The inter-cycle correlation of fission source distributions(FSDs)in the Monte Carlo power iteration process results in variance underestimation of tallied physical quantities,especially in large local tallies.This stu...The inter-cycle correlation of fission source distributions(FSDs)in the Monte Carlo power iteration process results in variance underestimation of tallied physical quantities,especially in large local tallies.This study provides a mesh-free semiquantitative variance underestimation elimination method to obtain a credible confidence interval for the tallied results.This method comprises two procedures:Estimation and Elimination.The FSD inter-cycle correlation length is estimated in the Estimation procedure using the Sliced Wasserstein distance algorithm.The batch method was then used in the elimination procedure.The FSD inter-cycle correlation length was proved to be the optimum batch length to eliminate the variance underestimation problem.We exemplified this method using the OECD sphere array model and 3D PWR BEAVRS model.The results showed that the average variance underestimation ratios of local tallies declined from 37 to 87%to within±5%in these models.展开更多
A new reliability allocation model has been built for engine system, which is a repairable system, and consists of a large number of mechanical components. The cost and reliability are taken as objective function and ...A new reliability allocation model has been built for engine system, which is a repairable system, and consists of a large number of mechanical components. The cost and reliability are taken as objective function and constraint condition respectively. The parameters of components lifetime distribution are given as decision variables, and the component lifetimes are assumed to follow that Weibull distribution. The allocation is separated into two steps to reduce calculated amount of one allocation. Genetic algorithm and Monte Carlo method are applied to solve distribution parameters and system cost separately.展开更多
This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method...This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method is efficient.展开更多
We introduce the potential-decomposition strategy （PDS）, which can be used in Markov chain Monte Carlo sampling algorithms. PDS can be designed to make particles move in a modified potential that favors diffusion in...We introduce the potential-decomposition strategy （PDS）, which can be used in Markov chain Monte Carlo sampling algorithms. PDS can be designed to make particles move in a modified potential that favors diffusion in phase space, then, by rejecting some trial samples, the target distributions can be sampled in an unbiased manner. Furthermore, if the accepted trial samples are insumcient, they can be recycled as initial states to form more unbiased samples. This strategy can greatly improve efficiency when the original potential has multiple metastable states separated by large barriers. We apply PDS to the 2d Ising model and a double-well potential model with a large barrier, demonstrating in these two representative examples that convergence is accelerated by orders of magnitude.展开更多
In tomographic statics seismic data processing, it 1s crucial to cletermme an optimum base for a near-surface model. In this paper, we consider near-surface model base determination as a global optimum problem. Given ...In tomographic statics seismic data processing, it 1s crucial to cletermme an optimum base for a near-surface model. In this paper, we consider near-surface model base determination as a global optimum problem. Given information from uphole shooting and the first-arrival times from a surface seismic survey, we present a near-surface velocity model construction method based on a Monte-Carlo sampling scheme using a layered equivalent medium assumption. Compared with traditional least-squares first-arrival tomography, this scheme can delineate a clearer, weathering-layer base, resulting in a better implementation of damming correction. Examples using synthetic and field data are used to demonstrate the effectiveness of the proposed scheme.展开更多
