Large area imaging of hydrogenous materials using fast neutrons from a DD fusion generator

doi:10.1016/j.nima.2012.02.003

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

Volume 675, 21 May 2012, Pages 51-55

https://doi.org/10.1016/j.nima.2012.02.003 Get rights and content

Abstract

A small-laboratory fast-neutron generator and a large area detector were used to image hydrogen-bearing materials. The overall image resolution of 2.5 mm was determined by a knife-edge measurement. Contact images of objects were obtained in 5–50 min exposures by placing them close to a plastic scintillator at distances of 1.5 to 3.2 m from the neutron source. The generator produces 10⁹ n/s from the DD fusion reaction at a small target. The combination of the DD-fusion generator and electronic camera permits both small laboratory and field-portable imaging of hydrogen-rich materials embedded in high density materials.

Section snippets

Acknowledgments

We acknowledge the valued assistance of J. Reijonen and M. King of Lawrence Berkeley National Laboratory. This work was supported in part by the US National Science Foundation, Grant no. IIP-0724503, and US Department of Energy, Grant no. DE-FG02–04ER86177.

References (14)

J. Reijonen et al.
Appl. Radiat. Isot.
(2005)
J. Hall et al.
AIP Conf. Proc.
(2001)
J. Hall, F. Dietrich, C. Logan, B. Rusnak, Lawrence Livermore National Lab, UCRL-MI-140345 (Sept. 11,...
C. Hurlbut, ELJEN Technology, Sweetwater,...
Saint-Gobain Crystals and Detectors, Newbury, Ohio,...
M. Buchin, Stanford Photonics, Palo Alto, CA,...
S.S. Nargolwalla et al.
Activation Analysis with Neutron Generators
(1973)

There are more references available in the full text version of this article.

Cited by (27)

