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    Inline critical boron concentration search with p-CMFD feedback in whole-core continuous-energy Monte Carlo simulation

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    For the pressurized water reactors (PWRs) depletion analysis, the critical boron concentration (CBC) search is required. In the Monte Carlo (MC) simulation, the CBC can be estimated by the neutron balance approach, which is called as the inline CBC search method in this paper. This method is based on the fixed-point iteration, which results in a number of inactive cycles for the MC simulation. For the acceleration of the convergence and real variance reduction, the p-CMFD feedback is applied to the inline CBC search method. Furthermore, a rejection technique is introduced in the p-CMFD calculation to suppress the error propagation from the p-CMFD parameters to the p-CMFD solution. For a near critical system with zero boron concentration, the inline CBC search method may get in trouble with the negative boron concentration. To solve this problem, the extended particle-tracking algorithm is proposed and verified in a simplified toy problem. In realistic PWR test problems, the numerical results show that the proposed method accurately estimates the CBCs, while the p-CMFD feedback effectively reduces the number of inactive cycles. In conclusion, for the PWR depletion analysis, the continuous-energy MC simulation under the CBC condition can be efficiently performed by the inline CBC search method with the p-CMFD feedback. (C) 2018 Elsevier Ltd. All rights reserved.

    원자력발전소의 저층 성능 기준설정과 불확실성에 대하여

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    This paper addresses the issues in setting performance criteria for safety regulation of nuclear power plants. Since setting criteria at the low level is a much more difficult task than it is at the top level, the low-level performance criteria should be derived consistently from the more easily determinable top-level performance criteria. The paper also proposes several approaches to characterizing uncertainties in performance criteria, by extending the reliability allocation methodology that is based on the mean-to-mean mapping to a stochastic multi-objective optimization problem where the state variables are uncertain
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