γ spectrometer is studied. We convert γ spectrometer measurement objects into a four-layer theoretical model, which are attenuation thickness + radioactive hot area + attenuation thickness + disturb source. Then, the source item layer is virtualized into a point source by using virtual technology. So, the theoretical model is further simplified. Then the detection efficiency and peak/valley ratio parameter of source term are simulated by Monte Carlo method. Finally, the source term parameters are retrieved by using the least square method, and thus establishing the theoretical method and procedure of inversion calculation of source boundary parameters. In this paper, the theoretical and experimental results are shown to be consistent with each other. So, this method is verified to be correct and practicable. Currently, the method can accurately determine the depth distribution parameters of radioactive contamination area for uniformly distributed radio nuclides. In conclusion, the technical achievements can be used to accurately determine the boundary range of the radioactive hot zone, thus realizing the purpose of reducing the waste disposal capacity during the treatment. At the same time, the determination of the inert layer thickness parameters of the target nuclear warhead of Nuclear Test Ban Treaty has a significant reference value."> - 必威体育下载

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    Tian Zi-Ning, Ouyang Xiao-Ping, Chen Wei, Wang Xue-Mei, Deng Ning, Liu Wen-Biao, Tian Yan-Jie
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    • In the in situ γspectrometer based measurement of " hot particular”, " radioactive collection point” and " radioactive collection area”, only the position of the pollution source can be located roughly, but its boundary parameters such as the thickness of pollution source cannot be given. In this paper, the application of virtual technology to the scanning of γspectrometer is studied. We convert γspectrometer measurement objects into a four-layer theoretical model, which are attenuation thickness + radioactive hot area + attenuation thickness + disturb source. Then, the source item layer is virtualized into a point source by using virtual technology. So, the theoretical model is further simplified. Then the detection efficiency and peak/valley ratio parameter of source term are simulated by Monte Carlo method. Finally, the source term parameters are retrieved by using the least square method, and thus establishing the theoretical method and procedure of inversion calculation of source boundary parameters. In this paper, the theoretical and experimental results are shown to be consistent with each other. So, this method is verified to be correct and practicable. Currently, the method can accurately determine the depth distribution parameters of radioactive contamination area for uniformly distributed radio nuclides. In conclusion, the technical achievements can be used to accurately determine the boundary range of the radioactive hot zone, thus realizing the purpose of reducing the waste disposal capacity during the treatment. At the same time, the determination of the inert layer thickness parameters of the target nuclear warhead of Nuclear Test Ban Treaty has a significant reference value.
          Corresponding author:Tian Zi-Ning,tzn1019@126.com
        • Funds:Project supported by the National Natural Science Foundation of China (Grant No. 11405134)
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      • 测量对象 测量时长t/105s N241(54—57 keV) N241(59.54 keV) N239(51.62, 129 keV) A/104Bq
        241Am 239Pu
        探测模式1 3.16 4325136 25339979
        239Pu体源 2.00 75248200 239711, 52717 4.56 18.7
        DownLoad: CSV

        测量对象 测量时长t/105s N241(26.4 keV) N241(54—57 keV) N241(59.54 keV) A/× 104Bq
        26.4 keV 59.54 keV
        探测模式2 4.00 240050 9531180 65964536 8.16 8.91
        241Am点源 0.565 4160024 65331456 7.74 8.38
        241Am体源 0.800 45346 2745773 0.423 0.532
        DownLoad: CSV

        h/cm ${\varepsilon _{241}}(h)$/10–3 ${\varepsilon _{239}}(h)$/10–3 A241/104Bq A239/105Bq Q ${N}/{ { {N_{\rm v}} } }(h)$
        –1.25 24.3 24.6 0.921 0.497 5.4 8.05
        –1.60 17.9 19.5 1.24 0.629 5.0 6.99
        –2.00 12.9 15.1 1.73 0.810 4.7 6.12
        –2.40 9.45 11.9 2.36 1.03 4.4 5.44
        –2.80 7.00 9.45 3.19 1.30 4.1 4.92
        –3.20 5.24 7.60 4.26 1.61 3.8 4.51
        –3.60 3.97 6.16 5.62 1.98 3.5 4.18
        –3.80 3.47 5.56 6.45 2.20 3.4 4.04
        –4.00 3.03 5.04 7.37 2.43 3.3 3.90
        DownLoad: CSV

