Forthcoming
M. Kryger, J. F. MacArt, “Optimization of Second-Order Transport Models for Transition-Continuum Flows,” https://arxiv.org/abs/2411.13515
J. Sirignano, J. F. MacArt, “Dynamic Deep Learning LES Closures: Online Optimization With Embedded DNS,” https://arxiv.org/abs/2303.02338
S. W. Suh, J. F. MacArt, L. N. Olson, J. B. Freund, “A TVD neural network closure and application to turbulent combustion,” https://arxiv.org/abs/2408.03413
Journal Articles (Refereed)
2024
L. Nista, C. D. K. Schumann, M. Bode, T. Grenga, J. F. MacArt, A. Attili, H. Pitsch, “Influence of adversarial training on super-resolution turbulence reconstruction” Physical Review Fluids 9 (2024), 064601
X. Liu, J. F. MacArt, “Adjoint-based machine learning for active flow control,” Physical Review Fluids 9 (2024), 013901
2023
A. Nair, J. Sirignano, M. Panesi, J. F. MacArt, “Deep Learning Closure of the Navier–Stokes Equations for Transition-Continuum Flows,” AIAA Journal 61 (2023), p. 5484–5497
J. Sirignano, J. F. MacArt, “Deep learning closure models for large-eddy simulation of flows around bluff bodies,” Journal of Fluid Mechanics 966 (2023), A26
J. Sirignano, J. F. MacArt, K. Spiliopoulos, “PDE-constrained models with neural network terms: Optimization and global convergence,” Journal of Computational Physics 481 (2023), 112016
2021
J. F. MacArt, J. Sirignano, J. B. Freund, “Embedded training of neural-network subgrid-scale turbulence models,” Physical Review Fluids 6 (2021), 050502 (invited article)
J. M. Wang, J. F. MacArt, J. B. Freund, “Flow dynamics of laser-induced breakdown at a fuel–oxidizer interface and its effect on ignition,” Combustion and Flame 229 (2021), 111375
J. F. MacArt, M. E. Mueller, “Damköhler number scaling of active cascade effects in turbulent premixed combustion,” Physics of Fluids 33 (2021), 035103 (invited article; Editor’s Choice)
2020
J. F. MacArt, J. M. Wang, P. P. Popov, J. B. Freund, “Detailed simulation of ignition, flame acceleration, and instability transition following a laser-induced breakdown,” Proceedings of the Combustion Institute 38 (2020), p. 2341–2349
J. Sirignano, J. F. MacArt, J. B. Freund, “DPM: A deep learning PDE augmentation method with application to large–eddy simulation,” Journal of Computational Physics 423 (2020), 109811
J. Lee, J. F. MacArt, M. E. Mueller, “Heat release effects on the Reynolds stress budgets in turbulent premixed flames,” Combustion and Flame 216 (2020), p. 1–8
2019
J. F. MacArt, T. Grenga, M. E. Mueller, “Evolution of flame-conditioned velocity statistics in turbulent premixed jet flames at low and high Karlovitz numbers,” Proceedings of the Combustion Institute 37 (2019), p. 2503–2510
2018
J. F. MacArt, T. Grenga, M. E. Mueller, “Effects of combustion heat release on velocity and scalar statistics in turbulent premixed jet flames at low and high Karlovitz numbers,” Combustion and Flame 191 (2018), p. 468–485
T. Grenga, J. F. MacArt, M. E. Mueller, “Dynamic Mode Decomposition of a Direct Numerical Simulation of a Turbulent Premixed Planar Jet Flame: Convergence of the Modes,” Combustion Theory and Modelling 22 (2018), p. 795–811
2016
J. F. MacArt, M. E. Mueller, “Semi-implicit iterative methods for low Mach number turbulent reacting flows: Operator splitting versus approximate factorization,” Journal of Computational Physics 326 (2016), p. 569–595
Conference Papers
A. Nair, D. Waidmann, J. Sirignano, N. Singh, M. Panesi, J. F. MacArt, “Adjoint-Trained Deep-Learning Closures of the Navier–Stokes Equations for 2D Nonequilibrium Flows,” AIAA SciTech Forum (2024)
E. Monti, N. Singh, J. Sirignano, J. F. MacArt, M. Panesi, G. Gori, “Physics-constrained deep learning- based model for non-equilibrium flows,” AIAA SciTech Forum (2024)
T. Hickling, J. Sirignano, J. F. MacArt, “Large Eddy Simulation of Airfoil Flows Using Adjoint-Trained Deep Learning Closure Models,” AIAA SciTech Forum (2024)
N. Daultry Ball, M. Panesi, J. F. MacArt, J. Sirignano, “Adjoint Optimization of the BGK Equation with an Embedded Neural Network for Reduced-Order Modeling of Hypersonic Flows,” AIAA SciTech Forum (2024)
A. Nair, J. Sirignano, M. Panesi, J. F. MacArt, “Entropy-stable Deep Learning for Navier–Stokes Predictions of Transitional-regime Flows,” AIAA SciTech Forum (2023)
J. F. MacArt, J. Sirignano, M. Panesi, “Deep Learning Closure of the Navier–Stokes Equations for Transitional Flows,” AIAA SciTech Forum (2022)
P. P. Popov, M. Nishihara, A. Munafò, J. F. MacArt, G. S. Elliott, J. B. Freund, “Laser-Induced Breakdown Ignition of Low-Pressure Hydrogen-Air Premixtures,” AIAA SciTech Forum (2020)
A. C. Nunno, B. A. Perry, J. F. MacArt, M. E. Mueller, “Data-Driven Dimension Reduction in Turbulent Combustion: Utility and Limitations,” AIAA SciTech Forum (2019)
Research Briefs
J. F. MacArt, M. E. Mueller, Scaling and modeling of heat-release effects on subfilter turbulence in premixed combustion, Center for Turbulence Research Proceedings of the Summer Program (2018), p. 299–308