activity
20182021
most citedUsing Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations

20 citations · 24 across the 2 of their papers we have counts for

collaborators

5 papers

math.NA20214 cited

On the numerical accuracy in finite-volume methods to accurately capture turbulence in compressible flows

Emmanuel Motheau, John Wakefield

The goal of the present paper is to understand the impact of numerical schemes for the reconstruction of data at cell faces in finite-volume methods, and to assess their interactio…

physics.comp-ph202020 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.comp-ph2019

Capturing shocks and turbulence spectra in compressible flows. Part 2: A new hybrid PPM/WENO method

Emmanuel Motheau, John Wakefield

In the Part 1 of the present paper the performance of several different low and high-order finite-volume methods were assessed by investigating how well they can capture the turbul…

physics.comp-ph2019

Investigation of finite-volume methods to capture shocks and turbulence spectra in compressible flows

Emmanuel Motheau, John Wakefield

The aim of the present paper is to provide a comparison between several finite-volume methods of different numerical accuracy: second-order Godunov method with PPM interpolation an…

physics.flu-dyn2018

A Fourth-Order Adaptive Mesh Refinement Algorithm for the Multicomponent, Reacting Compressible Navier-Stokes Equations

Matthew Emmett, Emmanuel Motheau, Weiqun Zhang +2

In this paper we present a fourth-order in space and time block-structured adaptive mesh refinement algorithm for the compressible multicomponent reacting Navier-Stokes equations.…