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Proceedings Papers

Using Machine Learning to Identify Important Parameters for Flow-Induced Vibration

Leixin Ma, Themistocles L. Resvanis, J. Kim Vandiver
Proc. ASME. OMAE2020, Volume 4: Pipelines, Risers, and Subsea Systems, V004T04A011, August 3–7, 2020
Paper No: OMAE2020-18325
DOI: https://doi.org/10.1115/OMAE2020-18325
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Topics: Feature selection, Flow-induced vibrations, Machine learning, Vortex-induced vibration, Cylinders, Vibration, Damping, Errors, Reynolds number, Artificial neural networks
Proceedings Papers

Real-Time Wellhead Bending Moment Measurement Using Motion Reference Unit (MRU) Sensors and Machine Learning

Rohit Srikonda, Rune Haakonsen, Massimiliano Russo, Peri Periyasamy
Proc. ASME. OMAE2018, Volume 3: Structures, Safety, and Reliability, V003T02A095, June 17–22, 2018
Paper No: OMAE2018-78301
DOI: https://doi.org/10.1115/OMAE2018-78301
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Topics: Machine learning, Sensors, Pipeline risers, Tension, Time series, Errors, Artificial neural networks, Cycles, Fatigue damage, Finite element analysis
Proceedings Papers

Prediction of Characteristics of Wave Breaking in Shallow Water Using Neural Network Techniques

Nicholas Kouvaras, Manhar R. Dhanak
Proc. ASME. OMAE2017, Volume 7A: Ocean Engineering, V07AT06A030, June 25–30, 2017
Paper No: OMAE2017-62283
DOI: https://doi.org/10.1115/OMAE2017-62283
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Topics: Artificial neural networks, Water, Waves, Neural network models, Geometry, Energy dissipation, Feedforward control, Shorelines, Testing, Trains
Proceedings Papers

Application of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers

Ehsan Sarshari, Philippe Mullhaupt
Proc. ASME. OMAE2015, Volume 7: Ocean Engineering, V007T06A061, May 31–June 5, 2015
Paper No: OMAE2015-42387
DOI: https://doi.org/10.1115/OMAE2015-42387
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Topics: Artificial neural networks, Bridges (Structures), Equilibrium (Physics), Piers (Structural), Algorithms, Collapse, Design, Flow (Dynamics), Neural network models
Proceedings Papers

AUV Guidance System for Subsurface Ice Intelligence

Francesco Scibilia, Ulrik Jørgensen, Roger Skjetne
Proc. ASME. OMAE2012, Volume 6: Materials Technology; Polar and Arctic Sciences and Technology; Petroleum Technology Symposium, 529-535, July 1–6, 2012
Paper No: OMAE2012-83811
DOI: https://doi.org/10.1115/OMAE2012-83811
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Topics: Ice, Dynamics (Mechanics), Kinematics, Navigation, Neural network models
Proceedings Papers

A Novel Approach to Calibrating Wavemakers and Generating Wavemaker Transfer Functions

Thomas C. Fu, Anne M. Fullerton, James R. Rice, Thomas K. S. Fu, David E. Hess
Proc. ASME. OMAE2007, Volume 4: Materials Technology; Ocean Engineering, 465-471, June 10–15, 2007
Paper No: OMAE2007-29230
DOI: https://doi.org/10.1115/OMAE2007-29230
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Topics: Transfer functions, Waves, Seas, Computers, Feedforward control, Geometry, Hull, Navy, Neural network models, Physics
Proceedings Papers

Hydrodynamic and Control Analysis of a Self-Controllable Underwater Towed Vehicle

Jiaming Wu, Xiaohui Xiong
Proc. ASME. OMAE2006, Volume 2: Ocean Engineering and Polar and Arctic Sciences and Technology, 139-146, June 4–9, 2006
Paper No: OMAE2006-92108
DOI: https://doi.org/10.1115/OMAE2006-92108
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Topics: Vehicles, Trajectories (Physics), Control systems, Artificial neural networks, Computer simulation, Deflection, Genetic algorithms, Neural network models, Wings, Algorithms