Nuclear microreactor controller offers autonomous load following
Rooted in physics, not AI, the new control algorithm autonomously adjusts reactor thermal power in high-fidelity simulations with 0.234% error while adhering to safety constraints.
Experts
Brendan Kochunas
A new physics-based algorithm clears a path towards nuclear microreactors that can autonomously adjust power output based on need, according to a University of Michigan-led study published in Progress in Nuclear Energy and funded by the U.S. Department of Energy Office of Nuclear Energy.
Easily transportable and able to generate up to 20 megawatts of thermal energy for heat or electricity, nuclear microreactors could be useful in remote locations such as rural communities, disaster zones, military bases or even cargo ships, in addition to other applications.
If integrated into an electrical grid, nuclear microreactors could provide stable, carbon-free energy, but they must be able to adjust power output to match shifting demand—a capability known as load following. In large reactors, staff make these adjustments manually, which would be cost-prohibitive in remote areas, imposing a barrier to adoption.
“Many startup and legacy companies in the U.S. are pushing towards near-term and broad deployment of nuclear microreactors, and our work establishes a clear avenue to achieve that in an economically viable way,” said Brendan Kochunas, an associate professor of nuclear engineering and radiological sciences at U-M and corresponding author of the study.
“Our method can help vendors design reactors with autonomous control systems that are safer and more secure.”
This study focused on High-Temperature Gas-Cooled Reactors (HTGR), advanced nuclear reactors that can scale from micro- to large-scale. While based on the Holos-Quad (Gen 2+) model, a HTGR-type microreactor design, the researchers outline a simplified microreactor model that preserves key parameters like power density, inlet coolant temperature, core pressure and flow velocity.
The research team leveraged model predictive control (MPC), a method that predicts future behavior to optimize control over a defined period of time under certain constraints. Specifically, they developed an MPC controller that optimized the rotation of control drums that surround the microreactor’s central core that decrease power when facing inwards and increase power when facing outwards.
To ensure the model was based in reality and accurately representing the microreactor’s operation, the researchers integrated PROTEUS, a simulation toolset for high-fidelity reactor physics analysis.
When tasked with ramping the power up or down at 20% per minute, their control algorithm stayed within 0.234% of the target. It does all of this without AI, meaning everything about the automated control for load follow operation is grounded in physics and mathematics and readily explainable—an essential feature for passing regulatory review.
Extensive sensitivity tests confirmed their MPC controller works for a wide range of model inputs, validating feasibility for autonomous control.
“The control algorithm’s success and integration with high-fidelity simulation tools demonstrates that we can now design nuclear reactors and their instrumentation and control systems together from the ground up, rather than trying to back fit the I&C (instrumentation and control) systems to a mostly complete reactor design,” said Kochunas.
This research was funded by the U.S. Department of Energy Office of Nuclear Energy’s Nuclear Energy University Program, United States (DE-NE0008887).