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

TL;DR

The paper extends a previously developed techno-economic optimization framework for heat-pipe microreactors (HPMRs) to a multi-objective setting using the PEARL algorithm. It jointly minimizes the rod-integrated peaking factor $F_{\Delta h}$ and the levelized cost of electricity (LCOE) under safety and operational constraints, across three reflector-cost scenarios. Through OpenMC-based neutron transport physics, bottom-up cost estimation, and RL-driven Pareto optimization, the work identifies robust design strategies—such as minimizing the drum coating angle $x_{ca}$, maximizing the fuel height $x_{fh}$, and increasing fuel burnup—that balance safety and cost. While PEARL reveals meaningful trade-offs and design guidance, surrogate-model discrepancies relative to full-order simulations indicate ongoing needs for surrogate refinement and constraint relaxation to fully realize economic gains in HPMR design.

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 ($F_{Δh}$) 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 $F_{Δh}$. 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 remain. Further improvements are anticipated through constraint relaxation and surrogate development, constituting an ongoing area of investigation.

Figures (6)

• Figure 1: Nominal reactor design with labeled components.
• Figure 2: Correlation matrix between the input design parameters and QoIs. "1" signifies a strong positive linear correlation, and "-1" a strong negative one.
• Figure 3: Pareto fronts from PEARL agents for the scenario involving expensive reflectors. The two solutions corresponding to the minimum LCOE (i.e., Solution 1) and $F_{\Delta h}$ (i.e., Solution 2) are circled in blue.
• Figure 4: Pareto fronts from PEARL agents for the scenario with an inexpensive axial reflector. The two solutions corresponding to the minimum LCOE (i.e., Solution 1) and $F_{\Delta h}$ (i.e., Solution 2) are circled in blue.
• Figure 5: Pareto fronts from PEARL agents for the scenario with inexpensive axial and drum reflectors. The two solutions corresponding to the minimum LCOE (i.e., Solution 1) and $F_{\Delta h}$ (i.e., Solution 2) are circled in blue.