Work

Monte Carlo Power Iteration Algorithms: Convergence, Analysis and Application to Reactor Criticality Calculations

Year: 2026

Type: preprint

Abstract: We study Monte Carlo power iteration algorithms in the context of reactor criticality simulations. Under mild conditions on the underlying physical system and the algorithmic structure, we derive explicit error bounds, which are to the best of our knowledge the first rigorous results for such methods which are ubiquitous in nuclear engineering. This is done by exploiting connections to Sequential Monte Carlo algorithms, and extending recent developments about stochastic neutron transport. We demonstrate the applicability of our results by considering a concrete version of the Monte Carlo power iteration algorithm as well as variance reduction schemes.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.36266

Authors: Cox, Alexander M. G., Davis, Tom, Hofstadler, Julian, Kyprianou, Andreas E.

Institutions: University of Bath, University of Warwick

Language: English

Topic: Nuclear reactor physics and engineering

Subfield: Nuclear Energy and Engineering

Field: Energy

Domain: Physical Sciences

Open Access status: green

Funders: Engineering and Physical Sciences Research Council, Centre for Doctoral Training in Statistical Applied Mathematics, University of Bath

Awards: G2717065481, Stochastic analysis of the neutron transport equation and applications to nuclear safety, Mathematical Theory of Radiation Transport: Nuclear Technology Frontiers (MaThRad), EPSRC Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa), G876242530

OpenAlex ID: W7224090112

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