• 시스템엔지니어링학술지
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• 제16권2호
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• pp.38-46
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• 2020

The accurate measurement of critical heat flux (CHF) in flow boiling is important for the safety requirement of the nuclear power plant to prevent sharp degradation of the convective heat transfer between the surface of the fuel rod cladding and the reactor coolant. In this paper, a System Engineering approach is used to develop a model that predicts the CHF using machine learning. The model is built using artificial neural network (ANN). The model is then trained, tested and validated using pre-existing database for different flow conditions. The Talos library is used to tune the model by optimizing the hyper parameters and selecting the best network architecture. Once developed, the ANN model can predict the CHF based solely on a set of input parameters (pressure, mass flux, quality and hydraulic diameter) without resorting to any physics-based model. It is intended to use the developed model to predict the DNBR under a large break loss of coolant accident (LBLOCA) in APR1400. The System Engineering approach proved very helpful in facilitating the planning and management of the current work both efficiently and effectively.

• 에너지공학
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• 제16권4호
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• pp.155-163
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• 2007

원자로의 안전성 확보를 위해 재장전 기간 동안 수행되는 노물리 시험에서 제어봉의 반응도가(reactivity worth) 산출을 위해 노심의 임계도를 측정해야 하고, 기동운전 시에도 반응도 사고를 대비하여 미임계도가 감시되어야 한다. 미임계도나 제어봉가 측정을 위한 연구가 국내외적으로 지속되어 왔으며, 최근에는 일본에서 "개선된 중성자 선원 증배법(Modified Neutron Source Multiplication Method, MNSM)"이 제안되어 기존의 중성자 선원 증배법의 한계를 극복하였다. 본 연구에서는 MNSM을 경희대 교육용원자로 AGN-201에 적용하여 미임계도를 계산하고 새로운 방법의 타당성을 평가하였다. MNSM의 적용을 위해 AGN-201 원자로에 적합한 핵자료집과 중성자수송 전산코드인 TRANSX - PARTISN 체계를 구축하였고, 유효증배계수와 중성자속(flux) 분포, 수반 중성자속(adjoint flux) 분포 등을 계산하여 제어봉위치에 따른 보정인자들을 산출하였다. 원자로의 미임계도 측정값은 $BF_3$ 비례계수관으로 측정한 중성자계수율을 사용하여 확보하였다. 연구 결과로서 MNSM을 사용하여 평가한 미임계도가 전산코드로 계산하여 얻어진 이론적인 미임계도 값에 근접하고 계산된 보정인자도 유효함을 확인하였다.

• Nuclear Engineering and Technology
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• 제55권4호
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• pp.1563-1570
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• 2023

In this study, a DeCART/MIG uncertainty quantification (UQ) analysis code system with a multicorrelated cross section stochastic sampling (S.S.) module was established and verified through the UAM (Uncertainty Analysis in Modeling) and the BEAVRS (Benchmark for Evaluation And Validation of Reactor Simulations) benchmark calculations. For the S.S. calculations, a sample of 500 DeCART multigroup cross section sets for two major actinides, i.e., ²³⁵U and ²³⁸U, were generated by the MIG code and covariance data from the ENDF/B-VII.1 evaluated nuclear data library. In the three pin problems (i.e. TMI-1, PB2, and Koz-6) from the UAM benchmark, the uncertainties in k_inf by the DeCART/MIG S.S. calculations agreed very well with the sensitivity and uncertainty (S/U) perturbation results by DeCART/MUSAD and the S/U direct subtraction (S/U-DS) results by the DeCART/MIG. From these results, it was concluded that the multi-group cross section sampling module of the MIG code works correctly and accurately. In the BEAVRS whole benchmark problems, the uncertainties in the control rod bank worth, isothermal temperature coefficient, power distribution, and critical boron concentration due to cross section uncertainties were calculated by the DeCART/MIG code system. Overall, the uncertainties in these design parameters were less than the general design review criteria of a typical pressurized water reactor start-up case. This newly-developed DeCART/MIG UQ analysis code system by the S.S. method can be widely utilized as uncertainty analysis and margin estimation tools for developing and designing new advanced nuclear reactors.