| 1. |
- al-Dbissi, Moad, 1994, et al.
(author)
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Conceptual design and initial evaluation of a neutron flux gradient detector
- 2022
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In: Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. - : Elsevier BV. - 0168-9002. ; 1026
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Journal article (peer-reviewed)abstract
- Identification of the position of a localized neutron source, or that of local inhomogeneities in a multiplying or scattering medium (such as the presence of small, strong absorbers) is possible by measurement of the neutron flux in several spatial points, and applying an unfolding procedure. It was suggested earlier, and it was confirmed by both simulations and pilot measurements, that if, in addition to the usually measured scalar (angularly integrated) flux, the neutron current vector or its diffusion approximation (the flux gradient vector) is also considered, the efficiency and accuracy of the unfolding procedure is significantly enhanced. Therefore, in support of a recently started project, whose goal is to detect missing (replaced) fuel pins in a spent fuel assembly by non-intrusive methods, this idea is followed up. The development and use of a dedicated neutron detector for within-assembly measurements of the neutron scalar flux and its gradient are planned. The detector design is based on four small, fiber-mounted scintillation detector tips, arranged in a rectangular pattern. Such a detector is capable of measuring the two Cartesian components of the flux gradient vector in the horizontal plane. This paper presents an initial evaluation of the detector design, through Monte Carlo simulations in a hypothetical scenario.
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| 2. |
- al-Dbissi, Moad, 1994
(author)
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Developments toward a novel methodology for spent nuclear fuel verification
- 2022
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Licentiate thesis (other academic/artistic)abstract
- One of the tasks in nuclear safeguards is to regularly inspect spent nuclear fuel discharged from nuclear power reactors and verify the integrity of it, so that illegal removal and diversion of radioactive material can be promptly discovered. In the current project, which is a collaboration between Chalmers University of Technology and SCK CEN, a novel methodology for non-intrusive inspection of spent nuclear fuel is under development. The methodology consists of two main steps: 1) neutron flux and its gradient are measured inside spent nuclear fuel assemblies using small neutron detectors; and 2) the measurements are processed using an Artificial Neural Network (ANN) algorithm to identify the number and location of possible fuel pins that have been removed from the fuel assemblies and/or replaced with dummies. The use of small neutron detectors simplifies the inspection procedure since the fuel assemblies are not moved from their storage position. In addition, the neutron flux gradient measurements and its processing with the ANN algorithm have the potential for more detailed results. Different aspects have been investigated for the development of the methodology. For the first step of the methodology, the concept of a new neutron detector has been studied via Monte Carlo simulations and it relies on the use of optical fiber-mounted neutron scintillators. The outcome of the computational study shows that the selected detector design is a viable option since it has a suitable size to be introduced inside a fuel assembly and can measure neutron flux gradients. Then, experimental work has been carried out to test and characterize two optical fiber-based neutron scintillators that can be used to build the detector, with respect to detection of thermal neutrons and sensitivity to gamma radiation. For the second step of the methodology, a machine learning algorithm based on ANN is studied. At this initial stage, a simpler problem has been considered, i.e., an ANN has been prepared, trained and tested using a dataset of synthetic neutron flux measurements for the classification of PWR nuclear fuel assemblies according to the total amount of missing fuel, without including neutron flux gradient measurements and without localizing the anomalies. From the comparison with other machine learning methods such as decision trees and k-nearest neighbors, the ANN shows promising performance.
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| 3. |
- al-Dbissi, Moad, 1994, et al.
(author)
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Identification of diversions in spent PWR fuel assemblies by PDET signatures using Artificial Neural Networks (ANNs)
- 2023
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In: Annals of Nuclear Energy. - 0306-4549 .- 1873-2100. ; 193
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Journal article (peer-reviewed)abstract
- Spent nuclear fuel represents the majority of materials placed under nuclear safeguards today and it requires to be inspected and verified regularly to promptly detect any illegal diversion. Research is ongoing both on the development of non-destructive assay instruments and methods for data analysis in order to enhance the verification accuracy and reduce the inspection time. In this paper, two models based on Artificial Neural Networks (ANNs) are studied to process measurements from the Partial Defect Tester (PDET) in spent fuel assemblies of Pressurized Water Reactors (PWRs), and thus to identify at different levels of detail whether nuclear fuel has been replaced with dummy pins or not. The first model provides an estimation of the percentage of replaced fuel pins within the inspected fuel assembly, while the second model determines the exact configuration of the replaced fuel pins. The two models are trained and tested using a dataset of Monte-Carlo simulated PDET responses for intact spent PWR fuel assemblies and a variety of hypothetical diversion scenarios. The first model classifies fuel assemblies according to the percentage of diverted fuel with a high accuracy (96.5%). The second model reconstructs the correct configuration for 57.5% of the fuel assemblies available in the dataset and still retrieves meaningful information of the diversion pattern in many of the misclassified cases.
