• Nuclear Engineering and Technology
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• 제52권1호
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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.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1997년도 추계학술발표회논문집(1)
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• pp.85-90
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• 1997

Previous our numerical results in computing point kinetics equations show a possibility in developing approximations to estimate sensitivity responses of nuclear reactor We recalculate sensitivity responses by maintaining the corrections with first order of sensitivity parameter. We present a method for computing sensitivity responses of nuclear reactor based on an approximation derived from point kinetics equations. Exploiting this approximation, we found that the first order approximation works to estimate variations in the time to reach peak power because of their linear dependence on a sensitivity parameter, and that there are errors in estimating the peak power in the first order approximation for larger sensitivity parameters. To confirm legitimacy of our approximation, these approximate results are compared with exact results obtained from our previous numerical study.

• Nuclear Engineering and Technology
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• 제44권3호
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• pp.249-260
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• 2012

Main focus of the combined nuclear research activities at Aachen University (RWTH) and the Research Center J$\ddot{u}$lich (J$\ddot{U}$LICH) is the experimental and analytical investigation of containment phenomena and processes. We are deeply convinced that reliable simulations for operation, design basis and beyond-design basis accidents of nuclear power plants need the application of so-called lumped-parameter (LP) based codes as well as computational fluid dynamics (CFD) codes in an indispensable manner. The LP code being used at our institutions is the GRS code COCOSYS and the CFD tool is ANSYS CFX mostly used in German nuclear research. Both codes are applied for safety analyses especially of beyond design accidents. Focal point of the work is containment thermal-hydraulics, but source term relevant investigations for aerosol and iodine behavior are performed as well. To increase the capability of COCOSYS and CFX detailed models for specific features, e.g. recombiner behavior including chimney effect, building condenser, and wall condensation are developed and validated against facilities at different scales. The close connection between analytical and experimental activities is notable and identifying feature of the RWTH/J$\ddot{U}$LICH activities.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1775-1782
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• 2023

Several reactor physics commissioning experiments were conducted to obtain the neutronic parameters at the beginning of the G.A. Siwabessy Multi-purpose Reactor (RSG-GAS) operation. These parameters are essential for the reactor to safety operate. Leveraging the experimental data, this study evaluated the calculated core reactivity, control rod reactivity worth, integral control rod reactivity curve, and fuel reactivity. Calculations were carried out with Serpent 2 code using the latest neutron cross-section data ENDF/B-VIII.0. The criticality calculations were carried out for the RSG-GAS first core up to the third core configuration, which has been done experimentally during these commissioning periods. The excess reactivity for the second and third cores showed a difference of 510.97 pcm and 253.23 pcm to the experiment data. The calculated integral reactivity of the control rod has an error of less than 1.0% compared to the experimental data. The calculated fuel reactivity value is consistent with the measured data, with a maximum error of 2.12%. Therefore, it can be concluded that the RSG-GAS reactor core model is in good agreement to reproduce excess reactivity, control rod worth, and fuel element reactivity.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1998년도 추계학술발표회요약집
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• pp.73-73
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• 1998

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1999년도 추계학술발표회요약집
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• pp.61-61
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• 1999

• Nuclear Engineering and Technology
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• 제42권6호
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• pp.690-703
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• 2010

Sampling based uncertainty analysis was carried out to quantify uncertainty in predictions of best estimate code RELAP5/MOD3.2 for a thermal hydraulic test (10% hot leg break LOCA) performed in the Large Scale Test Facility (LSTF) as a part of an IAEA coordinated research project. The nodalisation of the test facility was qualified for both steady state and transient level by systematically applying the procedures led by uncertainty methodology based on accuracy extrapolation (UMAE); uncertainty analysis was carried out using the Latin hypercube sampling (LHS) method to evaluate uncertainty for ten input parameters. Sixteen output parameters were selected for uncertainty evaluation and uncertainty band between $5^{th}$ and $95^{th}$ percentile of the output parameters were evaluated. It was observed that the uncertainty band for the primary pressure during two phase blowdown is larger than that of the remaining period. Similarly, a larger uncertainty band is observed relating to accumulator injection flow during reflood phase. Importance analysis was also carried out and standard rank regression coefficients were computed to quantify the effect of each individual input parameter on output parameters. It was observed that the break discharge coefficient is the most important uncertain parameter relating to the prediction of all the primary side parameters and that the steam generator (SG) relief pressure setting is the most important parameter in predicting the SG secondary pressure.

• Nuclear Engineering and Technology
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• 제55권11호
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• pp.4102-4111
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• 2023

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

• Nuclear Engineering and Technology
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• 제56권6호
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• pp.1989-2001
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• 2024

Artificial intelligence (AI) techniques are now being considered in the nuclear field, but application faces with the lack of actual plant data. For this reason, most previous studies on AI applications in nuclear power plants (NPPs) have relied on simulators or thermal-hydraulic codes to mimic the plants. However, it remains uncertain whether an AI model trained using a simulator can properly work in an actual NPP. To address this issue, this study suggests the use of metadata, which can give information about parameter trends. Referred to here as robust AI, this concept started with the idea that although the absolute value of a plant parameter differs between a simulator and actual NPP, the parameter trend is identical under the same scenario. Based on the proposed robust AI, this study designs an event diagnosis algorithm to classify abnormal and emergency scenarios in NPPs using prototypical learning. The algorithm was trained using a simulator referencing a Westinghouse 990 MWe reactor and then tested in different environments in Advanced Power Reactor 1400 MWe simulators. The algorithm demonstrated robustness with 100 % diagnostic accuracy (117 out of 117 scenarios). This indicates the potential of the robust AI-based algorithm to be used in actual plants.

• 한국에너지공학회:학술대회논문집
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• 한국에너지공학회 1994년도 추계학술발표회 초록집
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• pp.146-149
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• 1994

A nonlinear dynamic optimization has been performed for reactor power control system of CANDU 6 nuclear power plant considering xenon, fuel and moderator temperature feedback effects. Integral-of-Time-multiplied Absolute-Error (ITAE) criterion has been used as a performance index of the system behavior. Optimum controller gain are found by searching algorithm of Sequential Quadratic Programming (SQP). System models are referenced from most recent literatures. Signal flow network construction and optimization have been done by using commercial computer software package.