3-D gamma dose rate reconstruction for a radioactive waste processing facility using sparse and arbitrarily-positioned measurements

Zhu, Shangzhen; Cao, Jianzhu; Dong, Xinwen; Li, Wenqian; Liu, Xuegang; He, Qiye; Wang, Xinghai

doi:10.1016/j.pnucene.2021.104073

Cited by 8 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang and Cai, (2018) proposed a method based on net function interpolation to the reciprocal of the dose rate field. Sai et al (2016) incorporated the interpolation technique for multiple secondary scattering data into the reconstruction process, and Zhu et al (2021) proposed a method based on the improved Cahn-Hilliard (C-H) equation to reconstruct the 3D gamma dose rate field using arbitrarily located sparse measurements. Although reverse reconstruction methods based on interpolation algorithms have been extensively studied, these methods often require a large number of uniform sampling points located throughout space, and in reality, these are often unattainable requirements.…”

Section: Introductionmentioning

confidence: 99%

Validation of the neural network for 3D photon radiation field reconstruction under various source distributions

Hao

Yanheng

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

Introduction: This paper proposes a five-layer fully connected neural network for predicting radiation parameters in a radiation space based on detector readings.Methods: The network is trained and tested using gamma flux values from individual detector positions as input, and is used to predict the gamma radiation field in 3D space under different source term distributions. The method is evaluated using the mean percentage change error (PCT) for the test set under different source term distributions.Results: The results show that the neural network method can accurately predict radiation parameters with an average PCT error range of 0.53% to 3.11%, within the given measurement input error range of ± 10%. The method also demonstrates its ability to directly reconstruct the 3D radiation field with some simple source terms.Discussion: The proposed method has practical value in real operations within radiation spaces, and can be used to improve the accuracy and efficiency of predicting radiation parameters. Further research could explore the use of more complex source term distributions and the integration of other types of sensors for improved accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Validation of the neural network for 3D photon radiation field reconstruction under various source distributions

Hao

Yanheng

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…Wang et al reconstruct radiation fields based on Bayesian inference and net function interpolation method inside a room [ 12 , 13 ]. The TV-H-1 inpainting equation and Cahn–Hilliard equation were applied to reconstruct radiation fields for radioactive wastes [ 14 , 15 ]. Sasaki M et al established an analytical method to convert the count rates obtained by the aircraft detector to the one-meter AGL dose rate [ 16 , 17 ] and used artificial neural networks to visualize ambient dose rate distribution from airborne radiation monitoring [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Ground Radioactivity Distribution Reconstruction and Dose Rate Estimation Based on Spectrum Deconvolution

Xu¹,

Ai²,

Wang³

et al. 2023

Sensors

View full text Add to dashboard Cite

Estimating the gamma dose rate at one meter above ground level and determining the distribution of radioactive pollution from aerial radiation monitoring data are the core technical issues of unmanned aerial vehicle nuclear radiation monitoring. In this paper, a reconstruction algorithm of the ground radioactivity distribution based on spectral deconvolution was proposed for the problem of regional surface source radioactivity distribution reconstruction and dose rate estimation. The algorithm estimates unknown radioactive nuclide types and their distributions using spectrum deconvolution and introduces energy windows to improve the accuracy of the deconvolution results, achieving accurate reconstruction of multiple continuous distribution radioactive nuclides and their distributions, as well as dose rate estimation of one meter above ground level. The feasibility and effectiveness of the method were verified through cases of single-nuclide (137Cs) and multi-nuclide (137Cs and 60Co) surface sources by modeling and solving them. The results showed that the cosine similarities between the estimated ground radioactivity distribution and dose rate distribution with the true value were 0.9950 and 0.9965, respectively, which could prove that the proposed reconstruction algorithm would effectively distinguish multiple radioactive nuclides and accurately restore their radioactivity distribution. Finally, the influences of statistical fluctuation levels and the number of energy windows on the deconvolution results were analyzed, showing that the lower the statistical fluctuation level and the more energy window divisions, the better the deconvolution results.

show abstract

3-D gamma radiation field reconstruction method using limited measurements for multiple radioactive sources

Zhu

Zhuang

Dong

et al. 2022

Annals of Nuclear Energy

View full text Add to dashboard Cite

3-D gamma dose rate reconstruction for a radioactive waste processing facility using sparse and arbitrarily-positioned measurements

Cited by 8 publications

References 17 publications

Validation of the neural network for 3D photon radiation field reconstruction under various source distributions

Validation of the neural network for 3D photon radiation field reconstruction under various source distributions

Ground Radioactivity Distribution Reconstruction and Dose Rate Estimation Based on Spectrum Deconvolution

3-D gamma radiation field reconstruction method using limited measurements for multiple radioactive sources

Contact Info

Product

Resources

About