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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

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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2022-87636
https://doi.org/10.1115/FEDSM2022-87636
View Paper titled, FSI of a Cantilever Beam: FVM-FEM and Neural Network Analysis
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Topics: Artificial neural networks, Cantilever beams, Computational fluid dynamics, Finite element methods, Finite volume methods, Fluid structure interaction
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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2022-86953
https://doi.org/10.1115/FEDSM2022-86953
View Paper titled, Physics-Informed Long-Short Term Memory Neural Network Performance on Holloman High-Speed Test Track Sled Study
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Topics: Artificial neural networks, Physics, Simulation, Braking, Fluid dynamics, Architecture, Computational fluid dynamics, Errors, Fluids, Geometry
Proceedings Papers

Inferring Unsteady Wake Flow Fields From Partial Data by Physics-Informed Neural Networks

Chang Yan, Shengjun Ju, Dilong Guo, Guowei Yang, Shuanbao Yao
Proc. ASME. FEDSM2022, Volume 2: Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC), V002T05A013, August 3–5, 2022
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2022-86945
https://doi.org/10.1115/FEDSM2022-86945
View Paper titled, Inferring Unsteady Wake Flow Fields From Partial Data by Physics-Informed Neural Networks
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Topics: Artificial neural networks, Noise (Sound), Physics, Wakes
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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2022-86957
https://doi.org/10.1115/FEDSM2022-86957
View Paper titled, Field Predictions of Hypersonic Cones Using Physics-Informed Neural Networks
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Topics: Artificial neural networks, Physics, Compressible flow, Computational fluid dynamics, Differential equations, Dimensions, Flow (Dynamics), Fluid dynamics, Inverse problems, Mach number
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

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
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2021-65794
https://doi.org/10.1115/FEDSM2021-65794
View Paper titled, A CNN With Deep Learning for Non-Equilibrium Characterization of Al-Sm Melt Infusion Into a B4C Packed Bed
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Topics: Alloys, Artificial neural networks, Equilibrium (Physics), Fluid dynamics, High temperature, Metals, Porosity, Scanning electron microscopes, Temperature, 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
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

Numerical Performance Predictions of Artificial Intelligence-Driven Centrifugal Compressor Designs

Manuel Fritsche, Philipp Epple, Boris Kubrak, Stefan Gast, Antonio Delgado, Valentyn Barannik
Proc. ASME. FEDSM2020, Volume 1: Fluid Applications and Systems; Fluid Measurement and Instrumentation, V001T01A034, July 13–15, 2020
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2020-20087
https://doi.org/10.1115/FEDSM2020-20087
View Paper titled, Numerical Performance Predictions of Artificial Intelligence-Driven Centrifugal Compressor Designs
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Topics: Compressors, Design, Turbomachinery, Artificial neural networks, Performance characterization, Computer software, Errors, Geometry, Machinery, Numerical analysis
Proceedings Papers

Investigation of Applying Physics Informed Neural Networks (PINN) and Variants on 2D Aerodynamics Problems

Wee-Beng Tay, Murali Damodaran, Zhi-Da Teh, Rahul Halder
Proc. ASME. FEDSM2020, Volume 3: Computational Fluid Dynamics; Micro and Nano Fluid Dynamics, V003T05A055, July 13–15, 2020
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2020-20184
https://doi.org/10.1115/FEDSM2020-20184
View Paper titled, Investigation of Applying Physics Informed Neural Networks (PINN) and Variants on 2D Aerodynamics Problems
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Topics: Aerodynamics, Artificial neural networks, Nozzles, Physics
Proceedings Papers

Investigating the Effect of Configuration and Reduced Frequency on SD7003 Airfoils Performance in Tandem

Niloofar Hosseini, Mehran Tadjfar
Proc. ASME. FEDSM2020, Volume 3: Computational Fluid Dynamics; Micro and Nano Fluid Dynamics, V003T05A056, July 13–15, 2020
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2020-20196
https://doi.org/10.1115/FEDSM2020-20196
View Paper titled, Investigating the Effect of Configuration and Reduced Frequency on SD7003 Airfoils Performance in Tandem
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Topics: Airfoils, Artificial neural networks, Genetic algorithms, Optimization
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

CNN-SINDy Based Reduced Order Modeling of Unsteady Flow Fields

Takaaki Murata, Kai Fukami, Koji Fukagata
Proc. ASME. AJKFluids2019, Volume 2: Computational Fluid Dynamics, V002T02A074, July 28–August 1, 2019
Publisher: American Society of Mechanical Engineers
Paper No: AJKFluids2019-5056
https://doi.org/10.1115/AJKFluids2019-5056
View Paper titled, CNN-SINDy Based Reduced Order Modeling of Unsteady Flow Fields
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Topics: Artificial neural networks, Circular cylinders, Computation, Computer simulation, Errors, Flow (Dynamics), Modeling, Nonlinear dynamics, Principal component analysis, Unsteady flow
Proceedings Papers

