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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
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Topics: Artificial neural networks, Turbulence, Flow (Dynamics), Machine learning, Boundary layers, Cylinders, Laminar flow, Simulation, Trains
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
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Topics: Fractals, Machine learning, Turbulence, Dimensions, Artificial neural networks, Flow (Dynamics), Large eddy simulation, Scalars, Shapes, Trains
Proceedings Papers

Deep Learning Techniques for Effective Prediction of Aerodynamic Properties of Elliptical Bluff Bodies

W. M. U. Weerasekara, H. M. C. D. B. Gunarathna, W. A. K. P. Wanigasooriya, T. P. Miyanawala
Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A053, August 10–12, 2021
Paper No: FEDSM2021-66265
DOI: https://doi.org/10.1115/FEDSM2021-66265
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Topics: Aerodynamics, Artificial neural networks, Computation, Computational fluid dynamics, Data collection, Drag (Fluid dynamics), Eddies (Fluid dynamics), Experimental methods, Filters, Flow (Dynamics)
Proceedings Papers

Machine Learning Approach to Predict Sand Transport in Horizontal and Inclined Flow

R. E. Vieira, B. Xu, S. Karimi, S. A. Shirazi
Proc. ASME. FEDSM2021, Volume 3: Fluid Mechanics; Micro and Nano Fluid Dynamics; Multiphase Flow, V003T08A021, August 10–12, 2021
Paper No: FEDSM2021-65229
DOI: https://doi.org/10.1115/FEDSM2021-65229
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Topics: Flow (Dynamics), Machine learning, Sands
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
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Topics: Machine learning, Valves, Risk, Blood flow, Algorithms, Biomedicine, Blood, Cancer, Cloud computing, Computer software
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

A Quantitative Analysis of Machine Learning Based Regressors for Pressure Reconstruction in Particle Image Velocimetry Applications

Manan Basu, Sushrut Kumar, Priyam Gupta, Raj Kumar Singh
Proc. ASME. FEDSM2020, Volume 1: Fluid Applications and Systems; Fluid Measurement and Instrumentation, V001T02A016, July 13–15, 2020
Paper No: FEDSM2020-20262
DOI: https://doi.org/10.1115/FEDSM2020-20262
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Topics: Machine learning, Particulate matter, Pressure
Proceedings Papers

Machine Learning for Turbulence Modeling and Predictions

S. Bhushan, Greg W. Burgreen, D. Martinez, Wes Brewer
Proc. ASME. FEDSM2020, Volume 3: Computational Fluid Dynamics; Micro and Nano Fluid Dynamics, V003T05A008, July 13–15, 2020
Paper No: FEDSM2020-20038
DOI: https://doi.org/10.1115/FEDSM2020-20038
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Topics: Machine learning, Modeling, Turbulence
Proceedings Papers

WearGP: A UQ/ML Wear Prediction Framework for Slurry Pump Impellers and Casings

Anh Tran, Yan Wang, John Furlan, Krishnan V. Pagalthivarthi, Mohamed Garman, Aaron Cutright, Robert Visintainer
Proc. ASME. FEDSM2020, Volume 2: Fluid Mechanics; Multiphase Flows, V002T04A008, July 13–15, 2020
Paper No: FEDSM2020-20059
DOI: https://doi.org/10.1115/FEDSM2020-20059
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Topics: Pump impellers, Slurries, Uncertainty quantification, Wear, Engineering simulation, Simulation, Design, Computational fluid dynamics, Machine learning, Machinery
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
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Topics: Flow (Dynamics), Fluidized beds, Particulate matter, Simulation, Acoustic emissions, Drugs, Dynamic systems, Fluidization, Heat transfer coefficients, Machine learning
Proceedings Papers

Heat Transfer Prediction With Unknown Thermal Boundary Conditions Using Physics-Informed Neural Networks

Shengze Cai, Zhicheng Wang, Chryssostomos Chryssostomidis, George Em Karniadakis
Proc. ASME. FEDSM2020, Volume 3: Computational Fluid Dynamics; Micro and Nano Fluid Dynamics, V003T05A054, July 13–15, 2020
Paper No: FEDSM2020-20159
DOI: https://doi.org/10.1115/FEDSM2020-20159
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Topics: Artificial neural networks, Boundary-value problems, Heat transfer, Physics, Temperature, Cylinders, Temperature measurement, Convection, Thermal boundary layers, Errors
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
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Topics: Optimization, Particulate matter, Engineering simulation, Simulation, Algorithms, Boundary-value problems, Computational fluid dynamics, Flow (Dynamics), Gaussian distribution, Machine learning
Proceedings Papers

