68 citations · 90 across the 6 of their papers we have counts for
6 papers
Comparison of turbulence statistics in isothermal and non-isothermal large eddy simulations of supercritical carbon dioxide jets
Julia Ream, Marc T. Henry de Frahan, Shashank Yellapantula +3
Supercritical carbon dioxide is of interest in a wide range of engineering problems, including carbon capture, utilization, and storage as well as advanced cycles for power generat…
A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
Graham Pash, Malik Hassanaly, Shashank Yellapantula
While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical s…
Field measurements reveal insights into the impact of turbulent wind on loads experienced by parabolic trough solar collectors
Ulrike Egerer, Scott Dana, David Jager +3
To ensure efficient and reliable operation of a concentrating solar-thermal power (CSP) plant, its solar collector field needs to accurately focus sunlight. The optical efficiency…
Variational Auto-Encoder Based Deep Learning Technique For Filling Gaps in Reacting PIV Data
Shashank Yellapantula
In this study, a deep learning based conditional density estimation technique known as conditional variational auto-encoder (CVAE) is used to fill gaps typically observed in partic…
Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows
Malik Hassanaly, Bruce A. Perry, Michael E. Mueller +1
Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that is routinely generated. In applications that are constrained b…
Deep learning for presumed probability density function models
Marc T. Henry de Frahan, Shashank Yellapantula, Ryan King +2
In this work, we use ML techniques to develop presumed PDF models for large eddy simulations of reacting flows. The joint sub-filter PDF of mixture fraction and progress variable i…