69 citations · 116 across the 9 of their papers we have counts for
9 papers
Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows
Daniel Dehtyriov, Jonathan F. MacArt, Justin Sirignano
Turbulence is ubiquitous in engineering and science, yet direct simulation is prohibitively expensive. The Reynolds-averaged Navier-Stokes (RANS) equations provide savings exceedin…
oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation
Daniel Dehtyriov, Jonathan F. MacArt, Justin Sirignano
Deep learning (DL) has demonstrated promise for accelerating and enhancing the accuracy of flow physics simulations, but progress is constrained by the scarcity of high-fidelity tr…
Physics-Based Machine Learning Closures and Wall Models for Hypersonic Transition-Continuum Boundary Layer Predictions
Ashish S. Nair, Narendra Singh, Marco Panesi +2
Modeling rarefied hypersonic flows remains a fundamental challenge due to the breakdown of classical continuum assumptions in the transition-continuum regime, where the Knudsen num…
OGF: An Online Gradient Flow Method for Optimizing the Statistical Steady-State Time Averages of Unsteady Turbulent Flows
Tom Hickling, Jonathan F. MacArt, Justin Sirignano +1
Turbulent flows are chaotic and unsteady, but their statistical distribution converges to a statistical steady state. Engineering quantities of interest typically take the form of…
Online Optimisation of Machine Learning Collision Models to Accelerate Direct Molecular Simulation of Rarefied Gas Flows
Nicholas Daultry Ball, Jonathan F. MacArt, Justin Sirignano
We develop an online optimisation algorithm for in situ calibration of collision models in simulations of rarefied gas flows. The online optimised collision models are able to achi…
Dynamic Deep Learning LES Closures: Online Optimization With Embedded DNS
Justin Sirignano, Jonathan F. MacArt
Deep learning (DL) has recently emerged as a candidate for closure modeling of large-eddy simulation (LES) of turbulent flows. High-fidelity training data is typically limited: it…
Deep Learning Closure of the Navier-Stokes Equations for Transition-Continuum Flows
Ashish S. Nair, Justin Sirignano, Marco Panesi +1
The predictive accuracy of the Navier-Stokes equations is known to degrade at the limits of the continuum assumption, thereby necessitating expensive and often highly approximate s…
Deep Learning Closure Models for Large-Eddy Simulation of Flows around Bluff Bodies
Justin Sirignano, Jonathan F. MacArt
A deep learning (DL) closure model for large-eddy simulation (LES) is developed and evaluated for incompressible flows around a rectangular cylinder at moderate Reynolds numbers. N…
Embedded training of neural-network sub-grid-scale turbulence models
Jonathan F. MacArt, Justin Sirignano, Jonathan B. Freund
The weights of a deep neural network model are optimized in conjunction with the governing flow equations to provide a model for sub-grid-scale stresses in a temporally developing…