Prediction of Drag Reduction Effect of Pulsating Control in Turbulent Pipe Flow by Machine Learning

Wataru Kobayashi, Takaaki Shimura, Akihiko Mitsuishi, Kaoru Iwamoto, Akira Murata
Proc. ASME. AJKFluids2019, Volume 1: Fluid Mechanics, V001T01A011, July 28–August 1, 2019
Paper No: AJKFluids2019-5681
DOI: https://doi.org/10.1115/AJKFluids2019-5681
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Topics: Drag reduction, Machine learning, Pipe flow, Turbulence
Proceedings Papers

Curvature Estimation Modeling Using Machine Learning for CLSVOF Method: Comparison With Conventional Methods

Majid Haghshenas, Ranganathan Kumar
Proc. ASME. AJKFluids2019, Volume 2: Computational Fluid Dynamics, V002T02A078, July 28–August 1, 2019
Paper No: AJKFluids2019-5415
DOI: https://doi.org/10.1115/AJKFluids2019-5415
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Topics: Machine learning, Modeling
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

Route to Chaos in the Fluidic Pinball

Nan Deng, Luc R. Pastur, Marek Morzyński, Bernd R. Noack
Proc. ASME. FEDSM2018, Volume 1: Flow Manipulation and Active Control; Bio-Inspired Fluid Mechanics; Boundary Layer and High-Speed Flows; Fluids Engineering Education; Transport Phenomena in Energy Conversion and Mixing; Turbulent Flows; Vortex Dynamics; DNS/LES and Hybrid RANS/LES Methods; Fluid Structure Interaction; Fluid Dynamics of Wind Energy; Bubble, Droplet, and Aerosol Dynamics, V001T01A005, July 15–20, 2018
Paper No: FEDSM2018-83359
DOI: https://doi.org/10.1115/FEDSM2018-83359
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Topics: Chaos, Flow (Dynamics), Bifurcation, Fluid dynamics, Machine learning, Reynolds number
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

Predicting the Depth of Penetration of Molten Metal Into a Pore Network Using TensorFlow

Arturo Schiaffino, V. M. Krushnarao Kotteda, Arturo Bronson, Sanjay Shantha-Kumar, Vinod Kumar
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, V002T09A018, July 15–20, 2018
Paper No: FEDSM2018-83258
DOI: https://doi.org/10.1115/FEDSM2018-83258
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Topics: Metals, Algorithms, Machine learning, Boron, Flow (Dynamics), Liquid metals, Alloys, Brain, Ceramics, Dynamics (Mechanics)
Proceedings Papers

Utilization of Machine Learning to Predict the Surface Tension of Metals and Alloys

Zackery Nieto, V. M. Krushnarao Kotteda, Arturo Rodriguez, Sanjay Shantha Kumar, Vinod Kumar, Arturo Bronson
Proc. ASME. FEDSM2018, Volume 3: Fluid Machinery; Erosion, Slurry, Sedimentation; Experimental, Multiscale, and Numerical Methods for Multiphase Flows; Gas-Liquid, Gas-Solid, and Liquid-Solid Flows; Performance of Multiphase Flow Systems; Micro/Nano-Fluidics, V003T21A003, July 15–20, 2018
Paper No: FEDSM2018-83248
DOI: https://doi.org/10.1115/FEDSM2018-83248
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Topics: Alloys, Machine learning, Metals, Surface tension, Algorithms, Copper, Silver, Computers, Liquid alloys, Neural network models
Proceedings Papers

Machine Learning Control for Experimental Turbulent Flow Targeting the Reduction of a Recirculation Bubble

Camila Chovet, Marc Lippert, Laurent Keirsbulck, Bernd R. Noack, Jean-Marc Foucaut
Proc. ASME. FEDSM2017, Volume 1C, Symposia: Gas-Liquid Two-Phase Flows; Gas and Liquid-Solid Two-Phase Flows; Numerical Methods for Multiphase Flow; Turbulent Flows: Issues and Perspectives; Flow Applications in Aerospace; Fluid Power; Bio-Inspired Fluid Mechanics; Flow Manipulation and Active Control; Fundamental Issues and Perspectives in Fluid Mechanics; Transport Phenomena in Energy Conversion From Clean and Sustainable Resources; Transport Phenomena in Materials Processing and Manufacturing Processes, V01CT22A005, July 30–August 3, 2017
Paper No: FEDSM2017-69272
DOI: https://doi.org/10.1115/FEDSM2017-69272
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Topics: Bubbles, Machine learning, Turbulence, Feedback, Sensors, Flow (Dynamics), Particulate matter