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Approximation to the covariance matrix for stochastic point kinetics


Article Information

Title: Approximation to the covariance matrix for stochastic point kinetics

Authors: Daniel Suescún-Díaz, Daniel E. Cedeño-Giron, Freddy Humberto Escobar

Journal: ARPN Journal of Engineering and Applied Sciences

HEC Recognition History
Category From To
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30
Y 2021-07-01 2022-06-30
X 2020-07-01 2021-06-30

Publisher: Khyber Medical College, Peshawar

Country: Pakistan

Year: 2020

Volume: 15

Issue: 4

Language: English

Categories

Abstract

In this work, the square root of the matrix of variances in stochastic point kinetics is analytically deduced using Cholesky decomposition. The system under study is solved numerically using the implicit Milstein scheme, the variance of the neutron population density and the concentration of precursors can be reduced, and better approximations of the expected values are obtained through the implementation of a new independent Brownian motion. The results obtained comprise different configurations of the reactivity parameters, precursor population, time steps, Brownian motion and initial conditions. The results were compared with those reported in the literature, being consistent with them, which is a manifestation of the efficiency of the proposed method.


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