通过强化学习优化微反应堆设计，成本降低超57%。
Techno-economic optimization of a heat-pipe microreactor, part I: theory and cost optimization
- 用代理模型与强化学习联合优化几何结构。
- 在约束条件下使度电成本降低超57%。
- 适合关注小型核反应堆经济性的研究者。
微反应堆，特别是热管微反应堆（HPMRs），是紧凑、可移动、自调节的电力系统，适用于难以获取能源的偏远地区，目前仍以昂贵的化石燃料为主。然而，其存在规模不经济问题，财务可行性不足。为解决此问题，本文提出一种融合技术与经济分析的统一几何设计优化方法。首先通过随机采样训练高斯过程（GPs）和多层感知机（MLPs）代理模型，再将其嵌入基于强化学习（RL）的优化框架中，以最小化度电成本（LCOE），同时满足燃料寿命、停机裕度（SDM）、峰值热流密度及棒集成峰因子等约束。研究了两种情形：轴向反射层成本极高与极低的情况。结果表明，运行维护成本与资本支出是影响总成本的主要因素，前者主要受轴向反射层成本影响，后者则由控制鼓材料决定。优化器巧妙调整设计参数，在满足约束前提下显著降低成本，两种情况下均实现超过57%的降幅。当前正推进燃料与热管性能的多目标协同优化，以深入理解约束与成本表现之间的交互关系。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。