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Luis F. Rodriguez
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Proceedings Papers

Trilinos Solvers Scalability on a MFiX-Trilinos Framework Applied to Fluidized Bed Simulations

Arturo Rodriguez, V. M. Krushnarao Kotteda, Luis F. Rodriguez, Vinod Kumar, Jorge A. Munoz
Proc. ASME. FEDSM2020, Volume 2: Fluid Mechanics; Multiphase Flows, V002T04A033, July 13–15, 2020
Paper No: FEDSM2020-20250
DOI: https://doi.org/10.1115/FEDSM2020-20250
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Topics: Engineering simulation, Fluidized beds, Multiphase flow, Simulation, Computational fluid dynamics, FORTRAN, Linear systems, Algorithms, Architecture, Flow (Dynamics)
Proceedings Papers

Stochastic Analysis of LES Atmospheric Turbulence Solutions With Generative Machine Learning Models

Arturo Rodriguez, Carlos R. Cuellar, Luis F. Rodriguez, Armando Garcia, V. S. Rao Gudimetla, V. M. Krushnarao Kotteda, Jorge A. Munoz, Vinod Kumar
Proc. ASME. FEDSM2020, Volume 1: Fluid Applications and Systems; Fluid Measurement and Instrumentation, V001T01A001, July 13–15, 2020
Paper No: FEDSM2020-20127
DOI: https://doi.org/10.1115/FEDSM2020-20127
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Topics: Machine learning, Turbulence
Proceedings Papers

Parameter Sensitivity and Statistical Correlation Found in Atmospheric Turbulence Studies

Luis F. Rodriguez, Vinod Kumar, Arturo Rodriguez, V. M. Krushnarao Kotteda, V. S. Rao Gudimetla, Jorge A. Munoz
Proc. ASME. FEDSM2020, Volume 2: Fluid Mechanics; Multiphase Flows, V002T03A041, July 13–15, 2020
Paper No: FEDSM2020-20254
DOI: https://doi.org/10.1115/FEDSM2020-20254
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Topics: Computational fluid dynamics, Turbulence
Proceedings Papers

Branch and Bound Analysis to Characterize Phase Variations in Laser Propagation Through Deep Turbulence

Luis F. Rodriguez, Vinod Kumar, Jose Espiritu, Arturo Bronson, Krushnarao Kotteda, Diego Lozano, Arturo Rodriguez
Proc. ASME. AJKFluids2019, Volume 2: Computational Fluid Dynamics, V002T02A080, July 28–August 1, 2019
Paper No: AJKFluids2019-5567
DOI: https://doi.org/10.1115/AJKFluids2019-5567
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Topics: Lasers, Turbulence, Machine learning, Diffraction, Laser beams, Phase (Wave motion), Waves
Proceedings Papers

Machine Learning Approach to Predict the Flow Rate for an Immiscible Two-Phase Flow at Pore Scale for Enhanced Oil Recovery Application

Arturo Rodriguez, V. M. Krushnarao Kotteda, Luis F. Rodriguez, Vinod Kumar, Arturo Schiaffino, Zackery R. Nieto
Proc. ASME. FEDSM2018, Volume 2: Development and Applications in Computational Fluid Dynamics; Industrial and Environmental Applications of Fluid Mechanics; Fluid Measurement and Instrumentation; Cavitation and Phase Change, V002T09A003, July 15–20, 2018
Paper No: FEDSM2018-83050
DOI: https://doi.org/10.1115/FEDSM2018-83050
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Topics: Flow (Dynamics), Machine learning, Tertiary petroleum recovery, Two-phase flow, Petroleum extraction, Pressure drop, Surface tension, Viscosity, Algorithms, Brain
Proceedings Papers

Turbulent Flow Over a Facing Step at Several Reynolds Numbers

Jose A. Jimenez-Bernal, Adan Juarez-Montalvo, Claudia del C. Gutierrez-Torres, Juan G. Barbosa Saldan˜a, Luis F. Rodriguez-Jimenez
Proc. ASME. IMECE2011, Volume 6: Fluids and Thermal Systems; Advances for Process Industries, Parts A and B, 731-733, November 11–17, 2011
Paper No: IMECE2011-64462
DOI: https://doi.org/10.1115/IMECE2011-64462
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Topics: Reynolds number, Turbulence, Flow (Dynamics), Shear stress, Fitness-for-service, Particulate matter, Transparency, Visualization, Vortices, Water