Neutron dose and power released by the JCO criticality accident in Tokai-mura

2000 ◽  
Vol 50 (1-2) ◽  
pp. 15-20 ◽  
Author(s):  
Tetsuji Imanaka
2001 ◽  
Vol 42 (SUPPL) ◽  
pp. S129-S135 ◽  
Author(s):  
HIROSHI TAKEDA ◽  
KIRIKO MIYAMOTO ◽  
MASAE YUKAWA ◽  
YOSHIKAZU NISHIMURA ◽  
YOSHITO WATANABE ◽  
...  

2014 ◽  
Vol 6 (1) ◽  
pp. 1006-1015
Author(s):  
Negin Shagholi ◽  
Hassan Ali ◽  
Mahdi Sadeghi ◽  
Arjang Shahvar ◽  
Hoda Darestani ◽  
...  

Medical linear accelerators, besides the clinically high energy electron and photon beams, produce other secondary particles such as neutrons which escalate the delivered dose. In this study the neutron dose at 10 and 18MV Elekta linac was obtained by using TLD600 and TLD700 as well as Monte Carlo simulation. For neutron dose assessment in 2020 cm2 field, TLDs were calibrated at first. Gamma calibration was performed with 10 and 18 MV linac and neutron calibration was done with 241Am-Be neutron source. For simulation, MCNPX code was used then calculated neutron dose equivalent was compared with measurement data. Neutron dose equivalent at 18 MV was measured by using TLDs on the phantom surface and depths of 1, 2, 3.3, 4, 5 and 6 cm. Neutron dose at depths of less than 3.3cm was zero and maximized at the depth of 4 cm (44.39 mSvGy-1), whereas calculation resulted  in the maximum of 2.32 mSvGy-1 at the same depth. Neutron dose at 10 MV was measured by using TLDs on the phantom surface and depths of 1, 2, 2.5, 3.3, 4 and 5 cm. No photoneutron dose was observed at depths of less than 3.3cm and the maximum was at 4cm equal to 5.44mSvGy-1, however, the calculated data showed the maximum of 0.077mSvGy-1 at the same depth. The comparison between measured photo neutron dose and calculated data along the beam axis in different depths, shows that the measurement data were much more than the calculated data, so it seems that TLD600 and TLD700 pairs are not suitable dosimeters for neutron dosimetry in linac central axis due to high photon flux, whereas MCNPX Monte Carlo techniques still remain a valuable tool for photonuclear dose studies.


ANRI ◽  
2020 ◽  
pp. 45-53
Author(s):  
A. Lachugin ◽  
M. Kocherygin ◽  
A. Gayazov ◽  
Yury Martynyuk ◽  
A. Vasil'ev

The paper presents basic results of development of a criticality accident alarm system to ensure safe retrieval of the spent nuclear fuel from the Lepse Floating Maintenance Base. The key features and engineering aspects of the system design are described. Locations of criticality detector units and selected alarm level settings are justified, hazardous area boundaries were identified, and parameters to identify inadequately protected zones were calculated. The SRKS-01D criticality accident alarm system by SPC “Doza” was selected as base equipment. The system was commissioned in 2019 and has been successfully operated for more than 6 months.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Igor Shuryak ◽  
Helen C. Turner ◽  
Monica Pujol-Canadell ◽  
Jay R. Perrier ◽  
Guy Garty ◽  
...  

AbstractWe implemented machine learning in the radiation biodosimetry field to quantitatively reconstruct neutron doses in mixed neutron + photon exposures, which are expected in improvised nuclear device detonations. Such individualized reconstructions are crucial for triage and treatment because neutrons are more biologically damaging than photons. We used a high-throughput micronucleus assay with automated scanning/imaging on lymphocytes from human blood ex-vivo irradiated with 44 different combinations of 0–4 Gy neutrons and 0–15 Gy photons (542 blood samples), which include reanalysis of past experiments. We developed several metrics that describe micronuclei/cell probability distributions in binucleated cells, and used them as predictors in random forest (RF) and XGboost machine learning analyses to reconstruct the neutron dose in each sample. The probability of “overfitting” was minimized by training both algorithms with repeated cross-validation on a randomly-selected subset of the data, and measuring performance on the rest. RF achieved the best performance. Mean R2 for actual vs. reconstructed neutron doses over 300 random training/testing splits was 0.869 (range 0.761 to 0.919) and root mean squared error was 0.239 (0.195 to 0.351) Gy. These results demonstrate the promising potential of machine learning to reconstruct the neutron dose component in clinically-relevant complex radiation exposure scenarios.


2016 ◽  
Vol 17 (5) ◽  
pp. 366-376 ◽  
Author(s):  
Xin Wang ◽  
Falk Poenisch ◽  
Narayan Sahoo ◽  
Ronald X. Zhu ◽  
MingFwu Lii ◽  
...  

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