Abstract Optimization of nuclear fuel assemblies if performed effectively, will lead to fuel efficiency improvement, cost reduction, and safety assurance. However, assembly optimization involves solving high-dimensional and computationally expensive combinatorial problems. As such, fuel designers’ expert judgement has commonly prevailed over the use of stochastic optimization (SO) algorithms such as genetic algorithms and simulated annealing. To improve the state-of-art, we explore a class of artificial intelligence (AI) algorithms, namely, reinforcement learning (RL) in this work. We propose a physics-informed AI optimization methodology by establishing a connection through reward shaping between RL and the tactics fuel designers follow in practice by moving fuel rods in the assembly to meet specific constraints and objectives. The methodology utilizes RL algorithms, deep Q learning and proximal policy optimization, and compares their performance to SO algorithms. The methodology is applied on two boiling water reactor assemblies of low-dimensional ( ∼ 2 × 10 6 combinations) and high-dimensional ( ∼ 10 31 combinations) natures. The results demonstrate that RL is more effective than SO in solving high dimensional problems, i.e., 10 × 10 assembly, through embedding expert knowledge in form of game rules and effectively exploring the search space. For a given computational resources and timeframe relevant to fuel designers, RL algorithms outperformed SO through finding more feasible patterns, 4–5 times more than SO, and through increasing search speed, as indicated by the RL outstanding computational efficiency. The results of this work clearly demonstrate RL effectiveness as another decision support tool for nuclear fuel assembly optimization.

中文翻译：

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核组装设计的物理信息强化学习优化

摘要 核燃料组件的优化如果有效执行，将导致燃料效率提高、成本降低和安全保证。然而，装配优化涉及解决高维且计算成本高的组合问题。因此，燃料设计者的专家判断通常胜过使用随机优化 (SO) 算法，例如遗传算法和模拟退火。为了改进最先进的技术，我们在这项工作中探索了一类人工智能 (AI) 算法，即强化学习 (RL)。我们提出了一种基于物理信息的 AI 优化方法，通过在 RL 和燃料设计师在实践中通过移动燃料棒以满足特定约束和目标而在实践中遵循的战术之间的奖励塑造建立联系。该方法利用 RL 算法、深度 Q 学习和近端策略优化，并将其性能与 SO 算法进行比较。该方法应用于两个低维（~ 2 × 10 6 组合）和高维（~ 10 31 组合）性质的沸水反应堆组件。结果表明，通过以游戏规则的形式嵌入专家知识并有效探索搜索空间，RL 在解决高维问题（即 10 × 10 装配）方面比 SO 更有效。对于与燃料设计者相关的给定计算资源和时间范围，RL 算法通过找到更可行的模式（比 SO 多 4-5 倍）以及通过提高搜索速度来超越 SO，正如 RL 出色的计算效率所表明的那样。