        w h/cm $\varepsilon (h)$/10–3 ${\varepsilon ^*}(h)$/10–3 ${N}/{ { {N_{\rm v}} } }(h)$ X2/10–3 X3/10–3 X1 $\sigma (X)$
        0.10 –1.25 24.3 24.6 8.05
        0.90 –3.20 5.24 7.60 4.51 7.15 9.30 4.87 0.177
        0.90 –3.60 3.97 6.16 4.18 6.00 8.01 4.56 0.308
        0.90 –3.80 3.47 5.56 4.04 5.55 7.47 4.44 0.385
        0.90 –4.00 3.03 5.04 3.90 5.16 6.99 4.31 0.458
        0.10 –1.60 17.9 19.5 6.99
        0.90 –3.20 5.24 7.60 4.51 6.51 8.79 4.76 0.217
        0.90 –3.60 3.97 6.16 4.18 5.36 7.50 4.46 0.396
        0.90 –3.80 3.47 5.56 4.04 4.91 6.96 4.33 0.479
        0.90 –4.00 3.03 5.04 3.90 4.52 6.48 4.21 0.554
        0.10 –2.00 12.9 15.1 6.12
        0.90 –3.20 5.24 7.60 4.51 6.01 8.35 4.67 0.277
        0.90 –3.60 3.97 6.16 4.18 4.86 7.06 4.37 0.474
        0.90 –3.80 3.47 5.56 4.04 4.41 6.52 4.24 0.559
        0.90 –4.00 3.03 5.04 3.90 4.02 6.04 4.12 0.635
        0.10 –2.40 9.45 11.9 5.44
        0.90 –3.20 5.24 7.60 4.51 5.66 8.03 4.61 0.327
        0.90 –3.60 3.97 6.16 4.18 4.52 6.74 4.30 0.530
        0.90 –3.80 3.47 5.56 4.04 4.06 6.20 4.18 0.616
        0.90 –4.00 3.03 5.04 3.90 3.67 5.72 4.05 0.693
        DownLoad: CSV

        h/cm w $\sigma (X)$ w $\sigma (X)$ w $\sigma (X)$ w $\sigma (X)$ w $\sigma (X)$
        –1.25 0.10 0.20 0.30 0.40 0.50
        –3.20 0.90 0.177 0.80 0.355 0.70 0.664 0.60 0.988 0.50 1.32
        –3.60 0.90 0.308 0.80 0.223 0.70 0.508 0.60 0.848 0.50 1.20
        –3.80 0.90 0.385 0.80 0.201 0.70 0.448 0.60 0.792 0.50 1.15
        –4.00 0.90 0.458 0.80 0.210 0.70 0.399 0.60 0.744 0.50 1.11
        –1.60 0.10 0.20 0.30 0.40 0.50
        –3.20 0.90 0.217 0.80 0.195 0.70 0.360 0.60 0.568 0.50 0.784
        –3.60 0.90 0.396 0.80 0.210 0.70 0.234 0.60 0.435 0.50 0.669
        –3.80 0.90 0.479 0.80 0.262 0.70 0.203 0.60 0.384 0.50 0.622
        –4.00 0.90 0.554 0.80 0.318 0.70 0.196 0.60 0.343 0.50 0.583
        –2.00 0.10 0.20 0.30 0.40 0.50
        –3.20 0.90 0.277 0.80 0.187 0.70 0.177 0.60 0.256 0.50 0.371
        –3.60 0.90 0.474 0.80 0.328 0.70 0.210 0.60 0.181 0.50 0.272
        –3.80 0.90 0.559 0.80 0.399 0.70 0.257 0.60 0.178 0.50 0.238
        –4.00 0.90 0.635 0.80 0.465 0.70 0.307 0.60 0.193 0.50 0.215
        –2.40 0.10 0.20 0.30 0.40 0.50
        –3.20 0.90 0.327 0.80 0.264 0.70 0.209 0.60 0.173 0.50 0.167
        –3.60 0.90 0.530 0.80 0.435 0.70 0.344 0.60 0.261 0.50 0.195
        –3.80 0.90 0.616 0.80 0.510 0.70 0.407 0.60 0.309 0.50 0.225
        –4.00 0.90 0.693 0.80 0.577 0.70 0.464 0.60 0.356 0.50 0.257
        DownLoad: CSV
        h/cm w $\sigma (X)$ w $\sigma (X)$ w $\sigma (X)$ w $\sigma (X)$
        –1.25 0.60 0.70 0.80 0.9
        –3.20 0.40 1.65 0.30 1.98 0.20 2.31 0.10 2.64
        –3.60 0.40 1.55 0.30 1.90 0.20 2.26 0.10 2.61
        –3.80 0.40 1.51 0.30 1.88 0.20 2.24 0.10 2.60
        –4.00 0.40 1.48 0.30 1.85 0.20 2.22 0.10 2.60
        –1.60 0.60 0.70 0.80 0.90
        –3.20 0.40 1.00 0.30 1.23 0.20 1.45 0.10 1.67
        –3.60 0.40 0.910 0.30 1.16 0.20 1.40 0.10 1.65
        –3.80 0.40 0.872 0.30 1.13 0.20 1.38 0.10 1.64
        –4.00 0.40 0.839 0.30 1.10 0.20 1.37 0.10 1.63
        –2.00 0.60 0.70 0.80 0.90
        –3.20 0.40 0.498 0.30 0.630 0.20 0.763 0.10 0.898
        –3.60 0.40 0.410 0.30 0.561 0.20 0.716 0.10 0.875
        –3.80 0.40 0.375 0.30 0.533 0.20 0.698 0.10 0.865
        –4.00 0.40 0.347 0.30 0.509 0.20 0.681 0.10 0.857
        –2.40 0.60 0.70 0.80 0.90
        –3.20 0.40 0.194 0.30 0.244 0.20 0.305 0.10 0.372
        –3.60 0.40 0.169 0.30 0.199 0.20 0.267 0.10 0.351
        –3.80 0.40 0.174 0.30 0.186 0.20 0.252 0.10 0.342
        –4.00 0.40 0.186 0.30 0.178 0.20 0.240 0.10 0.335
        DownLoad: CSV

