activity
20032023
most citedCASTRO: A New Compressible Astrophysical Solver. I. Hydrodynamics and Self-Gravity

288 citations · 858 across the 18 of their papers we have counts for

collaborators

18 papers

physics.flu-dyn2023★ 24 cited

Direct numerical simulation of a high-pressure hydrogen micromix combustor: flame structure and stabilisation mechanism

Thomas L. Howarth, Mark A. Picciani, Edward S. Richardson +2

A high-pressure hydrogen micromix combustor has been investigated using direct numerical simulation with detailed chemistry to examine the flame structure and stabilisation mechani…

physics.flu-dyn2021★ 24 cited

A Moving Embedded Boundary Approach For The Compressible Navier-Stokes Equations In A Block-Structured Adaptive Refinement Framework

Mahesh Natarajan, Ray Grout, Weiqun Zhang +1

A computational technique has been developed to perform compressible flow simulations involving moving boundaries using an embedded boundary approach within the block-structured ad…

physics.comp-ph2020★ 20 cited

Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations

Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath +3

Simulation of turbulent flows at high Reynolds number is a computationally challenging task relevant to a large number of engineering and scientific applications in diverse fields…

physics.flu-dyn2019★ 7 cited

A spectral deferred correction strategy for low Mach number reacting flows subject to electric fields

Lucas Esclapez, Valentina Ricchiuti, John B. Bell +1

We propose an algorithm for low Mach number reacting flows subjected to electric field that includes the chemical production and transport of charged species. This work is an exten…

physics.flu-dyn2019★ 5 cited

High-Resolution Numerical Simulations of a Large-Scale Helium Plume Using Adaptive Mesh Refinement

Nicholas T. Wimer, Marcus S. Day, Caelan Lapointe +5

The physical characteristics and evolution of a large-scale helium plume are examined through a series of numerical simulations with increasing physical resolution using adaptive m…

physics.comp-ph2019★ 68 cited

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…