Monte Carlo simulations allow for accurately solving the neutron transport equation. However, the uncertainty associated with each estimation has a slow (ideal) convergence rate. As a result, efforts have been mostly devoted to criticality calculations where the system is in steady state. When analysing the transient behaviour of a reactor in the context of safety and stability, accounting for neutron transport in the presence of multi-physics phenomena becomes relevant. This is known as Dynamic Monte Carlo. The associated computational cost of simulating dynamic real-size systems, however, is even greater and poses a challenge. This project aims to investigate variance reduction methods based on mathematical, statistical, and machine learning techniques to contribute to making the Monte Carlo framework more viable for simulating neutron transport in such dynamic systems.

This project is being delivered via an PhD studentship supported by an EPSRC Industrial CASE award, and Jacobs Clean Energy. The project team are Martin Skretteberg and Eugene Shwageraus