Predictability Study of Viscoelastic Turbulent Channel Flow Using Deep Learning

Atsushi Nagamachi, Takahiro Tsukahara
Proc. ASME. AJKFluids2019, Volume 2: Computational Fluid Dynamics, V002T02A077, July 28–August 1, 2019
Publisher: American Society of Mechanical Engineers
Paper No: AJKFluids2019-5261
https://doi.org/10.1115/AJKFluids2019-5261
View Paper titled, Predictability Study of Viscoelastic Turbulent Channel Flow Using Deep Learning
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Topics: Artificial neural networks, Channel flow, Computer simulation, Multilayer perceptron, Turbulence, Viscoelastic fluids
Proceedings Papers

Numerical Simulation and Neural Network Study Using an Upstream Cylinder for Flow Control of an Airfoil

Meihua Zhang, Zhongquan Charlie Zheng, Yangliu Liu, Xiaoyu Jiang
Proc. ASME. AJKFluids2019, Volume 2: Computational Fluid Dynamics, V002T02A045, July 28–August 1, 2019
Publisher: American Society of Mechanical Engineers
Paper No: AJKFluids2019-4724
https://doi.org/10.1115/AJKFluids2019-4724
View Paper titled, Numerical Simulation and Neural Network Study Using an Upstream Cylinder for Flow Control of an Airfoil
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Topics: Airfoils, Algorithms, Artificial neural networks, Computer simulation, Cylinders, Flow control, Genetic algorithms
Proceedings Papers

Reconstruction of RANS Model and Cross-Validation of Flow Field Based on Tensor Basis Neural Network

Xudong Song, Zhen Zhang, Yiwei Wang, Shuran Ye, Chenguang Huang
Proc. ASME. AJKFluids2019, Volume 2: Computational Fluid Dynamics, V002T02A081, July 28–August 1, 2019
Publisher: American Society of Mechanical Engineers
Paper No: AJKFluids2019-5572
https://doi.org/10.1115/AJKFluids2019-5572
View Paper titled, Reconstruction of RANS Model and Cross-Validation of Flow Field Based on Tensor Basis Neural Network
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Topics: Artificial neural networks, Flow (Dynamics), Reynolds-averaged Navier–Stokes equations, Stress, Tensors, Turbulence
Proceedings Papers

Synthetic Jet Flow Control Optimization on SD7003 Airfoil at Low Reynolds Number

Djavad Kamari, Mehran Tadjfar
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, V001T01A004, July 15–20, 2018
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2018-83195
https://doi.org/10.1115/FEDSM2018-83195
View Paper titled, Synthetic Jet Flow Control Optimization on SD7003 Airfoil at Low Reynolds Number
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Topics: Airfoils, Jets, Optimization, Reynolds number, Artificial neural networks, Boundary layers, Design, Drag (Fluid dynamics), Flow (Dynamics), Form drag
Proceedings Papers

Numerical Simulation of Formation of Melt Jets in Melt-Coolant Interactions

Jorge Contreras-Serna, Arturo Schiaffino, V. M. Krushnarao Kotteda, Alejandro J. García-Cuéllar, 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, V002T09A019, July 15–20, 2018
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2018-83273
https://doi.org/10.1115/FEDSM2018-83273
View Paper titled, Numerical Simulation of Formation of Melt Jets in Melt-Coolant Interactions
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Topics: Computer simulation, Coolants, Jets, Water, Liquid metals, Vapors, Computational fluid dynamics, Pressure, Simulation, Algorithms
Proceedings Papers

Multi-Objective Optimization on Inlet Pipe of a Vertical Inline Pump Based on Genetic Algorithm and Artificial Neural Network

Xingcheng Gan, Ji Pei, Shouqi Yuan, Wenjie Wang, Yajing Tang
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, V001T06A003, July 15–20, 2018
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2018-83053
https://doi.org/10.1115/FEDSM2018-83053
View Paper titled, Multi-Objective Optimization on Inlet Pipe of a Vertical Inline Pump Based on Genetic Algorithm and Artificial Neural Network
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Topics: Artificial neural networks, Genetic algorithms, Pareto optimization, Pipes, Pumps, Optimization, Computer simulation, Design, Flow (Dynamics), Impellers