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David S Ching
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Enriching MRI mean flow data of inclined jets in crossflow with large eddy simulations
PM Milani, IE Gunady, DS Ching, AJ Banko, CJ Elkins, JK Eaton
International Journal of Heat and Fluid Flow 80, 108472, 2019
202019
Shear layer of inclined jets in crossflow studied with spectral proper orthogonal decomposition and spectral transfer entropy
PM Milani, DS Ching, AJ Banko, JK Eaton
International Journal of Heat and Mass Transfer 147, 118972, 2020
152020
Investigation of geometric sensitivity of a non-axisymmetric bump: 3D mean velocity measurements
DS Ching, CJ Elkins, JK Eaton
Experiments in Fluids 59, 1-14, 2018
132018
Reduced order modeling of hypersonic aerodynamics with grid tailoring
D Ching, PJ Blonigan, F Rizzi, JA Fike
AIAA SCITECH 2022 Forum, 1247, 2022
11*2022
Large-eddy simulation study of unsteady wake dynamics and geometric sensitivity on a skewed bump
DS Ching, JK Eaton
Journal of Fluid Mechanics 885, 2020
112020
Unsteady vortex structures in the wake of nonaxisymmetric bumps using spiral MRV
DS Ching, CJ Elkins, MT Alley, JK Eaton
Experiments in Fluids 59, 1-17, 2018
102018
Machine Learning Modeling for RANS Turbulent Kinetic Energy Transport in 3D Separated Flows
DS Ching, AJ Banko, PM Milani, JK Eaton
11th International Symposium on Turbulence and Shear Flow Phenomena, 2019
62019
Turbulence modeling for compressible flows using discrepancy tensor-basis neural networks and extrapolation detection
E Parish, DS Ching, NE Miller, SJ Beresh, MF Barone
AIAA SciTech 2023 Forum, 2126, 2023
42023
Sensitivity-informed bayesian inference for home PLC network models with unknown parameters
DS Ching, C Safta, TA Reichardt
Energies 14 (9), 2402, 2021
42021
3D Measurements of coupled freestream turbulence and secondary flow effects on film cooling
DS Ching, HHA Xu, CJ Elkins, JK Eaton
Experiments in Fluids 59, 1-16, 2018
42018
Efficient sampling methods for machine learning error models with application to surrogates of steady hypersonic flows
EH Krath, DS Ching, PJ Blonigan
AIAA SCITECH 2022 Forum, 1249, 2022
32022
Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference
R Wonnacott, DS Ching, J Chilleri, C Safta, L Rashkin, TA Reichardt
Sensors 23 (5), 2416, 2023
22023
Lipshitz-continuous tensor-basis neural networks for turbulence modeling in hypersonic flows
E Parish, DS Ching, NE Miller, S Beresh, MF Barone, N Gupta, ...
AIAA SCITECH 2024 Forum, 0070, 2024
12024
Residual minimization formulations for model reduction of steady hypersonic flow
RL Van Heyningen, DS Ching, PJ Blonigan, EJ Parish, F Rizzi
AIAA Aviation 2023 Forum, 3267, 2023
12023
On correcting the eddy-viscosity models in RANS simulations for turbulent flows and scalar transport around obstacles
Z Hao, C Gorlé, DS Ching, JK Eaton
arXiv preprint arXiv:2206.14469, 2022
12022
Reduced Order Models of Hypersonic Aerodynamics for Aerothermal Heating Analysis
DS Ching, PJ Blonigan, MC Sands, JC Murray
AIAA SCITECH 2024 Forum, 1293, 2024
2024
A data-driven turbulence modeling framework for the Reynolds-averaged Navier-Stokes equations via discrepancy-based tensor-basis neural networks.
E Parish, M Barone, N Miller, D Ching, S Beresh
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022
2022
Grid Tailored Reduced-Order Models for Steady Hypersonic Aerodynamics.
D Ching, P Blonigan, M Arienti, F Rizzi, J Fike
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021
2021
Quantifying the impact of the initial guess for projection-based model reduction of steady hypersonic aerodynamics.
D Ching, PJ Blonigan, F Rizzi, M Howard, JA Fike
Sandia National Lab.(SNL-CA), Livermore, CA (United States); Sandia National …, 2020
2020
Machine Learning Uncertainty Quantification for Reduced Order Models of Hypersonic Flows.
D Ching, PJ Blonigan
Sandia National Lab.(SNL-CA), Livermore, CA (United States), 2020
2020
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Articles 1–20