Search Results | ASME Digital Collection

1-20 of 30

Sort by

Sort Order Select

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

Designing an Experimental Setup for Analyzing the Flow Through a 3D Printed Venous Valve System From Arthroscopic Images

Alan Rascon, Nazmul Hossain, V. M. Krushnarao Kotteda, Christopher Harris, Herbert Janssen, Ellen Dudrey, Vinod Kumar, Amit Lopes

Proc. ASME. FEDSM2022, Volume 1: Fluid Applications and Systems (FASTC); Fluid Measurement and Instrumentation (FMITC); Fluid Mechanics (FMTC), V001T03A017, August 3–5, 2022

Paper No: FEDSM2022-87419

DOI: https://doi.org/10.1115/FEDSM2022-87419

View Paper

Topics: Additive manufacturing, Arthroscopy, Design, Flow (Dynamics), Valves

Proceedings Papers

Learning and Engaging Through Transformative Informal Setting Driven by COVID Induced Leading Edge Virtual Technology for Advanced Fluid Mechanics Modeling and Simulations Seminar Series

Demy Rodriguez, Leslie Trevizo, Arturo Rodriguez, Julio Aguilar, Clinton Chijioke, Brandon Paez, Rafael Baez, V. M. Krushnarao Kotteda, Vinod Kumar

Proc. ASME. FEDSM2022, Volume 1: Fluid Applications and Systems (FASTC); Fluid Measurement and Instrumentation (FMITC); Fluid Mechanics (FMTC), V001T03A016, August 3–5, 2022

Paper No: FEDSM2022-87409

DOI: https://doi.org/10.1115/FEDSM2022-87409

View Paper

Topics: Engineering simulation, Fluid mechanics, Modeling, Pandemics, Simulation

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

Journal Articles

On-Sun Testing of a High-Temperature Solar Receiver’s Flux Distribution

Samia Afrin, Nazmul Hossain, Zhiwen Ma, V. M. Krushnarao Kotteda, Antara Badhan, Vinod Kumar

J. Sol. Energy Eng. April 2022, 144(2): 021001.

Paper No: SOL-21-1142

DOI: https://doi.org/10.1115/1.4052550

Published Online: October 19, 2021

View Article

Topics: Solar energy, Testing, High temperature, Modeling, Temperature, Furnaces

Proceedings Papers

A Novel Mathematical Framework for the Venous Valve Leaflet Morphology Extracted From In-Vitro Images Using Machine Learning Assisted Stereological Analysis

V. M. Krushnarao Kotteda, Herb F. Janssen, Christopher Harris, Vinod Kumar

Proc. ASME. FEDSM2021, Volume 3: Fluid Mechanics; Micro and Nano Fluid Dynamics; Multiphase Flow, V003T05A008, August 10–12, 2021

Paper No: FEDSM2021-65744

DOI: https://doi.org/10.1115/FEDSM2021-65744

View Paper

Topics: Machine learning, Valves, Risk, Blood flow, Algorithms, Biomedicine, Blood, Cancer, Cloud computing, Computer software

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

Journal Articles

Quantifying Uncertainty in the Residence Time of the Drug and Carrier Particles in a Dry Powder Inhaler

Antara Badhan, V. M. Krushnarao Kotteda, Samia Afrin, Vinod Kumar

ASME J. Risk Uncertainty Part B. September 2021, 7(3): 031004.

Paper No: RISK-20-1107

DOI: https://doi.org/10.1115/1.4050250

Published Online: May 28, 2021

View Article

Topics: Drugs, Particulate matter, Density, Uncertainty quantification, 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

View Paper

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

View Paper

Topics: Computational fluid dynamics, Turbulence

Proceedings Papers

Characterization of Flow Regimes in Gas-Solid Fluidized Beds via a Data Driven Framework

V. M. Krushnarao Kotteda, Anitha Kommu, Vinod Kumar

Proc. ASME. FEDSM2020, Volume 3: Computational Fluid Dynamics; Micro and Nano Fluid Dynamics, V003T05A053, July 13–15, 2020

Paper No: FEDSM2020-20126

DOI: https://doi.org/10.1115/FEDSM2020-20126

View Paper

Topics: Flow (Dynamics), Fluidized beds, Particulate matter, Simulation, Acoustic emissions, Drugs, Dynamic systems, Fluidization, Heat transfer coefficients, Machine learning

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

View Paper

Topics: Engineering simulation, Fluidized beds, Multiphase flow, Simulation, Computational fluid dynamics, FORTRAN, Linear systems, Algorithms, Architecture, Flow (Dynamics)

Proceedings Papers

Parametric Optimization of a Dry Powder Inhaler

V. M. Krushnarao Kotteda, Antara Badhan, Vinod Kumar

Proc. ASME. FEDSM2020, Volume 3: Computational Fluid Dynamics; Micro and Nano Fluid Dynamics, V003T05A059, July 13–15, 2020

Paper No: FEDSM2020-20391

DOI: https://doi.org/10.1115/FEDSM2020-20391

View Paper

Topics: Optimization, Particulate matter, Engineering simulation, Simulation, Algorithms, Boundary-value problems, Computational fluid dynamics, Flow (Dynamics), Gaussian distribution, Machine learning

Journal citation

NARROW

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

Designing an Experimental Setup for Analyzing the Flow Through a 3D Printed Venous Valve System From Arthroscopic Images

Learning and Engaging Through Transformative Informal Setting Driven by COVID Induced Leading Edge Virtual Technology for Advanced Fluid Mechanics Modeling and Simulations Seminar Series

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

On-Sun Testing of a High-Temperature Solar Receiver’s Flux Distribution

A Novel Mathematical Framework for the Venous Valve Leaflet Morphology Extracted From In-Vitro Images Using Machine Learning Assisted Stereological Analysis

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

Quantifying Uncertainty in the Residence Time of the Drug and Carrier Particles in a Dry Powder Inhaler

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

Parameter Sensitivity and Statistical Correlation Found in Atmospheric Turbulence Studies

Characterization of Flow Regimes in Gas-Solid Fluidized Beds via a Data Driven Framework

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

Parametric Optimization of a Dry Powder Inhaler

Journals

Conference Proceedings

eBooks

Resources

Opportunities

Journals

Conference Proceedings

eBooks

Resources

Opportunities

This Feature Is Available To Subscribers Only