Verification of MCNP6 model of the Jordan Research and Training Reactor (JRTR) for calculations of neutronic parameters

Jaradat, Mustafa K.; Radulović, Vladimir; Park, Chang Je; Snoj, Luka; Alkhafaji, Salih M.

doi:10.1016/j.anucene.2016.06.003

Cited by 15 publications

(3 citation statements)

References 9 publications

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“…Also, additional research is needed to determine whether the slight differences in uncertainties produced by MCD and BNN are caused by the algorithms themselves or by the training data used in this study. Possible future studies to be considered include: (1) an attempt to use the measurement data for predicting 3D flux shapes, (2) investigate reasons for the inferior performance of NNs in the vicinity of the coupling piece of the control follower assemblies and find a way to increase the accuracy of predictions, thereby improve the overall performance of these models, and (3) benchmark with other machine learning techniques such as Gaussian Processes that can provide the interpolation uncertainties directly.…”

Section: Discussionmentioning

confidence: 99%

“…The knowledge of the neutron flux distribution is essential in determining various reactor physical parameters such as its absolute power, fuel burnup, power peaking factor (PPF), peak-clad fuel temperature, safety margins, as well as in the monitoring of the reactor operation. Furthermore, the determination of axial and radial neutron flux distribution is also important for fuel management, code verification and validation (V&V) studies [1,2] and experiments, as well as for various applications such as medical isotope production [3]. A good knowledge of the flux distribution is, therefore, crucial for an efficient operation of the reactors without violation of safety limits.…”

Section: Introductionmentioning

confidence: 99%

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Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks

Moloko¹,

Bokov²,

Xu³

et al. 2022

Preprint

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Artificial Neural Networks (ANNs) have been successfully used in various nuclear engineering applications, such as predicting reactor physics parameters within reasonable time and with a high level of accuracy. Despite this success, they cannot provide information about the model prediction uncertainties, making it difficult to assess ANN prediction credibility, especially in extrapolated domains. In this study, Deep Neural Networks (DNNs) are used to predict the assembly axial neutron flux profiles in the SAFARI-1 research reactor, with quantified uncertainties in the ANN predictions and extrapolation to cycles not used in the training process. The training dataset consists of copper-wire activation measurements, the axial measurement locations and the measured control bank positions obtained from the reactor's historical cycles. Uncertainty Quantification of the regular DNN models' predictions is performed using Monte Carlo Dropout (MCD) and Bayesian Neural Networks solved by Variational Inference (BNN VI). The regular DNNs, DNNs solved with MCD and BNN VI results agree very well among each other as well as with the new measured dataset not used in the training process, thus indicating good prediction and generalization capability. The uncertainty bands produced by MCD and BNN VI agree very well, and in general, they can fully envelop the noisy measurement data points. The developed ANNs are useful in supporting the experimental measurements campaign and neutronics code Verification and Validation (V&V).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks

Moloko¹,

Bokov²,

Xu³

et al. 2022

Preprint

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show abstract

“…e plate-type fuel with the uranium density of 3 gU/cm 3 was also used in the MPRR design with the power of 20 MW for achieving high neutron fluxes at irradiation channels [5]. Several designs of MPRRs include the OPAL reactor (20 MW) in Australia [9,10], the CARR reactor (60 MW) in China, the RA-10 reactor (30 MW) in Argentina [11], the HFIR reactor (85 MW) in USA, the FRM-II reactor (20 MW) in Germany [12], the HANARO reactor (30 MW) in Korea [13,14], JRR-3M (20 MW) in Japan, the RMB reactor (30 MW) in Brazil [15], and the JRTR reactor (5 MW) in Jordan [16]. A comparison among several MPRRs shows that the FRM-II reactor achieves the highest thermal flux per unit power with a maximum thermal neutron flux of 8 × 10 14 n•cm −2 •s −1 [6].…”

Section: Introductionmentioning

confidence: 99%

Conceptual Design of a 10 MW Multipurpose Research Reactor Using VVR-KN Fuel

Nguyen

Huynh

et al. 2020

Science and Technology of Nuclear Installations

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The paper presents a conceptual design of a 10 MW multipurpose nuclear research reactor (MPRR) loaded with the low-enriched uranium (LEU) VVR-KN fuel type. Neutronics and burnup calculations have been performed using the REBUS-MCNP6 linkage system code and the ENDF/B-VII.0 data library. The core consists of 36 fuel assemblies: 27 standard fuel assemblies and 9 control fuel assemblies with the uranium density of 2.8 gU/cm3 and the 235U enrichment of 19.75 wt.%. The cycle length of the core is 86 effective full-power days with the excess reactivity of 9600 and 1039 pcm at the beginning of cycle and the end of cycle, respectively. The highest power rate and the highest discharged burnup of fuel assembly are 393.49 kW and 56.74% loss of 235U, respectively. Thermal hydraulics analysis has also been conducted using the PLTEMP4.2 code for evaluating the safety parameters at a steady state of the hottest channel. The maximum temperatures of coolant and fuel cladding are 66.0°C and 83.0°C, respectively. This value is lower than the design limit of 98°C for cladding temperature. Thermal fluxes at the vertical irradiation channels and the horizontal beam ports have been evaluated. The maximum thermal fluxes of 2.5 × 1014 and 8.9 ×1013 n·cm−2·s−1 are found at the neutron trap and the beryllium reflector, respectively.

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Advancing Nuclear Research and Education in Slovenia and EU: From Operating the TRIGA Reactor to Building a New Generation Facility

Malec,

Tiselj,

Cizelj

et al. 2024

Arab J Sci Eng

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The TRIGA Mark II research reactor at the Jožef Stefan Institute in Slovenia achieved first criticality in 1966. Since then, the reactor has been playing an important role in developing nuclear technology. The reactor has been mainly used for research, education of university students, training of operators of the Krško nuclear power plant (start of operation in 1983) and other nuclear specialists, isotope production and beam applications. The reactor is experiencing a high level of activity today, engaging in a diverse range of experiments and studies across reactor physics, environmental research, radiation hardness testing as well training and education. The future of nuclear technology in Slovenia is focused on new NPPs, while the research community is looking forward to a possible new nuclear reactor. The basic initiatives are at a very preliminary stage: the primary choice is dual-core pool-type reactor, with a zero-power core and a separate MW-size core, cooled and moderated with light water. Such a dual-core configuration is designed to meet the varied requirements of the European Union member states. Another option would be hosting one or more micro-reactors with electrical and/or heating power producing capability that could offer stronger support toward demonstration of prototype small modular reactors in prototype future electrical grids.

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Verification of MCNP6 model of the Jordan Research and Training Reactor (JRTR) for calculations of neutronic parameters

Cited by 15 publications

References 9 publications

Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks

Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks

Conceptual Design of a 10 MW Multipurpose Research Reactor Using VVR-KN Fuel

Advancing Nuclear Research and Education in Slovenia and EU: From Operating the TRIGA Reactor to Building a New Generation Facility

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