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

TL;DR

This paper extends a design optimization framework for heat-pipe microreactors to a multi-objective setting, balancing safety, cost, and operational efficiency using advanced algorithms, and evaluates different cost scenarios to identify key design strategies.

Contribution

It introduces a multi-objective optimization approach using the PEARL algorithm for heat-pipe microreactors, considering safety and economic trade-offs across various cost scenarios.

Findings

Reducing moderator radius, pin pitch, and coating angle lowers peaking factor.

Key strategies for lowering LCOE include minimizing reflector costs and maximizing fuel burnup.

PEARL effectively explores trade-offs but needs improved surrogate models.

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