The accurate modeling of depletion,intricately tied to the solution of the neutron transport equation,is crucial for the design,analysis,and licensing of nuclear reactors and their fuel cycles.This paper introduces a ...The accurate modeling of depletion,intricately tied to the solution of the neutron transport equation,is crucial for the design,analysis,and licensing of nuclear reactors and their fuel cycles.This paper introduces a novel multi-group Monte-Carlo depletion calculation approach.Multi-group cross-sections(MGXS)are derived from both 3D whole-core model and 2D fuel subassembly model using the continuous-energy Monte-Carlo method.Core calculations employ the multi-group Monte-Carlo method,accommodating both homogeneous and specific local heterogeneous geometries.The proposed method has been validated against the MET-1000 metal-fueled fast reactors,using both the OECD/NEA benchmark and a new refueling benchmark introduced in this paper.Our findings suggest that microscopic MGXS,produced via the Monte-Carlo method,are viable for fast reactor depletion analyses.Furthermore,the locally heterogeneous model with angular-dependent MGXS offers robust predictions for core reactivity,control rod value,sodium void value,Doppler constants,power distribution,and concentration levels.展开更多
This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a rand...This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.展开更多
Reconfigurable intelligent surface(RIS)employs passive beamforming to control the wireless propagation channel,which benefits the wireless communication capacity and the received energy efficiency of wireless power tr...Reconfigurable intelligent surface(RIS)employs passive beamforming to control the wireless propagation channel,which benefits the wireless communication capacity and the received energy efficiency of wireless power transfer(WPT)systems.Such beamforming schemes are classified as discrete and non-convex integer program-ming problems.In this paper,we propose a Monte-Carlo(MC)based random energy passive beamforming of RIS to achieve the maximum received power of electromagnetic(EM)WPT systems.Generally,the Gibbs sampling and re-sampling methods are employed to generate phase shift vector samples.And the sample with the maximum received power is considered the optimal solution.In order to adapt to the application scenarios,we develop two types of passive beamforming algorithms based on such MC sampling methods.The first passive beamforming uses an approximation of the integer programming as the initial sample,which is calculated based on the channel information.And the second one is a purely randomized algorithm with the only total received power feedback.The proposed methods present several advantages for RIS control,e.g.,fast convergence,easy implementation,robustness to the channel noise,and limited feedback requirement,and they are applicable even if the channel information is unknown.According to the simulation results,our proposed methods outperform other approxi-mation and genetic algorithms.With our methods,the WPT system even significantly improves the power effi-ciency in the nonline-of-sight(NLOS)environment.展开更多
基金supported by the National Natural Science Foundation of China(Nos.12105170,12135008)Science and Technology on Reactor System Design Technology Laboratory.
文摘The accurate modeling of depletion,intricately tied to the solution of the neutron transport equation,is crucial for the design,analysis,and licensing of nuclear reactors and their fuel cycles.This paper introduces a novel multi-group Monte-Carlo depletion calculation approach.Multi-group cross-sections(MGXS)are derived from both 3D whole-core model and 2D fuel subassembly model using the continuous-energy Monte-Carlo method.Core calculations employ the multi-group Monte-Carlo method,accommodating both homogeneous and specific local heterogeneous geometries.The proposed method has been validated against the MET-1000 metal-fueled fast reactors,using both the OECD/NEA benchmark and a new refueling benchmark introduced in this paper.Our findings suggest that microscopic MGXS,produced via the Monte-Carlo method,are viable for fast reactor depletion analyses.Furthermore,the locally heterogeneous model with angular-dependent MGXS offers robust predictions for core reactivity,control rod value,sodium void value,Doppler constants,power distribution,and concentration levels.
基金supported by the National Natural Science Foundation of China(Grant No.12002246 and No.52178301)Knowledge Innovation Program of Wuhan(Grant No.2022010801020357)+2 种基金the Science Research Foundation of Wuhan Institute of Technology(Grant No.K2021030)2020 annual Open Fund of Failure Mechanics&Engineering Disaster Prevention and Mitigation,Key Laboratory of Sichuan Province(Sichuan University)(Grant No.2020JDS0022)Open Research Fund Program of Hubei Provincial Key Laboratory of Chemical Equipment Intensification and Intrinsic Safety(Grant No.2019KA03)。
文摘This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.
基金supported by National Nature Science Foundation of China(No.62171484)Zhuhai Fundamental and Application Research(No.ZH22017003210006PWC)Fundamental Research Funds for the Central Universities(No.21621420).
文摘Reconfigurable intelligent surface(RIS)employs passive beamforming to control the wireless propagation channel,which benefits the wireless communication capacity and the received energy efficiency of wireless power transfer(WPT)systems.Such beamforming schemes are classified as discrete and non-convex integer program-ming problems.In this paper,we propose a Monte-Carlo(MC)based random energy passive beamforming of RIS to achieve the maximum received power of electromagnetic(EM)WPT systems.Generally,the Gibbs sampling and re-sampling methods are employed to generate phase shift vector samples.And the sample with the maximum received power is considered the optimal solution.In order to adapt to the application scenarios,we develop two types of passive beamforming algorithms based on such MC sampling methods.The first passive beamforming uses an approximation of the integer programming as the initial sample,which is calculated based on the channel information.And the second one is a purely randomized algorithm with the only total received power feedback.The proposed methods present several advantages for RIS control,e.g.,fast convergence,easy implementation,robustness to the channel noise,and limited feedback requirement,and they are applicable even if the channel information is unknown.According to the simulation results,our proposed methods outperform other approxi-mation and genetic algorithms.With our methods,the WPT system even significantly improves the power effi-ciency in the nonline-of-sight(NLOS)environment.
