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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

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
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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
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

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
Paper No: FEDSM2020-20087
DOI: https://doi.org/10.1115/FEDSM2020-20087
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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
Paper No: FEDSM2020-20184
DOI: https://doi.org/10.1115/FEDSM2020-20184
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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
Paper No: FEDSM2020-20196
DOI: https://doi.org/10.1115/FEDSM2020-20196
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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
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

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
Paper No: AJKFluids2019-5056
DOI: https://doi.org/10.1115/AJKFluids2019-5056
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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
Paper No: AJKFluids2019-5261
DOI: https://doi.org/10.1115/AJKFluids2019-5261
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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
Paper No: AJKFluids2019-4724
DOI: https://doi.org/10.1115/AJKFluids2019-4724
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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
Paper No: AJKFluids2019-5572
DOI: https://doi.org/10.1115/AJKFluids2019-5572
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Topics: Artificial neural networks, Flow (Dynamics), Reynolds-averaged Navier–Stokes equations, Stress, Tensors, Turbulence
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
Paper No: FEDSM2018-83273
DOI: https://doi.org/10.1115/FEDSM2018-83273
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Topics: Computer simulation, Coolants, Jets, Water, Liquid metals, Vapors, Computational fluid dynamics, Pressure, Simulation, Algorithms
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
Paper No: FEDSM2018-83195
DOI: https://doi.org/10.1115/FEDSM2018-83195
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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

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
Paper No: FEDSM2018-83053
DOI: https://doi.org/10.1115/FEDSM2018-83053
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Topics: Artificial neural networks, Genetic algorithms, Pareto optimization, Pipes, Pumps, Optimization, Computer simulation, Design, Flow (Dynamics), Impellers
Proceedings Papers

Accelerating the High-Fidelity Simulation of Turbulence: Ensemble Averaging

Vakhtang Makarashvili, Elia Merzari, Aleksandr Obabko, Paul Fischer, Andrew Siegel
Proc. ASME. FEDSM2016, Volume 1B, Symposia: Fluid Mechanics (Fundamental Issues and Perspectives; Industrial and Environmental Applications); Multiphase Flow and Systems (Multiscale Methods; Noninvasive Measurements; Numerical Methods; Heat Transfer; Performance); Transport Phenomena (Clean Energy; Mixing; Manufacturing and Materials Processing); Turbulent Flows — Issues and Perspectives; Algorithms and Applications for High Performance CFD Computation; Fluid Power; Fluid Dynamics of Wind Energy; Marine Hydrodynamics, V01BT26A003, July 10–14, 2016
Paper No: FEDSM2016-7853
DOI: https://doi.org/10.1115/FEDSM2016-7853
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Topics: Simulation, Turbulence, Transients (Dynamics), Computational fluid dynamics, Architecture, Flow (Dynamics), Algorithms, Reynolds-averaged Navier–Stokes equations, Separation (Technology), Artificial neural networks
Proceedings Papers

Free Vortex Turbulent Swirling Flow Reconstruction Using a Neural Network

Saad A. Ahmed, Bharath V. Raghavan
Proc. ASME. FEDSM2012, Volume 1: Symposia, Parts A and B, 783-788, July 8–12, 2012
Paper No: FEDSM2012-72468
DOI: https://doi.org/10.1115/FEDSM2012-72468
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Topics: Artificial neural networks, Combustion chambers, Swirling flow, Turbulence, Vortices
Proceedings Papers

Intelligent Regime Recognition in Upward Vertical Gas-Liquid Two Phase Flow Using Neural Network Techniques

Soheil Ghanbarzadeh, Pedram Hanafizadeh, Mohammad Hassan Saidi, Ramin Bozorgmehry Boozarjomehry
Proc. ASME. FEDSM2010, ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting: Volume 2, Fora, 293-302, August 1–5, 2010
Paper No: FEDSM-ICNMM2010-31126
DOI: https://doi.org/10.1115/FEDSM-ICNMM2010-31126
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Topics: Artificial neural networks, Two-phase flow, Flow (Dynamics), Pressure, Signals, Density, Design, Fast Fourier transforms, Flow visualization, Multilayer perceptron
Proceedings Papers

Calibration of Triple-Wire Probes Using an Artificial Neural Network

Noorallah Rostamy, Soheil Akbari, David Sumner, Donald J. Bergstrom
Proc. ASME. FEDSM2010, ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting: Volume 2, Fora, 217-224, August 1–5, 2010
Paper No: FEDSM-ICNMM2010-31198
DOI: https://doi.org/10.1115/FEDSM-ICNMM2010-31198
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Topics: Artificial neural networks, Calibration, Probes, Wire, Algorithms, Cross-flow, Errors, Feedforward control, Flow (Dynamics), Interpolation
Proceedings Papers

Three-Dimensional Aerodynamic Optimization for Axial-Flow Compressors Based Upon the Inverse Design and the Aerodynamic Variables

Liu He, Peng Shan
Proc. ASME. FEDSM2010, ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting: Volume 1, Symposia – Parts A, B, and C, 467-479, August 1–5, 2010
Paper No: FEDSM-ICNMM2010-30039
DOI: https://doi.org/10.1115/FEDSM-ICNMM2010-30039
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Topics: Axial flow, Compressors, Design, Optimization, Blades, Fans, Rotors, Airfoils, Artificial neural networks, Computational fluid dynamics
Proceedings Papers

Predictions of Turbulence Intensity in a Combustor Model Using Neural Network Analysis

Saad A. Ahmed, Hany El Kadi
Proc. ASME. FEDSM2010, ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting: Volume 1, Symposia – Parts A, B, and C, 893-898, August 1–5, 2010
Paper No: FEDSM-ICNMM2010-30834
DOI: https://doi.org/10.1115/FEDSM-ICNMM2010-30834
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Topics: Artificial neural networks, Combustion chambers, Flow (Dynamics), Turbulence