一种用于预测燃气轮机燃烧室贫油熄火的强化学习增强型液体燃料反应器网络模型

中文摘要 AI 辅助

本研究引入了一种强化学习（RL）框架，用于生成优化的液体燃料反应器，以改进燃气轮机燃烧室贫油熄火（LBO）预测。现有确定簇边界的方法依赖于手动启发式或输入空间中基于距离的度量。相比之下，所提方法是目标导向的，在簇形成过程中明确考虑目标度量（如LBO预测精度）。该框架采用多阶段聚类-分类策略：初始聚类步骤（如k均值聚类）生成大量同质微簇，随后由一个演员-评论家RL智能体将它们合并为优化的反应器区域。使用Jet-A机制（119种物质，841个反应）进行的验证研究表明，与k均值相比，RL框架具有更高的预测保真度，能捕捉到正确的LBO趋势，且相对于高保真计算模型实现了大幅加速。总体而言，RL驱动方法作为一种计算高效的降阶建模技术，具有强大潜力，可补充高保真模拟用于快速设计空间探索。

英文摘要

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.

发表机构

• Louisiana State University（路易斯安那州立大学）
• Argonne National Laboratory（阿贡国家实验室）

机构由 AI 辅助整理，请以论文原文为准。