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, which are not goal-oriented. The method introduces a reinforcement learning (RL) framework with a multi-stage clustering–classification strategy, using 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, captures correct LBO trends, and achieves 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.