A New Model for the Stochastic Point Reactor: Development and Comparison with Available Models

Abstract

The point kinetic model is a system of differential equations that enables analysis of reactor dynamics without the need to solve coupled space-time system of partial differential equations (PDEs). The random variations, especially during the startup and shutdown, may become severe and hence should be accounted for in the reactor model. There are two well-known stochastic models for the point reactor that can be used to estimate the mean and variance of the neutron and precursor populations. In this paper, we reintroduce a new stochastic model for the point reactor, which we named the Langevin point kinetic model (LPK). The new LPK model combines the advantages, accuracy, and efficiency of the available models. The derivation of the LPK model is outlined in detail, and many test cases are analyzed to investigate the new model compared with the results in the literature.

On the Zero-Neutron Density in Stochastic Nuclear Dynamics

“…These results suggest that when the source is reduced, the stochastic model moves away from the deterministic one and, above all, the neutrons disappear much earlier. However, in [20,21] an alternative stochastic formulation was proposed based on the classical Chemical Calgevin Method (e.g., [22][23][24][25]). However, in my opinion this statement is not correct.…”