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Techno-economic optimization of a heat-pipe microreactor, part I: theory and cost optimization

Paul Seurin, Dean Price, Luis Nunez

TL;DR

This paper tackles the economic viability of heat-pipe microreactors (HPMRs) by introducing a unifying framework that blends surrogate models with reinforcement learning to minimize the FOAK LCOE under safety and performance constraints. Using OpenMC for neutronics and MOUSE COA for techno-economic analysis, the authors explore a seven-parameter design space around a nominal HPMR and demonstrate that O&M and capital costs—especially axial reflector and control-drum materials—are dominant cost drivers. Two cost-sensitivity cases (Be reflector expensive vs graphite-like cheaper) show substantial LCOE reductions (≈57% and ≈78%, respectively) through design adjustments such as altering the axial reflector thickness and moderator size. Surrogate models (Gaussian processes and MLPs) achieve strong predictive accuracy, enabling efficient RL-guided optimization, with a roadmap for integrating full fuel and HP performance in a companion multi-objective study. This work provides a first-of-its-kind methodology to inform early-stage, economically informed HPMR design iterations and highlights key paths to improve competitiveness in remote-energy applications.

Abstract

Microreactors, particularly heat-pipe microreactors (HPMRs), are compact, transportable, self-regulated power systems well-suited for access-challenged remote areas where costly fossil fuels dominate. However, they suffer from diseconomies of scale, and their financial viability remains unconvincing. One step in addressing this shortcoming is to design these reactors with comprehensive economic and physics analyses informing early-stage design iteration. In this work, we present a novel unifying geometric design optimization approach that accounts for techno-economic considerations. We start by generating random samples to train surrogate models, including Gaussian processes (GPs) and multi-layer perceptrons (MLPs). We then deploy these surrogates within a reinforcement learning (RL)-based optimization framework to optimize the levelized cost of electricity (LCOE), all the while imposing constraints on the fuel lifetime, shutdown margin (SDM), peak heat flux, and rod-integrated peaking factor. We study two cases: one in which the axial reflector cost is very high, and one in which it is inexpensive. We found that the operation and maintenance and capital costs are the primary contributors to the overall LCOE particularly the cost of the axial reflectors (for the first case) and the control drum materials. The optimizer cleverly changes the design parameters so as to minimize one of them while still satisfying the constraints, ultimately reducing the LCOE by more than 57% in both instances. A comprehensive integration of fuel and HP performance with multi-objective optimization is currently being pursued to fully understand the interaction between constraints and cost performance.

Techno-economic optimization of a heat-pipe microreactor, part I: theory and cost optimization

TL;DR

This paper tackles the economic viability of heat-pipe microreactors (HPMRs) by introducing a unifying framework that blends surrogate models with reinforcement learning to minimize the FOAK LCOE under safety and performance constraints. Using OpenMC for neutronics and MOUSE COA for techno-economic analysis, the authors explore a seven-parameter design space around a nominal HPMR and demonstrate that O&M and capital costs—especially axial reflector and control-drum materials—are dominant cost drivers. Two cost-sensitivity cases (Be reflector expensive vs graphite-like cheaper) show substantial LCOE reductions (≈57% and ≈78%, respectively) through design adjustments such as altering the axial reflector thickness and moderator size. Surrogate models (Gaussian processes and MLPs) achieve strong predictive accuracy, enabling efficient RL-guided optimization, with a roadmap for integrating full fuel and HP performance in a companion multi-objective study. This work provides a first-of-its-kind methodology to inform early-stage, economically informed HPMR design iterations and highlights key paths to improve competitiveness in remote-energy applications.

Abstract

Microreactors, particularly heat-pipe microreactors (HPMRs), are compact, transportable, self-regulated power systems well-suited for access-challenged remote areas where costly fossil fuels dominate. However, they suffer from diseconomies of scale, and their financial viability remains unconvincing. One step in addressing this shortcoming is to design these reactors with comprehensive economic and physics analyses informing early-stage design iteration. In this work, we present a novel unifying geometric design optimization approach that accounts for techno-economic considerations. We start by generating random samples to train surrogate models, including Gaussian processes (GPs) and multi-layer perceptrons (MLPs). We then deploy these surrogates within a reinforcement learning (RL)-based optimization framework to optimize the levelized cost of electricity (LCOE), all the while imposing constraints on the fuel lifetime, shutdown margin (SDM), peak heat flux, and rod-integrated peaking factor. We study two cases: one in which the axial reflector cost is very high, and one in which it is inexpensive. We found that the operation and maintenance and capital costs are the primary contributors to the overall LCOE particularly the cost of the axial reflectors (for the first case) and the control drum materials. The optimizer cleverly changes the design parameters so as to minimize one of them while still satisfying the constraints, ultimately reducing the LCOE by more than 57% in both instances. A comprehensive integration of fuel and HP performance with multi-objective optimization is currently being pursued to fully understand the interaction between constraints and cost performance.

Paper Structure

This paper contains 16 sections, 5 equations, 5 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Nominal Reactor Design
  4. Physics and Techno-economic Modeling
  5. Physics Modeling
  6. Techno-economic Modeling
  7. Design Parameterization and Bounds
  8. Optimization Methodology
  9. RL for Optimization and Objective Function Formulation
  10. Results
  11. Samples Description
  12. Surrogate Model Performance
  13. Application of the Optimization Methodology, and Analysis of the Optimized Designs
  14. Deep Dive into the LCOE
  15. Conclusion
  16. ...and 1 more sections

Figures (5)

  • Figure 1: Nominal reactor design, with labeled components (subject to change during the optimization process).
  • Figure 2: Performance results for the GPs on the first three QoIs: (A) lifetime, (B) SDM, (C) $F_{\Delta h}$, and (F) MLP for $q^{"}_{max}$.
  • Figure 3: Evolution of the mean and cumulative reward across epochs (1 epoch corresponds to 10,000 samples) for (a) the case of the nominal Be's cost and (B) the replaced-by-graphite cost.
  • Figure 4: Breakdown of the LCOE per component: fuel cycle cost ((A) and (D)), capital cost ((B) and (E)), and O&M cost ((C) and (F)).
  • Figure 5: Correlation matrix between the input design parameters and the QoIs. "1" signifies a strong positive linear correlation and "-1" a strong negative one.