69 citations · 162 across the 24 of their papers we have counts for
19 papers · 1 filter
A physics-constrained machine-learning sub-grid-scale modeling approach for turbulent premixed flames
Seung Won Suh, Jonathan F. MacArt, Luke N. Olson +1
A physics-embedded training framework is used to close the sub-grid-scale dynamics of turbulent premixed flames. The trained model augments the resolved flow equations and is train…
Solver-in-the-loop training of deep learning closures for large-eddy simulation of turbulent premixed jet flames
Priyesh Kakka, Jonathan F. MacArt
Large-eddy simulation (LES) turbulence models often fail to capture the effects of chemical heat release and the resulting modulation of turbulence in premixed flames, underscoring…
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…
Dynamic mixed turbulence modeling using a super-resolution generative adversarial approach
Ludovico Nista, Christoph D. K. Schumann, Temistocle Grenga +3
A dynamic mixed super-resolution model (DMSRM) for large-eddy simulations (LESs) is proposed, which combines the traditional dynamic mixed model (DMM) formulation with the generati…
Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning
Xuemin Liu, Tom Hickling, Jonathan F. MacArt
We train active neural-network flow controllers using a deep learning PDE augmentation method to optimize lift-to-drag ratios in turbulent airfoil flows at Reynolds number $5\times…
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…
Neural network-augmented eddy viscosity closures for turbulent premixed jet flames
Priyesh Kakka, Jonathan F. MacArt
Extending gradient-type turbulence closures to turbulent premixed flames is challenging due to the significant influence of combustion heat release. We incorporate a deep neural ne…
ZipGAN: Super-Resolution-based Generative Adversarial Network Framework for Data Compression of Direct Numerical Simulations
Ludovico Nista, Christoph D. K. Schumann, Fabian Fröde +5
The advancement of high-performance computing has enabled the generation of large direct numerical simulation (DNS) datasets of turbulent flows, driving the need for efficient comp…
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…
Optimization of Second-Order Transport Models for Transition-Continuum Flows
Mikolaj Kryger, Jonathan F. MacArt
Modeling transition-continuum hypersonic flows poses significant challenges due to thermodynamic nonequilibrium and the associated breakdown of the continuum assumption. Standard c…
Understanding Latent Timescales in Neural Ordinary Differential Equation Models for Advection-Dominated Dynamical Systems
Ashish S. Nair, Shivam Barwey, Pinaki Pal +3
The neural ordinary differential equation (ODE) framework has emerged as a powerful tool for developing accelerated surrogate models of complex physical systems governed by partial…
Influence of adversarial training on super-resolution turbulence reconstruction
Ludovico Nista, Christoph David Karl Schumann, Mathis Bode +4
Supervised super-resolution deep convolutional neural networks (CNNs) have gained significant attention for their potential in reconstructing velocity and scalar fields in turbulen…
Adjoint-based machine learning for active flow control
Xuemin Liu, Jonathan F. MacArt
We develop neural-network active flow controllers using a deep learning PDE augmentation method (DPM). The sensitivities for optimization are computed using adjoints of the governi…
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…