Matthew Barone

Sandia National Laboratories — United States

Engineering · Physical Sciences

27h-index2.7kcitations180works0.62yr avg
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Research Topics

Fluid Dynamics and Turbulent Flows(43), Wind and Air Flow Studies(41), Model Reduction and Neural Networks(37), Wind Energy Research and Development(36), Aerodynamics and Acoustics in Jet Flows(28)

Publications178 total

Non-Intrusive Reduced Order Modeling of Aerothermal Field Data Including Ablation Shape Change
2026
Independent Evaluation and Verification of Data-Driven RANS Closures in Turbulence Modeling
2025Open Access
SciML Credibility for Turbulence Closures
2025Open Access
Wall Pressure Fluctuation Prediction for High-Speed Flows via Wall-Modeled Large-Eddy Simulation
2025
Data-Driven Turbulent Prandtl Number Modeling for Hypersonic Shock–Boundary-Layer Interactions
AIAA Journal·2024· 4 cited
Data-driven closure modeling for hypersonic turbulent flows
2024· 1 citedOpen Access
Unraveling Sensitivity and Ensuring Reliability in Reynolds Stress Predictions for Data-Driven RANS
2024Open Access
Correction: Data-Driven Modifications to the Spalart-Allmaras Turbulence Model for Supersonic and Hypersonic Boundary Layers
2024
Turbulent Pressure Fluctuation Coherence Analysis on a Slender Cone at Angle of Attack
2024
Data-Driven Modifications to the Spalart-Allmaras Turbulence Model for Supersonic and Hypersonic Boundary Layers
2024· 2 cited
Lipshitz-continuous tensor-basis neural networks for turbulence modeling in hypersonic flows
2024
Computational Design of a Supersonic Expansion-Compression Experiment for Turbulence Model Development
2024Open Access
Experimental Characterization of an Axisymmetric Transonic Separated Flow for CFD Validation
2023Open Access
Experimental Characterization of an Axisymmetric Transonic Separated Flow for Computational Fluid Dynamics Validation
AIAA Journal·2023· 8 cited
Turbulence modeling for compressible flows using discrepancy tensor-basis neural networks and extrapolation detection
AIAA SCITECH 2023 Forum·2023· 8 cited
Turbulence modeling for compressible flows using discrepancy tensor-basis neural networks and extrapolation detection .
2022Open Access
Internal energy balance and aerodynamic heating predictions for hypersonic turbulent boundary layers
Physical Review Fluids·2022· 5 citedOpen Access
Creating Data-Driven Turbulence Models Using PIV
2022Open Access
A data-driven turbulence modeling framework for the Reynolds-averaged Navier-Stokes equations via discrepancy-based tensor-basis neural networks .
2022Open Access
Data-Driven Calibration of RANS Closure Models with PIV .
2022Open Access
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Frequent Co-authors

Irina Kalashnikova(10), Srinivasan Arunajatesan(6), Dale E. Berg(5), Brian Ray Resor(5), Joshua Paquette(5), William L. Oberkampf(4), Jonathan Berg(4), D. Todd Griffith(4), William J. Devenport(3), Ricardo A. Burdisso(3), Daniel J. Segalman(3), David G. Wilson(3), Brian Owens(3), Michael Eldred(3), Stefan P. Domino(3), Aurélien Borgoltz(2), Patricio A. Ravetta(2), Heidi Thornquist(2), Carlos Ferreira(2), Bart van Bloemen Waanders(2)