5 papers
A Reinforcement-Learning-Augmented Liquid-Fueled Reactor Network Model for Predicting Lean Blowout in Gas Turbine Combustors
Philip John, Eloghosa Ikponmwoba, Pinaki Pal +1
This study introduces a reinforcement learning (RL) framework for generating optimal liquid-fueled reactors to improve lean blowout (LBO) predictions in gas turbine combustors. Exi…
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 evalua…
A Liquid-Fueled Reactor Network Model for Enhanced NOx Prediction in Gas Turbine Combustors
Philip John, Haresh Chandrasekhar, Sourav Saha +1
This study introduces a liquid-fueled reactor network (LFRN) framework for reduced-order modeling of gas turbine combustors. The proposed LFRN extends conventional gaseous-fueled r…
Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning
Eloghosa Ikponmwoba, Opeoluwa Owoyele
The computational cost of stiff chemical kinetics remains a dominant bottleneck in reacting-flow simulation, yet hybrid integration strategies are typically driven by hand-tuned he…
Quantized Skeletal Learning (QSL): A Differentiable Programming Approach for Skeletal Reduction of Chemical Mechanisms
Opeoluwa Owoyele
This paper presents a data-driven approach, referred to as Quantized Skeletal Learning (QSL), for generating skeletal mechanisms. The approach has two key components: (1) a weight…