A Reinforcement-Learning-Augmented Liquid-Fueled Reactor Network Model for Predicting Lean Blowout in Gas Turbine Combustors

The problem is that existing methods for determining reactor cluster boundaries in lean blowout (LBO) prediction rely on manual heuristics or distance-based metrics. The method proposes a reinforcement learning (RL) framework using a multi-stage clustering-classification strategy with an actor-critic agent to merge micro-clusters into optimal reactor zones. Experimental evidence using a Jet-A mechanism shows the RL framework improves predictive fidelity over k-means and captures correct LBO trends with substantial speedups relative to high-fidelity models. This matters because the RL-driven approach offers a computationally efficient reduced-order modeling technique for rapid design-space exploration in gas turbine combustors.