本文提出了一种新的延时累加算法。基于底层的JCOGIN(J combinatorial geometry Monte Carlo transport infrastructure)框架和新的延时累加算法,通用型Monte Carlo中子-光子输运模拟软件JMCT的计数能力得到了较大提高。对所考察的非重...本文提出了一种新的延时累加算法。基于底层的JCOGIN(J combinatorial geometry Monte Carlo transport infrastructure)框架和新的延时累加算法,通用型Monte Carlo中子-光子输运模拟软件JMCT的计数能力得到了较大提高。对所考察的非重复结构的单层几何模型问题,JMCT的计数效率较MCNP 4C程序所采用的list scoring技巧高约28%;对于较复杂的重复结构几何模型问题,JMCT的大规模精细计数效率比MCNP 4C高约两个量级。JMCT目前的计数能力为反应堆物理分析及多燃耗步计算奠定了良好的基础。展开更多
EM算法是近年来常用的求后验众数的估计的一种数据增广算法,但由于求出其E步中积分的显示表达式有时很困难,甚至不可能,限制了其应用的广泛性.而Monte Carlo EM算法很好地解决了这个问题,将EM算法中E步的积分用Monte Carlo模拟来有效实...EM算法是近年来常用的求后验众数的估计的一种数据增广算法,但由于求出其E步中积分的显示表达式有时很困难,甚至不可能,限制了其应用的广泛性.而Monte Carlo EM算法很好地解决了这个问题,将EM算法中E步的积分用Monte Carlo模拟来有效实现,使其适用性大大增强.但无论是EM算法,还是Monte Carlo EM算法,其收敛速度都是线性的,被缺损信息的倒数所控制,当缺损数据的比例很高时,收敛速度就非常缓慢.而Newton-Raphson算法在后验众数的附近具有二次收敛速率.本文提出Monte Carlo EM加速算法,将Monte Carlo EM算法与Newton-Raphson算法结合,既使得EM算法中的E步用Monte Carlo模拟得以实现,又证明了该算法在后验众数附近具有二次收敛速度.从而使其保留了Monte Carlo EM算法的优点,并改进了Monte Carlo EM算法的收敛速度.本文通过数值例子,将Monte Carlo EM加速算法的结果与EM算法、Monte Carlo EM算法的结果进行比较,进一步说明了Monte Carlo EM加速算法的优良性.展开更多
随机算法在组合优化问题中具有广泛的应用 ,L as Vegas算法和 Monte Carlo算法是主要的两类随机算法 .随机算法的性能和稳定性常常得不到保证 ,以往的研究针对 L as Vegas算法提出了一种有效的性能改进策略——随机竞争策略 ,但其在 Mon...随机算法在组合优化问题中具有广泛的应用 ,L as Vegas算法和 Monte Carlo算法是主要的两类随机算法 .随机算法的性能和稳定性常常得不到保证 ,以往的研究针对 L as Vegas算法提出了一种有效的性能改进策略——随机竞争策略 ,但其在 Monte Carlo算法中的性能尚未被研究 .文中研究了随机竞争策略对 Monte Carlo算法性能和稳定性的影响 ,分析了使其效率大于 1的条件 ,在求解 TSP问题时的实验结果显示该策略具有显著的应用价值 ,在同等时间内能够将解的质量提高一倍以上 .展开更多
基金国家自然科学基金(52001148)江西省自然科学基金委(20201BBE51015)+2 种基金江西省科学院基金(2019-YYB-07,2019-XTPH1-07,2020-YZD-18,2020-YZD-20 and 2021YSBG22021)江西省科技合作专项项目(20212BDH81015)江西省重点研发计划重点项目(20202BBE52002)。
基金supported by China Nuclear Power Engineering Co.,Ltd.Scientific Research Project(No.KY22104)the fellowship of China Postdoctoral Science Foundation(No.2022M721793).
文摘The inter-cycle correlation of fission source distributions(FSDs)in the Monte Carlo power iteration process results in variance underestimation of tallied physical quantities,especially in large local tallies.This study provides a mesh-free semiquantitative variance underestimation elimination method to obtain a credible confidence interval for the tallied results.This method comprises two procedures:Estimation and Elimination.The FSD inter-cycle correlation length is estimated in the Estimation procedure using the Sliced Wasserstein distance algorithm.The batch method was then used in the elimination procedure.The FSD inter-cycle correlation length was proved to be the optimum batch length to eliminate the variance underestimation problem.We exemplified this method using the OECD sphere array model and 3D PWR BEAVRS model.The results showed that the average variance underestimation ratios of local tallies declined from 37 to 87%to within±5%in these models.
文摘A new reliability allocation model has been built for engine system, which is a repairable system, and consists of a large number of mechanical components. The cost and reliability are taken as objective function and constraint condition respectively. The parameters of components lifetime distribution are given as decision variables, and the component lifetimes are assumed to follow that Weibull distribution. The allocation is separated into two steps to reduce calculated amount of one allocation. Genetic algorithm and Monte Carlo method are applied to solve distribution parameters and system cost separately.
