31 citations · 38 across the 3 of their papers we have counts for
4 papers
Generalized Joint Probability Density Function Formulation inTurbulent Combustion using DeepONet
Rishikesh Ranade, Kevin Gitushi, Tarek Echekki
Joint probability density function (PDF)-based models in turbulent combustion provide direct closure for turbulence-chemistry interactions. The joint PDFs capture the turbulent fla…
An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure
Rishikesh Ranade, Genong Li, Shaoping Li +1
Probability density function (PDF) based turbulent combustion modelling is limited by the need to store multi-dimensional PDF tables that can take up large amounts of memory. A sig…
A Framework for Data-Based Turbulent Combustion Closure: A Posteriori Validation
Rishikesh Ranade, Tarek Echekki
In this work, we demonstrate a framework for developing closure models in turbulent combustion using experimental multi-scalar measurements. The framework is based on the construct…
A Framework for Data-Based Turbulent Combustion Closure: A Priori Validation
Rishikesh Ranade, Tarek Echekki
Experimental multi-scalar measurements in laboratory flames have provided important databases for the validation of turbulent combustion closure models. In this work, we present a…