A multi-fidelity adaptive dynamical low-rank based optimization algorithm for fission criticality problems

TL;DR

This paper introduces a multi-fidelity, adaptive low-rank optimization algorithm for efficiently computing the dominant eigenvalue in nuclear criticality problems, reducing computational costs through dynamic rank adjustment during inverse power iteration.

Contribution

It proposes a novel rank adaptive method that dynamically adjusts model fidelity during eigenvalue computation, improving efficiency in nuclear system optimization.

Findings

Effective reduction in computational cost demonstrated

Adaptive rank approach accelerates convergence

Applicable to parameterized nuclear reactor optimization

Abstract

Computing the dominant eigenvalue is important in nuclear systems as it determines the stability of the system (i.e. whether the system is sub or supercritical). Recently, the work of Kusch, Whewell, McClarren and Frank \cite{KWMF} showed that performing a low-rank approximation can be very effective in reducing the high memory requirement and computational cost of such problems. In this work, we propose a rank adaptive approach that changes the rank during the inverse power iteration. This allows us to progressively increase the rank (i.e. changing the fidelity of the model) as we get closer to convergence, thereby further reducing computational cost. We then exploit this multi-fidelity approach to optimize a simplified nuclear reactor. In this case the system is parameterized and the values of the parameters that give criticality are sought.