• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2026-2033
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• 2023

Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05a
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• pp.27-32
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• 1997

In this paper, a new multigrid method is developed to solve the reactor eigenvalue problems. The new algorithm can be used in any matrix equation concerned with the eigenvalue problem. The finite difference neutron diffusion problem is considered demonstration of the performance of the new multigrid algorithm. The numerical results show that the new multigrid algorithm works well and requires much shorter (7~10 times) computing time compaired to the production code VENTURE.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2107-2119
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• 2022

Accurate remaining useful life (RUL) prediction for critical components of nuclear power equipment is an important way to realize aging management of nuclear power equipment. The electric gate valve is one of the most safety-critical and widely distributed mechanical equipment in nuclear power installations. However, the electric gate valve's extended service in nuclear installations causes aging and degradation induced by crack propagation and leakages. Hence, it is necessary to develop a robust RUL prediction method to evaluate its operating state. Although the particle filter(PF) algorithm and its variants can deal with this nonlinear problem effectively, they suffer from severe particle degeneracy and depletion, which leads to its sub-optimal performance. In this study, we combined the whale algorithm with regularized particle filtering(RPF) to rationalize the particle distribution before resampling, so as to solve the problem of particle degradation, and for valve RUL prediction. The valve's crack propagation is studied using the RPF approach, which takes the Paris Law as a condition function. The crack growth is observed and updated using the root-mean-square (RMS) signal collected from the acoustic emission sensor. At the same time, the proposed method is compared with other optimization algorithms, such as particle swarm optimization algorithm, and verified by the realistic valve aging experimental data. The conclusion shows that the proposed method can effectively predict and analyze the typical valve degradation patterns.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1062-1065
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• 1993

Nuclear reactor operation is a human intensive task; one of the features of a problem for which fuzzy controllers present the most suitable solution. The performance of the fuzzy controllers can further be improved through tuning. In this work, application of a fuzzy controller in real-time control of a nuclear reactor is presented. The fuzzy controller is tuned on-line using direct gradient search method.

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.502-503
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• 2005

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.65-66
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• 2004

• Proceedings of the Korean Nuclear Society Conference
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• 2004.05a
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• pp.59.1-59.1
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• 2004

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.125-126
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• 2005

• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.287-295
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• 2020

Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3388-3400
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• 2023

Detailed analysis of the neutron pathway through matter inside the nuclear reactor core is exceedingly needed for safety and economic considerations. Due to the constant development of high-performance computing technologies, neutronics analysis using computer codes became more effective and efficient to perform sophisticated neutronics calculations. In this work, a commercial pressurized water reactor (PWR) presented by Virtual Environment for Reactor Applications (VERA) Core Physics Benchmark are modeled and simulated using a high-fidelity simulation of OpenMC code in terms of criticality and fuel pin power distribution. Various problems have been selected from VERA benchmark ranging from a simple two-dimension (2D) pin cell problem to a complex three dimension (3D) full core problem. The development of the code capabilities for reactor physics methods has been implemented to investigate the accuracy and performance of the OpenMC code against VERA SCALE codes. The results of OpenMC code exhibit excellent agreement with VERA results with maximum Root Mean Square Error (RMSE) values of less than 0.04% and 1.3% for the criticality eigenvalues and pin power distributions, respectively. This demonstrates the successful utilization of the OpenMC code as a simulation tool for a whole core analysis. Further works are undergoing on the accuracy of OpenMC simulations for the impact of different fuel types and burnup levels and the analysis of the transient behavior and coupled thermal hydraulic feedback.