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Proceedings Papers

Linear Versus Non-Linear Machine Learning Feature Selection for Erosion Rate Prediction

Yijie (Jamie) Li, Jun Zhang, Brett McKinney, Soroor Karimi, Siamack A. Shirazi
Proc. ASME. FEDSM2024, Volume 2: Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC); Flow Visualization, V002T05A005, July 15–17, 2024
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2024-130309
https://doi.org/10.1115/FEDSM2024-130309
View Paper titled, Linear Versus Non-Linear Machine Learning Feature Selection for Erosion Rate Prediction
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Topics: Erosion, Feature selection, Machine learning
Proceedings Papers

Optimization of the Distributor Setup in an Axial Turbine at Speed-No-Load Using Machine Learning

Jelle Kranenbarg, Pontus P. Jonsson, Berhanu G. Mulu, Joel Sundström, Michel J. Cervantes
Proc. ASME. FEDSM2024, Volume 1: Fluid Applications and Systems (FASTC); Fluid Measurement and Instrumentation (FMITC); Fluid Mechanics (FMTC); Multiphase Flow (MFTC), V001T01A006, July 15–17, 2024
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2024-128524
https://doi.org/10.1115/FEDSM2024-128524
View Paper titled, Optimization of the Distributor Setup in an Axial Turbine at Speed-No-Load Using Machine Learning
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Topics: Guide vanes, Hydraulic turbines, Machine learning, Optimization, Stress, Turbines
Proceedings Papers

Enhancing RANS Simulations Through Neural Network-Optimized Closure Coefficients

Philipp Schlichter, Jonas Sebald, Jutta Pieringer, Thomas Indinger
Proc. ASME. FEDSM2024, Volume 2: Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC); Flow Visualization, V002T05A006, July 15–17, 2024
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2024-130410
https://doi.org/10.1115/FEDSM2024-130410
View Paper titled, Enhancing RANS Simulations Through Neural Network-Optimized Closure Coefficients
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Topics: Reynolds-averaged Navier–Stokes equations, Simulation, Geometry, Artificial neural networks, Flow (Dynamics), Computational fluid dynamics, Experimental design, Cylinders, Flow separation, Flow simulation
Proceedings Papers

A Machine-Learned Actuator Line Model for Hydrokinetic Turbines

Joshua Bowman, Shanti Bhushan, Greg Burgreen, Ian Dettwiller
Proc. ASME. FEDSM2024, Volume 2: Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC); Flow Visualization, V002T05A028, July 15–17, 2024
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2024-131429
https://doi.org/10.1115/FEDSM2024-131429
View Paper titled, A Machine-Learned Actuator Line Model for Hydrokinetic Turbines
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Topics: Actuators, Hydraulic turbines, Machine learning, Machinery, Modeling, Additive manufacturing, Turbines, Computational fluid dynamics, Simulation, Blades
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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2022-87412
https://doi.org/10.1115/FEDSM2022-87412
View Paper titled, Is it Turbulent or Laminar? Convolutional Neural Network Predictions
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Topics: Artificial neural networks, Turbulence, Flow (Dynamics), Machine learning, Boundary layers, Cylinders, Laminar flow, Simulation, Trains
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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2021-65229
https://doi.org/10.1115/FEDSM2021-65229
View Paper titled, Machine Learning Approach to Predict Sand Transport in Horizontal and Inclined Flow
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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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2021-65744
https://doi.org/10.1115/FEDSM2021-65744
View Paper titled, A Novel Mathematical Framework for the Venous Valve Leaflet Morphology Extracted From In-Vitro Images Using Machine Learning Assisted Stereological Analysis
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Topics: Machine learning, Valves, Risk, Blood flow, Algorithms, Biomedicine, Blood, Cancer, Cloud computing, Computer software
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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2021-65798
https://doi.org/10.1115/FEDSM2021-65798
View Paper titled, Fractal and Convolutional Analysis for Deep Atmospheric Turbulence Using Machine Learning
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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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2021-66265
https://doi.org/10.1115/FEDSM2021-66265
View Paper titled, Deep Learning Techniques for Effective Prediction of Aerodynamic Properties of Elliptical Bluff Bodies
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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

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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2020-20127
https://doi.org/10.1115/FEDSM2020-20127
View Paper titled, Stochastic Analysis of LES Atmospheric Turbulence Solutions With Generative Machine Learning Models
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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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2020-20262
https://doi.org/10.1115/FEDSM2020-20262
View Paper titled, A Quantitative Analysis of Machine Learning Based Regressors for Pressure Reconstruction in Particle Image Velocimetry Applications
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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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2020-20038
https://doi.org/10.1115/FEDSM2020-20038
View Paper titled, Machine Learning for Turbulence Modeling and Predictions
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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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2020-20059
https://doi.org/10.1115/FEDSM2020-20059
View Paper titled, WearGP: A UQ/ML Wear Prediction Framework for Slurry Pump Impellers and Casings
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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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2020-20126
https://doi.org/10.1115/FEDSM2020-20126
View Paper titled, Characterization of Flow Regimes in Gas-Solid Fluidized Beds via a Data Driven Framework
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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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2020-20159
https://doi.org/10.1115/FEDSM2020-20159
View Paper titled, Heat Transfer Prediction With Unknown Thermal Boundary Conditions Using Physics-Informed Neural Networks
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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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2020-20391
https://doi.org/10.1115/FEDSM2020-20391
View Paper titled, Parametric Optimization of a Dry Powder Inhaler
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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

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
Publisher: American Society of Mechanical Engineers
Paper No: AJKFluids2019-5415
https://doi.org/10.1115/AJKFluids2019-5415
View Paper titled, Curvature Estimation Modeling Using Machine Learning for CLSVOF Method: Comparison With Conventional Methods
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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
Publisher: American Society of Mechanical Engineers
Paper No: AJKFluids2019-5567
https://doi.org/10.1115/AJKFluids2019-5567
View Paper titled, Branch and Bound Analysis to Characterize Phase Variations in Laser Propagation Through Deep Turbulence
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Topics: Lasers, Turbulence, Machine learning, Diffraction, Laser beams, Phase (Wave motion), Waves
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
Publisher: American Society of Mechanical Engineers
Paper No: AJKFluids2019-5681
https://doi.org/10.1115/AJKFluids2019-5681
View Paper titled, Prediction of Drag Reduction Effect of Pulsating Control in Turbulent Pipe Flow by Machine Learning
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Topics: Drag reduction, Machine learning, Pipe flow, Turbulence
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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2018-83248
https://doi.org/10.1115/FEDSM2018-83248
View Paper titled, Utilization of Machine Learning to Predict the Surface Tension of Metals and Alloys
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Topics: Alloys, Machine learning, Metals, Surface tension, Algorithms, Copper, Silver, Computers, Liquid alloys, Neural network models