Pulsed radiation image restoration based on unsupervised deep learning
2024, Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
The process of pulsed X-ray imaging generates noise and blurring that significantly degrade image quality, lead to loss of image information, and interfere with accurate diagnosis of X-ray spot size and objects. Traditional radiation image restoration methods lack flexibility and generalization. Furthermore, neural network methods based on supervised learning require many actual image data sets to be obtained in advance, which limits network training. In this paper, we propose an unsupervised radiation image restoration method based on a deep image prior. The classical DeepRED framework is extended by adding weight-adaptive variational regularization. Automatic strategies are employed to estimate local regularization parameters, resulting in better noise removal and preservation of image edges and texture structures. The proposed method is evaluated through numerical simulations and experimental studies of X-ray source images and flash radiographic images. The results indicate that the proposed method is effective in restoring pulsed radiation images, removing noise, and preserving boundary region information. Additionally, this method produces satisfactory results for the restoration of real neutron images, demonstrating its applicability in the field of radiation imaging.
Neutron image denoising and deblurring based on generative adversarial networks
2023, Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Neutron radiography has been widely employed in nondestructive investigations. The noise and blur that are inevitably generated in the process of neutron radiography seriously decrease the image quality and lead to the loss of image information. In particular, white spot noise, which has the characteristics of nonuniform distribution, large size, and high magnitude, is difficult to remove. To solve this issue, we apply a generative adversarial network-based to convert a degraded neutron image into a clean image. The basic idea is to integrate attention mechanisms into our generation and discrimination networks. A fusion block with a visual attention mechanism is proposed, which can extract more potential features from the images and preserve the image texture as much as possible. In addition, due to the lack of neutron image datasets, we propose a neutron image degradation model to simulate the noise and blur in real neutron images. The results of the experiments show that our method can effectively eliminate noise and blur from neutron images while retaining texture information well.
Development, design, and testing of a microwave-driven compact rotating-target D-D fast neutron generator for imaging applications
2021, Applied Radiation and Isotopes
A compact D-D fast neutron generator system was developed with an emphasis on imaging applications. It is equipped with an electron cyclotron resonance microwave ion source, an electric field method electron suppression electrode, and a rotating beam target. The design, mechanical assembling, and initial performance of the system is presented in this work. Electrostatics and charged particle tracing simulations were employed to dimension components in the vacuum system and estimate the size of the ion beam spot on the target surface. Stable operation of the neutron generator was possible with accelerating potentials of up to −120 kV and an estimated averaged ion beam current of around 0.5 mA. At these conditions, the neutron yield was estimated to 6 $\cdot$ 10⁷ s⁻¹ based on a combination of detailed Monte Carlo MCNP6 models and the ambient dose rate reading of an LB6411 neutron probe. The neutron emitting spot size was compared to the ion beam spot size and experimentally determined with an attenuating edge technique to be between 1.7 and 2.6 mm. With these neutron yield and emitting spot size values, the system compares favorably with commercially available neutron generators, and this context is also presented.
No-reference quality assessment for neutron radiographic image based on a deep bilinear convolutional neural network
2021, Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Citation Excerpt :
Besides, the PLCC and SROCC values reported in Table 1 further indicate that pre-training on the pre-training dataset can be beneficial in improving the performance of the proposed model. Since the image enhancement algorithm can improve the quality of the neutron radiographic image to some extent, the authentic and enhanced neutron radiographic images shown in Fig. 6 (i.e., (a) [33], (b) [34], (c) [35], and (d) [36]) and 7 (i.e., (a) [37], (b) [38], (c) [36], and (d) [38]) are used to evaluate the proposed model. We use the fine-tuned models obtained from the fine-tuning dataset of 10 random divisions to predict the quality of each image.
Neutron imaging (NI) has been widely employed in non-destructive investigations. Since the image quality assessment (IQA) method can be beneficial in reflecting the performance of imaging systems and image processing algorithms, we propose a proof-of-concept IQA method for the NI system based on a deep bilinear convolutional neural network (CNN) framework with two designed datasets. Due to the lack of neutron IQA database, different levels of authentic distortion induced by NI are first simulated on the natural and neutron radiographic images to generate the pre-training and fine-tuning datasets, respectively. Then, the gradient magnitude similarity deviation (GMSD) algorithm and transfer learning method are respectively employed to label the above datasets and optimize the prediction performance. Experimental results demonstrate that the proposed method can maintain good consistency with human perception in predicting the quality scores of both the authentic and enhanced neutron radiographic images.
A study on the feasibility of fast neutron imaging using the D–D fusion neutrons of the KSTAR tokamak
2019, Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
In the 2015 Annual Plasma Campaign of the Korea Superconducting Tokamak Advanced Research (KSTAR), which mainly used neutral beam injection (NBI) as auxiliary heating, the yield by the Deuterium–Deuterium (D–D) fusion neutrons of 2.45 MeV energy from the KSTAR plasma was estimated to have reached about 10¹⁴ n/s. Apart from the primary goal, which is energy production by nuclear fusion, these fusion neutrons may be used in other applications such as aerospace, defense, material, and electronic component research and testing, as well as battery and fuel cell research in the fields of archeology and security. This is because the tokamak is huge and intense as a volumetric neutron source.
This work aims to investigate the feasibility of fast neutron imaging using the D–D fusion neutrons emitted from the KSTAR tokamak. For this work, we adopted a fast neutron imaging technique based on a CCD camera in combination with a fiber–optical scintillator (FOS). The first experimental results about the feasibility of fast neutron imaging obtained from the KSTAR Tokamak will be described in this paper.
Thermal analysis, design, and testing of a rotating beam target for a compact D-D fast neutron generator
2019, Applied Radiation and Isotopes
Citation Excerpt :
Neutron angular yield and neutron energy distributions of this NG system are discussed thoroughly in Zboray et al. (2014) and Soubelet et al. (2018) Because fast neutron transmission tomography was the focus when the system was constructed, the neutron emitting spot size of the generator was designed to be comparably small (< 2 mm). The small spot size combined with the possibility of putting an imaging subject close to the source (< 10 cm) in the forward direction differentiates this NG from commercially available designs, for example in Chichester et al. (2005) and Cremer et al. (2012). Heat transfer analysis to find an optimal rotating target rod configuration was carried out with COMSOL Multiphysics 5.2a (COMSOL, 2016a).
Compact Deuterium-Deuterium fast neutron generators are generally limited to a low neutron yield due to outgassing of implanted deuterium in case of ion beam target overheating. This is even more pronounced for a generator with a small emitting spot tailored to transmission-based imaging applications, resulting in long exposure times. In this work a novel water-cooled rotating copper target rod with a 5 µm thin titanium coating as a deuteron beam target was developed. Using extensive computational fluid dynamics and heat transfer analysis, an optimized target configuration was chosen. The details of the target rod design, construction and loading with deuterium are outlined. The first results after loading showed a peak total neutron output of 4.8 × 10⁷ n/s for −107 kV target high voltage and 0.9 mA average target current at 220 rpm target rotational velocity. The comparison between actual and theoretical ideal neutron yield suggested that no loss of deuterium density in the target due to outgassing occurred.

View all citing articles on Scopus

View full text

Large area imaging of hydrogenous materials using fast neutrons from a DD fusion generator

Abstract

Section snippets

Acknowledgments

Appl. Radiat. Isot.

AIP Conf. Proc.

Activation Analysis with Neutron Generators