        hV/cm 体源厚度/cm $\varepsilon ({h_{\rm{V}}})$
        /10–3
        ${\varepsilon ^*}({h_{\rm{V}}})$
        /10–2
        ${N}/{ { {N_{\rm v}} } }({h_{\rm{V} } })$ $\sigma (X)$
        –2.80 0.80 4.54 0.689 4.69 0.444
        –2.80 1.2 4.60 0.696 4.74 0.431
        –2.80 1.6 4.70 0.705 4.81 0.414
        –2.45 0.80 5.68 0.818 5.03 0.231
        –2.45 1.2 5.76 0.826 5.07 0.217
        –2.45 1.6 5.89 0.837 5.16 0.200
        –2.45 2.0 6.05 0.851 5.26 0.181
        –2.45 2.5 6.31 0.874 5.41 0.159
        –2.45 3.0 6.65 0.903 5.63 0.163
        –2.45 4.0 7.57 0.981 6.24 0.289
        –2.45 4.9 8.78 1.08 7.12 0.539
        –2.00 0.80 7.62 1.03 5.58 0.188
        –2.00 1.2 7.75 1.04 5.64 0.212
        –2.00 1.6 7.93 1.05 5.75 0.248
        –2.00 2.0 8.17 1.07 5.88 0.295
        –2.00 2.5 8.55 1.10 6.11 0.373
        –2.00 3.0 9.03 1.14 6.41 0.474
        –2.00 4.0 10.4 1.25 7.35 0.769
        –1.50 0.80 10.7 1.33 6.43 0.744
        –1.50 1.2 10.9 1.35 6.54 0.781
        –1.50 1.6 11.2 1.37 6.68 0.834
        –1.50 2.0 11.5 1.40 6.90 0.905
        –1.50 3.0 12.9 1.50 7.79 1.18
        –0.50 0.80 22.0 2.34 10.1 2.81
        DownLoad: CSV

        h/cm ${\varepsilon _{26.4\;{\rm{keV}}}}(h)$/10–3 ${\varepsilon _{59.54\;{\rm{keV}}}}(h)$/10–2 A26.4 keV/104Bq A59.54 keV/104Bq A59.54 keV/A26.4 keV ${N}/{ { {N_{\rm v}} } }(h)$
        0.80 48.200 14.4 0.0519 0.318 6.10 17.0
        0.40 13.500 8.79 0.1850 0.523 2.80 12.0
        0 4.060 5.56 0.6150 0.826 1.30 9.3
        –0.20 2.260 4.48 1.1100 1.030 0.90 8.4
        –0.40 1.270 3.64 1.9700 1.260 0.60 7.7
        –0.60 0.717 2.98 3.4900 1.540 0.40 7.1
        –0.80 0.409 2.45 6.1200 1.880 0.30 6.6
        DownLoad: CSV