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| 4. |
- al-Dbissi, Moad, 1994
(author)
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Investigation of a Methodology for the Detection of Diversions in Spent Nuclear Fuel
- 2024
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Doctoral thesis (other academic/artistic)abstract
- One of the main tasks in nuclear safeguards is the inspection of Spent Nuclear Fuel (SNF) to detect possible diversions of their special nuclear material content, e.g., U-235 and Pu-239. These inspections verify the declared SNF via passive measurements of characteristic signatures such as the emissions of neutrons and gamma rays. The current PhD research investigates different aspects for the development of a novel non-intrusive methodology that can enhance safeguards inspections of SNF assemblies, and it includes two main parts. In the first part, simulations are performed to evaluate the feasibility of measuring the neutron flux and its gradient inside the empty guide tubes of a SNF assembly with a miniaturized detector made of an array of optical fiber-based neutron scintillators. In addition, experiments are carried out to characterize these types of neutron scintillators. The results of this preparatory work show that neutron flux gradient measurements in SNF assemblies may be a viable option and provide insights for the construction of a prototype of a detector for the purpose. In the second part of the research, the application of machine learning models based on Artificial Neural Networks (ANNs) is studied to process measured SNF signatures and reconstruct the arrangement of the fuel pins in an assembly. The objective of this part is two-fold. On one hand, ANN models are explored for the task of determining possible diversion patterns from SNF signatures collected inside the accessible guide tubes. On the other hand, the advantage of providing the neutron flux gradient as input feature to the algorithm is evaluated. The training and testing of the ANN models are performed with synthetic datasets generated from Monte-Carlo simulations of a typical PWR SNF assembly, considering the intact configuration and different degrees and patterns of diversion. The results show that the models effectively predict diversions and characterize most of them to a good extent. In addition, the use of the neutron flux gradient, which is not analyzed during standard inspections, is proven to be advantageous.
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| 5. |
- al-Dbissi, Moad, 1994, et al.
(author)
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On the use of neutron flux gradient with ANNs for the detection of diverted spent nuclear fuel
- 2024
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In: Annals of Nuclear Energy. - 0306-4549 .- 1873-2100. ; 204
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Journal article (peer-reviewed)abstract
- One of the main tasks in nuclear safeguards is regular inspections of Spent Nuclear Fuel (SNF) assemblies to detect possible diversions of special nuclear material such as 235U and 239Pu. In these inspections, characteristic signatures of SNF such as emissions of neutrons and gamma rays from the radioactive decay, are measured and their consistency with the declared assemblies is verified to ensure that no fuel pins have been removed. Research in this field is focused on both the development of detection equipment and methods for the analysis of the acquired measurement data. In this paper, the use of the neutron flux gradient, which is not considered in regular SNF verification, is investigated in combination with the scalar neutron flux as input to artificial neural network models for the quantification of fuel pins in SNF assemblies. The training and testing of these ANN models rely on a synthetic dataset that is generated from Monte Carlo simulations of a typical intact pressurized water reactor assembly and with different patterns of fuel pins replaced by dummy pins. The dataset consists of unique scenarios so that the ANN can be assessed over “unknown” cases that are not part of the learning phase. Results show that the neutron flux gradient is advantageous for a more accurate reconstruction of diversions within SNF assemblies.
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| 6. |
- Pazsit, Imre, 1948, et al.
(author)
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Ringhals Diagnostics and Monitoring, Annual Research Report 2021-2022
- 2022
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Reports (other academic/artistic)abstract
- This report gives an account of the work performed by the Division of Subatomic, High Energy and Plasma Physics (formerly, Division of Nuclear Engineering), Chalmers, in the frame of a research collaboration with Ringhals, Vattenfall AB, contract No. 4501747546-003. The contract constitutes a one-year co-operative research work concerning diagnostics and monitoring of the PWR units. The work in the contract has been performed between 1 July 2021 and 30 June 2022.
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