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 cluster boundaries in reactor network models rely on manual heuristics or distance-based metrics, which are not optimized for target metrics like lean blowout (LBO) prediction accuracy. The method introduces a reinforcement learning (RL) framework that uses a multi-stage clustering-classification strategy, where an actor-critic RL agent merges initial micro-clusters into optimal reactor zones in a goal-oriented manner. Experimental evidence from a validation study using a Jet-A mechanism (119 species, 841 reactions) shows that the RL framework improves predictive fidelity over k-means clustering and captures correct LBO trends while achieving substantial speedups relative to high-fidelity computational models. This matters because the RL-driven approach provides a computationally efficient reduced-order modeling technique that can complement high-fidelity simulations for rapid design-space exploration in gas turbine combustors.