Enhancing Nuclear Reactor Core Simulation through Data-Based Surrogate Models

TL;DR

This paper introduces data-driven surrogate models that significantly accelerate nuclear reactor core simulations, enabling faster and more flexible control strategies for nuclear power plants.

Contribution

It presents novel data-driven and physics-informed surrogate models that drastically reduce simulation time for nuclear reactor core dynamics.

Findings

Up to 1000x reduction in simulation time

Effective integration of complex nonlinear dynamics

Enhanced control capabilities for nuclear reactors

Abstract

In recent years, there has been an increasing need for Nuclear Power Plants (NPPs) to improve flexibility in order to match the rapid growth of renewable energies. The Operator Assistance Predictive System (OAPS) developed by Framatome addresses this problem through Model Predictive Control (MPC). In this work, we aim to improve MPC methods through data-driven simulation schemes. Thus, from a set of nonlinear stiff ordinary differential equations (ODEs), this paper introduces two surrogate models acting as alternative simulation schemes to enhance nuclear reactor core simulation. We show that both data-driven and physics-informed models can rapidly integrate complex dynamics, with a very low computational time (up to 1000x time reduction).