITS can directly import bare experimental data of cross sections to simulations. We performed simulations of transmutation
with the function and then conﬁrmed the reproductionof experimental cross sections. Based on the Frag Dataﬁles, we can perform macroscopic simulations with realistic conditions for the feasibility study of transmutation.
This work was funded by the ImPACT Program ofthe Council for Science, Technology and Innovation(Cabinet Oﬃce, Government of Japan).
References
1) H. Wang et al., Prog. Theor. Exp. Phys. 2017, 021D01(2017).
2) S. Kawase et al., Prog. Theor. Exp. Phys. 2017, 093D03(2017).
3) T. Sato et al., J. Nucl. Sci. Technol. 50, 913 (2013).