文摘This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method is efficient.
基金Supported by the National Natural Science Foundation of China under Grant Nos.10674016,10875013the Specialized Research Foundation for the Doctoral Program of Higher Education under Grant No.20080027005
文摘We introduce the potential-decomposition strategy （PDS）, which can be used in Markov chain Monte Carlo sampling algorithms. PDS can be designed to make particles move in a modified potential that favors diffusion in phase space, then, by rejecting some trial samples, the target distributions can be sampled in an unbiased manner. Furthermore, if the accepted trial samples are insumcient, they can be recycled as initial states to form more unbiased samples. This strategy can greatly improve efficiency when the original potential has multiple metastable states separated by large barriers. We apply PDS to the 2d Ising model and a double-well potential model with a large barrier, demonstrating in these two representative examples that convergence is accelerated by orders of magnitude.
基金funded by the National Science VIP specialized project of China(Grant No.2011ZX05025-001-03)by the National Science Foundation of China(Grant No.41274117)
文摘In tomographic statics seismic data processing, it 1s crucial to cletermme an optimum base for a near-surface model. In this paper, we consider near-surface model base determination as a global optimum problem. Given information from uphole shooting and the first-arrival times from a surface seismic survey, we present a near-surface velocity model construction method based on a Monte-Carlo sampling scheme using a layered equivalent medium assumption. Compared with traditional least-squares first-arrival tomography, this scheme can delineate a clearer, weathering-layer base, resulting in a better implementation of damming correction. Examples using synthetic and field data are used to demonstrate the effectiveness of the proposed scheme.
文摘本文提出了一种新的延时累加算法。基于底层的JCOGIN(J combinatorial geometry Monte Carlo transport infrastructure)框架和新的延时累加算法,通用型Monte Carlo中子-光子输运模拟软件JMCT的计数能力得到了较大提高。对所考察的非重复结构的单层几何模型问题,JMCT的计数效率较MCNP 4C程序所采用的list scoring技巧高约28%;对于较复杂的重复结构几何模型问题,JMCT的大规模精细计数效率比MCNP 4C高约两个量级。JMCT目前的计数能力为反应堆物理分析及多燃耗步计算奠定了良好的基础。
文摘EM算法是近年来常用的求后验众数的估计的一种数据增广算法,但由于求出其E步中积分的显示表达式有时很困难,甚至不可能,限制了其应用的广泛性.而Monte Carlo EM算法很好地解决了这个问题,将EM算法中E步的积分用Monte Carlo模拟来有效实现,使其适用性大大增强.但无论是EM算法,还是Monte Carlo EM算法,其收敛速度都是线性的,被缺损信息的倒数所控制,当缺损数据的比例很高时,收敛速度就非常缓慢.而Newton-Raphson算法在后验众数的附近具有二次收敛速率.本文提出Monte Carlo EM加速算法,将Monte Carlo EM算法与Newton-Raphson算法结合,既使得EM算法中的E步用Monte Carlo模拟得以实现,又证明了该算法在后验众数附近具有二次收敛速度.从而使其保留了Monte Carlo EM算法的优点,并改进了Monte Carlo EM算法的收敛速度.本文通过数值例子,将Monte Carlo EM加速算法的结果与EM算法、Monte Carlo EM算法的结果进行比较,进一步说明了Monte Carlo EM加速算法的优良性.
文摘随机算法在组合优化问题中具有广泛的应用 ,L as Vegas算法和 Monte Carlo算法是主要的两类随机算法 .随机算法的性能和稳定性常常得不到保证 ,以往的研究针对 L as Vegas算法提出了一种有效的性能改进策略——随机竞争策略 ,但其在 Monte Carlo算法中的性能尚未被研究 .文中研究了随机竞争策略对 Monte Carlo算法性能和稳定性的影响 ,分析了使其效率大于 1的条件 ,在求解 TSP问题时的实验结果显示该策略具有显著的应用价值 ,在同等时间内能够将解的质量提高一倍以上 .
