3 citations · 4 across the 11 of their papers we have counts for
12 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…
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…
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…
A Competitive Learning Approach for Specialized Models: A Solution for Complex Physical Systems with Distinct Functional Regimes
Okezzi F. Ukorigho, Opeoluwa Owoyele
Complex systems in science and engineering sometimes exhibit behavior that changes across different regimes. Traditional global models struggle to capture the full range of this co…
DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies
Eloghosa Ikponmwoba, Ope Owoyele
We present an approach for designing swarm-based optimizers for the global optimization of expensive black-box functions. In the proposed approach, the problem of finding efficient…
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design
Opeoluwa Owoyele, Pinaki Pal
A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the pre…
ChemNODE: A Neural Ordinary Differential Equations Approach for Chemical Kinetics Solvers
Opeoluwa Owoyele, Pinaki Pal
Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach. T…
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization
Opeoluwa Owoyele, Pinaki Pal, Alvaro Vidal Torreira +4
In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computa…
Efficient bifurcation and parameterization of multi-dimensional combustion manifolds using deep mixture of experts: an a priori study
Opeoluwa Owoyele, Prithwish Kundu, Pinaki Pal
This work describes and validates an approach for autonomously bifurcating turbulent combustion manifolds to divide regression tasks amongst specialized artificial neural networks…
The Stabilized Explicit Variable-Load Solver with Machine Learning Acceleration for the Rapid Solution of Stiff Chemical Kinetics
Kyle Buchheit, Opeoluwa Owoyele, Terry Jordan +1
In this study, a fast and stable machine-learned hybrid algorithm implemented in TensorFlow for the integration of stiff chemical kinetics is introduced. Numerical solutions to dif…