用强化学习优化燃烧器分区，提升贫燃熄火预测精度与速度
A Reinforcement-Learning-Augmented Liquid-Fueled Reactor Network Model for Predicting Lean Blowout in Gas Turbine Combustors
- 基于强化学习自动划分反应器区域，以预测精度为优化目标
- 相比k-means提升预测准确性，且计算速度显著快于高保真模型
- 适合需要快速迭代的燃气轮机燃烧设计场景
本研究提出一种强化学习（RL）框架，用于生成最优液态燃料反应器网络，以提升燃气轮机燃烧室贫燃熄火（LBO）的预测能力。现有方法依赖人工经验或输入空间中的距离度量来确定聚类边界，而本文方法为目标导向，将目标指标（如LBO预测精度）显式纳入聚类过程。该框架采用多阶段聚类-分类策略：先通过k-means等方法生成大量同质微聚类，再由演员-评论家型强化学习代理将它们合并为最优反应区。基于Jet-A机制（119种物种，841条反应）的验证表明，该框架在预测精度上优于k-means，能准确捕捉LBO趋势，同时相比高保真计算模型实现显著加速。整体而言，该强化学习驱动的方法展现出作为高效降阶建模技术的巨大潜力，可辅助高保真模拟实现快速设计空间探索。
原文摘要 · Abstract (English)
This study introduces a reinforcement learning (RL) framework for generating optimal liquid-fueled reactors to improve lean blowout (LBO) predictions in gas turbine combustors. Existing approaches for determining cluster boundaries rely on manual heuristics or distance-based metrics in the input space. In contrast, the proposed method is goal-oriented, explicitly accounting for the target metric (e.g., LBO prediction accuracy) during cluster formation. The framework employs a multi-stage clustering--classification strategy: an initial clustering step (e.g., $k$-means clustering) generates a large set of homogeneous micro-clusters, followed by an actor-critic RL agent that merges them into optimal reactor zones. The validation study, performed using a Jet-A mechanism (119 species, 841 reactions), shows the RL framework offers improved predictive fidelity compared to $k$-means and captures the correct LBO trends, while achieving substantial speedups relative to the high-fidelity computational model. Overall, the RL-driven approach demonstrates strong potential as a computationally efficient reduced-order modeling technique that can complement high-fidelity simulations for rapid design-space exploration.
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