3 citations · 3 across the 5 of their papers we have counts for
5 papers
UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration
Georgios Georgalis, Alejandro Becerra, Kenneth Budzinski +4
The goal of this paper is to demonstrate and address challenges related to all aspects of performing a complete uncertainty quantification analysis of a complicated physics-based s…
Physics Informed Machine Learning for Chemistry Tabulation
Amol Salunkhe, Dwyer Deighan, Paul Desjardin +1
Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Inste…
Combined Data and Deep Learning Model Uncertainties: An Application to the Measurement of Solid Fuel Regression Rate
Georgios Georgalis, Kolos Retfalvi, Paul E. DesJardin +1
In complex physical process characterization, such as the measurement of the regression rate for solid hybrid rocket fuels, where both the observation data and the model used have…
ChemTab: A Physics Guided Chemistry Modeling Framework
Amol Salunkhe, Dwyer Deighan, Paul DesJardin +1
Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Inste…
Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning
Gabriel Surina, Georgios Georgalis, Siddhant S. Aphale +2
This study presents an imaging-based deep learning tool to measure the fuel regression rate in a 2D slab burner experiment for hybrid rocket fuels. The slab burner experiment is de…