Search Results | ASME Digital Collection

1-14 of 14

Sort by

Sort Order Select

Proceedings Papers

FSI of a Cantilever Beam: FVM-FEM and Neural Network Analysis

Clinton Chijioke, Arturo Rodriguez, Andres Enriquez, Vinod Kumar, Vivek Tandon, Jose Terrazas, Daniel Villanueva, V. M. Krushnarao Kotteda

Proc. ASME. FEDSM2022, Volume 1: Fluid Applications and Systems (FASTC); Fluid Measurement and Instrumentation (FMITC); Fluid Mechanics (FMTC), V001T03A025, August 3–5, 2022

Paper No: FEDSM2022-87636

DOI: https://doi.org/10.1115/FEDSM2022-87636

View Paper

Topics: Artificial neural networks, Cantilever beams, Computational fluid dynamics, Finite element methods, Finite volume methods, Fluid structure interaction

Proceedings Papers

Atmospheric Turbulence Intermittency Effects on Remote Sensing Laser Propagation

Arturo Rodriguez, V. S. Rao Gudimetla, Richard Adansi, Jose Terrazas, Vicente Corral, Christopher Harris, Vinod Kumar, Rafael Baez, Brandon Paez

Proc. ASME. FEDSM2022, Volume 1: Fluid Applications and Systems (FASTC); Fluid Measurement and Instrumentation (FMITC); Fluid Mechanics (FMTC), V001T03A008, August 3–5, 2022

Paper No: FEDSM2022-86958

DOI: https://doi.org/10.1115/FEDSM2022-86958

View Paper

Topics: Lasers, Turbulence, Fluid dynamics, Refractive index, Approximation, Computer simulation, Statistical distributions, Tradeoffs

Proceedings Papers

Physics-Informed Long-Short Term Memory Neural Network Performance on Holloman High-Speed Test Track Sled Study

José Pérez, Rafael Baez, Jose Terrazas, Arturo Rodríguez, Daniel Villanueva, Olac Fuentes, Vinod Kumar, Brandon Paez, Abdiel Cruz

Proc. ASME. FEDSM2022, Volume 2: Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC), V002T05A014, August 3–5, 2022

Paper No: FEDSM2022-86953

DOI: https://doi.org/10.1115/FEDSM2022-86953

View Paper

Topics: Artificial neural networks, Physics, Simulation, Braking, Fluid dynamics, Architecture, Computational fluid dynamics, Errors, Fluids, Geometry

Proceedings Papers

Aerodynamic Performance of Symmetric and Cambered Airfoils at Low Reynolds Numbers

Abdiel Cruz, Arturo Rodriguez, V. M. Krushnarao Kotteda, Brandon Paez, Daniel Villanueva, Clinton Chijioke, Jose Terrazas, Vinod Kumar

Proc. ASME. FEDSM2022, Volume 1: Fluid Applications and Systems (FASTC); Fluid Measurement and Instrumentation (FMITC); Fluid Mechanics (FMTC), V001T03A009, August 3–5, 2022

Paper No: FEDSM2022-86960

DOI: https://doi.org/10.1115/FEDSM2022-86960

View Paper

Topics: Aerodynamics, Airfoils, Reynolds number, Drag (Fluid dynamics), Simulation, Approximation, Computational fluid dynamics, Computer software, Mach number, Vehicles

Proceedings Papers

Field Predictions of Hypersonic Cones Using Physics-Informed Neural Networks

Daniel Villanueva, Brandon Paez, Arturo Rodriguez, Ashesh Chattopadhyay, V. M. Krushnarao Kotteda, Rafael Baez, Jose Perez, Jose Terrazas, Vinod Kumar

Proc. ASME. FEDSM2022, Volume 2: Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC), V002T05A015, August 3–5, 2022

Paper No: FEDSM2022-86957

DOI: https://doi.org/10.1115/FEDSM2022-86957

View Paper

Topics: Artificial neural networks, Physics, Compressible flow, Computational fluid dynamics, Differential equations, Dimensions, Flow (Dynamics), Fluid dynamics, Inverse problems, Mach number

Proceedings Papers

Mesh Adaptability Technique for Canonical Turbulent Jet Flows via Reinforcement Learning

Brandon Paez, Arturo Rodriguez, V. M. Krushnarao Kotteda, Ashesh Chattopadhyay, Jose Terrazas, Rafael Baez, Vinod Kumar

Proc. ASME. FEDSM2022, Volume 2: Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC), V002T05A021, August 3–5, 2022

Paper No: FEDSM2022-87413

DOI: https://doi.org/10.1115/FEDSM2022-87413

View Paper

Topics: Flow (Dynamics), Reinforcement learning, Turbulence

Proceedings Papers

Is it Turbulent or Laminar? Convolutional Neural Network Predictions

Nicholas Dudu, Arturo Rodriguez, V. M. Krushnarao Kotteda, Jose Terrazas, Daniel Villanueva, Clinton Chijioke, Rafael Baez, Brandon Paez, Vinod Kumar

Proc. ASME. FEDSM2022, Volume 2: Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC), V002T05A020, August 3–5, 2022

Paper No: FEDSM2022-87412

DOI: https://doi.org/10.1115/FEDSM2022-87412

View Paper

Topics: Artificial neural networks, Turbulence, Flow (Dynamics), Machine learning, Boundary layers, Cylinders, Laminar flow, Simulation, Trains

Proceedings Papers

Three-Dimensional Two-Phase Flow Simulations of Water Braking Phenomena for High-Speed Test Track Sled

Jose Terrazas, Arturo Rodriguez, Vinod Kumar, Richard Adansi, V. M. Krushnarao Kotteda

Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A009, August 10–12, 2021

Paper No: FEDSM2021-65799

DOI: https://doi.org/10.1115/FEDSM2021-65799

View Paper

Topics: Braking, Computational fluid dynamics, Engineering simulation, Multiphase flow, Simulation, Two-phase flow, Water, Brakes, Inflow, Vehicles

Proceedings Papers

Fractal and Convolutional Analysis for Deep Atmospheric Turbulence Using Machine Learning

Nicholas Dudu, Arturo Rodriguez, Gael Moran, Jose Terrazas, Richard Adansi, V. M. Krushnarao Kotteda, Christopher Harris, Vinod Kumar

Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A051, August 10–12, 2021

Paper No: FEDSM2021-65798

DOI: https://doi.org/10.1115/FEDSM2021-65798

View Paper

Topics: Fractals, Machine learning, Turbulence, Dimensions, Artificial neural networks, Flow (Dynamics), Large eddy simulation, Scalars, Shapes, Trains

Proceedings Papers

Aerodynamic Performance of Design for a CO 2 Dragster

Brandon Paez, Arturo Rodriguez, Nicholas Dudu, Jose Terrazas, Richard Adansi, V. M. Krushnarao Kotteda, Julio C. Aguilar, Vinod Kumar

Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A040, August 10–12, 2021

Paper No: FEDSM2021-65793

DOI: https://doi.org/10.1115/FEDSM2021-65793

View Paper

Topics: Carbon dioxide, Design, Fluid structure interaction

Proceedings Papers

Performing Fourier Transform on a Velocity Profile From Atmospheric Turbulence Studies

Richard Adansi, Jose Terrazas, Arturo Rodriguez, V. M. Krushnarao Kotteda, Vinod Kumar, Aldo Rubio, Edgar Avalos

Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A035, August 10–12, 2021

Paper No: FEDSM2021-65812

DOI: https://doi.org/10.1115/FEDSM2021-65812

View Paper

Topics: Fourier transforms, Turbulence

Proceedings Papers

Causal Inference Analysis to Find Relationships Found in Boundary-Layer Transition – Part I: Theoretical

Arturo Rodriguez, Jose Terrazas, Richard Adansi, V. M. Krushnarao Kotteda, Jorge A. Munoz, Vinod Kumar

Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A050, August 10–12, 2021

Paper No: FEDSM2021-61843

DOI: https://doi.org/10.1115/FEDSM2021-61843

View Paper

Topics: Boundary layers, Turbulence, Flow (Dynamics), Computational fluid dynamics, Fluctuations (Physics), Reynolds number, Vehicles, Defense industry, Design, Fluid dynamics

Proceedings Papers

A CNN With Deep Learning for Non-Equilibrium Characterization of Al-Sm Melt Infusion Into a B4C Packed Bed

Julio Aguilar, Laura Sandoval, Arturo Rodriguez, Sanjay Shantha Kumar, Jose Terrazas, Richard Adansi, Vinod Kumar, Arturo Bronson

Proc. ASME. FEDSM2021, Volume 3: Fluid Mechanics; Micro and Nano Fluid Dynamics; Multiphase Flow, V003T08A020, August 10–12, 2021

Paper No: FEDSM2021-65794

DOI: https://doi.org/10.1115/FEDSM2021-65794

View Paper

Topics: Alloys, Artificial neural networks, Equilibrium (Physics), Fluid dynamics, High temperature, Metals, Porosity, Scanning electron microscopes, Temperature, Vehicles

Proceedings Papers

The CFD Modeling of the Water Braking Phenomena for the Holloman High-Speed Test Track

Jose Terrazas, V. M. Krushnarao Kotteda, Vinod Kumar, Robert Edmonds, Michelle Zeisset

Proc. ASME. AJKFluids2019, Volume 1: Fluid Mechanics, V001T01A053, July 28–August 1, 2019

Paper No: AJKFluids2019-5506

DOI: https://doi.org/10.1115/AJKFluids2019-5506

View Paper

Topics: Braking, Computational fluid dynamics, Modeling, Water

Journal citation

NARROW

FSI of a Cantilever Beam: FVM-FEM and Neural Network Analysis

Atmospheric Turbulence Intermittency Effects on Remote Sensing Laser Propagation

Physics-Informed Long-Short Term Memory Neural Network Performance on Holloman High-Speed Test Track Sled Study

Aerodynamic Performance of Symmetric and Cambered Airfoils at Low Reynolds Numbers

Field Predictions of Hypersonic Cones Using Physics-Informed Neural Networks

Mesh Adaptability Technique for Canonical Turbulent Jet Flows via Reinforcement Learning

Is it Turbulent or Laminar? Convolutional Neural Network Predictions

Three-Dimensional Two-Phase Flow Simulations of Water Braking Phenomena for High-Speed Test Track Sled

Fractal and Convolutional Analysis for Deep Atmospheric Turbulence Using Machine Learning

Aerodynamic Performance of Design for a CO 2 Dragster

Performing Fourier Transform on a Velocity Profile From Atmospheric Turbulence Studies

Causal Inference Analysis to Find Relationships Found in Boundary-Layer Transition – Part I: Theoretical

A CNN With Deep Learning for Non-Equilibrium Characterization of Al-Sm Melt Infusion Into a B4C Packed Bed

The CFD Modeling of the Water Braking Phenomena for the Holloman High-Speed Test Track

Journals

Conference Proceedings

eBooks

Resources

Opportunities

Journals

Conference Proceedings

eBooks

Resources

Opportunities

This Feature Is Available To Subscribers Only