• Nuclear Engineering and Technology
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• v.37 no.1
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• pp.25-78
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• 2005

As a key and core knowledge for the design of various types of nuclear reactors, the discipline of reactor physics has been advanced continually in the past six decades and has led to a very sophisticated fabric of analysis methods and computer codes in use today. Notwithstanding, the discipline faces interesting challenges from next-generation nuclear reactors and innovative new fuel designs in the coming. After presenting a brief overview of important tasks and steps involved in the nuclear design and analysis of a reactor, this article focuses on the currently-used design and analysis methods, issues and limitations, and current activities to resolve them as follows: (1) Derivation of the multi group transport equations and the multi group diffusion equations, with representative solution methods thereof. (2) Elements of modem (now almost three decades old) diffusion nodal methods. (3) Limitations of nodal methods such as transverse integration, flux reconstruction, and analysis of UO2-MOX mixed cores. Homogenization and related issues. (4) Description of the analytic function expansion nodal (AFEN) method. (5) Ongoing efforts for three-dimensional whole-core heterogeneous transport calculations and acceleration methods. (6) Elements of spatial kinetics calculation methods and coupled neutronics and thermal-hydraulics transient analysis. (7) Identification of future research and development areas in advanced reactors and Generation-IV reactors, in particular, in very high temperature gas reactor (VHTR) cores.

• Nuclear Engineering and Technology
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• v.52 no.6
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• pp.1208-1212
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• 2020

Knowledge on fuel enthalpy and its temperature derivative, the heat capacity, are important quantities in determination of fuel behavior in normal reactor operation and reactor transients. The aim of this study is to compare the heat capacity of oxide and nitrite fuels by using Einstein-Debye approximation. A simple analytical expression was performed to calculate the heat capacity of fuels. To test the validity and reliability, the calculated formulas were compared to published results for various nuclear fuels including UO₂, ThO₂, PuO₂ and UN. Calculated formulas yielded results in consistent with literature.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2753-2760
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• 2021

This paper proposes using sodium-cooled fast reactor technologies for use in hydrogen vapor methane (SMR) modification. Using three independent energy rings in the Russian BN-600 fast reactor, steam is generated in one of the steam-generating cycles with a pressure of 13.1 MPa and a temperature of 505 ℃. The reactor's second energy cycles can increase the gas-steam mixture's temperature to the required amount for efficient correction. The 620 ton/hr 540 ℃ steam generated in this cycle is sufficient to supply a high-temperature synthesis current source (700 ℃), which raises the steam-gas mixture's temperature in the reactor. The proposed technology provides a high rate of hydrogen production (approximately 144.5 ton/hr of standard H₂), also up to 25% of the original natural gas, in line with existing SMR technology for preparing and heating steam and gas mixtures will be saved. Also, exergy analysis results show that the plant's efficiency reaches 78.5% using HTR heat for combined hydrogen and power generation.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.30-35
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• 2022

In a nuclear reactor, gamma radiation is the dominant energy deposition in non-fuel regions. Heat is generated upon gamma deposition and consequently affects the mechanical and thermal structure of the material. Therefore, the safety of samples should be carefully considered so that their integrity and quality can be retained. To evaluate relevant parameters, an in-core gamma thermometer (GT) was used to measure gamma heating (GH) throughout the operation of the McMaster nuclear reactor (MNR) at four irradiation sites. Additionally, a Monte Carlo reactor physics code (Serpent-2) was utilized to model the MNR with the GT located in the same irradiation sites used in the measurement to verify its predictions against measured GH. This research aids in the development of modeling, calculation, and prediction of the GH utilizing Serpent-2 as well as implementing a new GH measurement at the MNR core. After all uncertainties were quantified for both approaches, comparable GH profiles were observed between the measurements and calculations. In addition, the GH values found in the four sites represent a strong level of radiation based on the distance of the sample from the core. In this study, the maximum and minimum GH values were found at 0.32 ± 0.05 W/g and 0.15 ± 0.02 W/g, respectively, corresponding to 320 Sv/s and 150 Sv/s. These values are crucial to be considered whenever sample is planned to be irradiated inside the MNR core.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.170-175
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• 2019

Displacement cross-sections for an advanced assessment of radiation damage rates were obtained for a number of materials using the arc-dpa model at neutron incident energies from $10^{-5}eV$ to 10 GeV. Evaluated data files, CEM03 and ECIS codes, and an approximate approach were applied for the calculation of recoil energy distributions in neutron induced reactions. Three sets of displacement cross-sections based on the use of low-energy data from JEFF-3.3, ENDF/B-VIII.0, and JENDL-4.0u were prepared. Files contain also cross-sections calculated using the standard NRT model. Special efforts were made to estimate the uncertainty of obtained displacement cross-sections.

• Nuclear Engineering and Technology
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• v.51 no.5
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• pp.1181-1194
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• 2019

A new moving-mesh Finite Volume Method (FVM) for the efficient solution of the two-dimensional neutron diffusion equation is introduced. Many other moving-mesh methods developed to solve the neutron diffusion problems use a relatively large number of sophisticated mathematical equations, and so suffer from a significant complexity of mathematical calculations. In this study, the proposed method is formulated based on simple mathematical algebraic equations that enable an efficient mesh movement and CV deformation for using in practical nuclear reactor applications. Accordingly, a computational framework relying on a new moving-mesh FVM is introduced to efficiently distribute the meshes and deform the CVs in regions with high gradient variations of reactor power. These regions of interest are very important in the neutronic assessment of the nuclear reactors and accordingly, a higher accuracy of the power densities is required to be obtained. The accuracy, execution time and finally visual comparison of the proposed method comprehensively investigated and discussed for three different benchmark problems. The results all indicated a higher accuracy of the proposed method in comparison with the conventional fixed-mesh FVM.

• Nuclear Engineering and Technology
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• v.52 no.1
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• pp.206-210
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• 2020

Super fine-grained graphite is a type of advanced nuclear graphite which was developed for Molten Salt Reactor (MSR). It is necessary to establish a failure assessment method used for nuclear graphite components in MSR. A modified assessment approach based on ASME BPVC-III-5_2017 is presented. The new approach takes a new parameter, KIC, into account and abandons the parameter, grain size, which is unrealistic for super fine-grained graphite as the computation is enormous if we use conventional methods. Three methodologies (KTA 3232, ASME, New approach) were also evaluated by theoretical prediction and experimental verification. The results indicated the new developed code can be used for design and failure assessment of super fine-graphite components and has more extensive applicability.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.135-136
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• 2004

• Proceedings of the Korean Nuclear Society Conference
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• 1999.10a
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• pp.66.1-66
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• 1999