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Opeoluwa Owoyele

2 papers indexed

arxivcs.LG2026-07-21

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

Philip John, Eloghosa Ikponmwoba, Pinaki Pal, Opeoluwa Owoyele

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 metr…

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arxivphysics.flu-dyncs.LG2026-07-10

Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics

Okezzi Ukorigho, Opeoluwa Owoyele

We present a physics-constrained machine learning framework for accelerating the direct numerical simulation (DNS) of turbulent reacting flows. The model replaces the direct evaluation of detailed chemical source terms with a surrogate that predicts reaction rates from a reduced…

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