Techno-economic optimization of a heat-pipe microreactor, part II: Multi-objective optimization analysis

Abstract

Heat-pipe microreactors (HPMRs) are compact and transportable nuclear power systems exhibiting inherent safety, well-suited for deployment in remote regions where access is limited and reliance on costly fossil fuels is prevalent. In prior work, we developed a design optimization framework that incorporates techno-economic considerations through surrogate modeling and reinforcement learning (RL)-based optimization, focusing solely on minimizing the levelized cost of electricity (LCOE) by using a bottom-up cost estimation approach. In this study, we extend that framework to a multi-objective optimization that uses the Pareto Envelope Augmented with Reinforcement Learning (PEARL) algorithm. The objectives include minimizing both the rod-integrated peaking factor ( F Δ h ) and LCOE—subject to safety and operational constraints. We evaluate three cost scenarios: (1) a high-cost axial and drum reflectors, (2) a low-cost axial reflector, and (3) low-cost axial and drum reflectors. Our findings indicate that reducing the solid moderator radius, pin pitch, and drum coating angle—all while increasing the fuel height—effectively lowers F Δ h . Across all three scenarios, four key strategies consistently emerged for optimizing LCOE: (1) minimizing the axial reflector contribution when costly, (2) reducing control drum reliance, (3) substituting expensive tri-structural isotropic (TRISO) fuel with axial reflector material priced at the level of graphite, and (4) maximizing fuel burnup. While PEARL demonstrates promise in navigating trade-offs across diverse design scenarios, discrepancies between surrogate model predictions and full-order simulations can lead to infeasibility in some cases, particularly for fuel lifetime and shutdown margin. Therefore, this study should be understood as a surrogate-guided trade-off exploration study rather than an effort to identify exact Pareto-optimal solutions. Further improvements are anticipated through constraint relaxation and surrogate development, constituting an ongoing area of investigation.

The authors' abstract, as published at the source. Nuclear Engineering and Design, 2026 · DOI ↗