Enhancing Nuclear Reactor Core Simulation through Data-Based Surrogate Models
This work addresses the need for faster simulations in nuclear reactor control to enhance flexibility in power plants, but it appears incremental as it builds on existing Model Predictive Control methods.
The paper tackled the problem of improving nuclear reactor core simulation for flexible nuclear power plant operation by introducing data-based surrogate models, achieving up to 1000x reduction in computational time.
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).