        hV/cm 体源
        厚度/cm
        ${\varepsilon ^*}({h_{\rm{V}}})$/
        10–2
        $\varepsilon ({h_{\rm{V}}})$/
        10–3
        ${N}/{ { {N_{\rm v}} } }({h_{\rm{V} } })$ $\sigma (X)$ hV/cm 体源
        厚度/cm
        ${\varepsilon ^*}({h_{\rm{V}}})$/
        10–2
        $\varepsilon ({h_{\rm{V}}})$/
        10–3
        ${N}/{ { {N_{\rm v}} } }({h_{\rm{V} } })$ $\sigma (X)$
        0.75 1.00 3.54 10.4 11.90 1.5900 0.15 2.20 2.45 5.00 9.34 0.2310
        0.75 0.60 3.47 8.06 11.40 1.0100 0.15 1.60 2.31 2.69 8.54 0.3470
        0.75 0.30 3.44 7.23 11.20 0.8060 0.15 1.00 2.21 1.72 8.07 0.5920
        0.25 2.00 2.59 5.49 9.61 0.3530 0.15 0.40 2.17 1.32 7.86 0.6920
        0.25 1.90 2.56 4.91 9.44 0.2110 0 2.50 2.26 4.40 9.00 0.0900
        0.25 1.80 2.54 4.42 9.29 0.0890 0 2.45 2.24 4.14 8.90 0.0470
        0.25 1.70 2.51 4.00 9.14 0.0220 0 2.40 2.23 3.90 8.83 0.0640
        0.25 1.60 2.49 3.63 9.03 0.1090 0 2.00 2.13 2.52 8.30 0.3950
        0.25 1.50 2.47 3.32 8.92 0.1870 0 1.50 2.04 1.60 7.83 0.6260
        0.25 1.30 2.43 2.81 8.72 0.3130 0 1.00 1.97 1.13 7.54 0.7470
        0.25 0.80 2.37 2.04 8.38 0.5080 –0.25 1.10 1.65 0.602 6.86 0.8910
        –0.25 0.50 1.61 0.457 6.70 0.9300
        DownLoad: CSV

        h/cm w $\sigma (X)$ w $\sigma (X)$ w $\sigma (X)$ w $\sigma (X)$ w $\sigma (X)$
        0.80 0.10 0.20 0.30 0.40 0.50
        –0.40 0.90 2.414 0.80 1.783 0.70 2.807 0.60 3.832 0.50 4.86
        –0.60 0.90 2.115 0.80 1.512 0.70 2.569 0.60 3.627 0.50 4.69
        –0.80 0.90 1.951 0.80 1.362 0.70 2.433 0.60 3.510 0.50 4.59
        0.40 0.10 0.20 0.30 0.40 0.50
        –0.40 0.90 0.466 0.80 0.291 0.70 0.529 0.60 0.780 0.50 1.03
        –0.60 0.90 0.177 0.80 0.119 0.70 0.286 0.60 0.567 0.50 0.857
        –0.80 0.90 0.185 0.80 0.245 0.70 0.175 0.60 0.447 0.50 0.753
        0 0.10 0.20 0.30 0.40 0.50
        –0.40 0.90 0.190 0.80 0.160 0.70 0.168 0.60 0.235 0.50 0.327
        –0.60 0.90 0.431 0.80 0.346 0.70 0.214 0.60 0.123 0.50 0.165
        –0.80 0.90 0.620 0.80 0.512 0.70 0.350 0.60 0.197 0.50 0.112
        DownLoad: CSV
        h/cm w $\sigma (X)$ w $\sigma (X)$ w $\sigma (X)$ w $\sigma (X)$
        0.80 0.60 0.70 0.80 0.90
        –0.40 0.40 5.88 0.30 6.91 0.20 7.93 0.10 8.96
        –0.60 0.40 5.75 0.30 6.81 0.20 7.87 0.10 8.92
        –0.80 0.40 5.67 0.30 6.75 0.20 7.83 0.10 8.90
        0.40 0.60 0.70 0.80 0.90
        –0.40 0.40 1.29 0.30 1.55 0.20 1.80 0.10 2.06
        –0.60 0.40 1.15 0.30 1.44 0.20 1.73 0.10 2.03
        –0.80 0.40 1.06 0.30 1.38 0.20 1.69 0.10 2.01
        0 0.60 0.70 0.80 0.90
        –0.40 0.40 0.427 0.30 0.532 0.20 0.639 0.10 0.747
        –0.60 0.40 0.287 0.30 0.425 0.20 0.567 0.10 0.711
        –0.80 0.40 0.207 0.30 0.361 0.20 0.523 0.10 0.689
        DownLoad: CSV
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      Metrics
      • Abstract views:6845
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      Publishing process
      • Received Date:16 July 2019
      • Accepted Date:19 September 2019
      • Available Online:27 November 2019
      • Published Online:05 December 2019

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