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Proceedings Papers

An Optimal Adaptive Bias RBF Controller for Dynamic Positioning Vessels

Yutu Ye, Yiting Wang, Lei Wang
Proc. ASME. OMAE2024, Volume 5B: Ocean Engineering, V05BT06A006, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-125699
https://doi.org/10.1115/OMAE2024-125699
View Paper titled, An Optimal Adaptive Bias RBF Controller for Dynamic Positioning Vessels
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Topics: Control equipment, Dynamic positioning systems, Uncertainty, Vessels, Design, Closed loop systems, Dynamics (Mechanics), Energy consumption, Lyapunov stability, Neural network models
Proceedings Papers

Mooring Line Tensions Prediction Based on MLP for Soft Yoke Mooring System

Ying Li, Qiyuan Zhong, Qiang Gan
Proc. ASME. OMAE2024, Volume 5B: Ocean Engineering, V05BT06A071, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-124829
https://doi.org/10.1115/OMAE2024-124829
View Paper titled, Mooring Line Tensions Prediction Based on MLP for Soft Yoke Mooring System
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Topics: Artificial neural networks, FPSO, Mooring, Water, Seas, Accidents, Fittings, Multilayer perceptron, Neural network models, Numerical analysis
Proceedings Papers

Forecasting Mooring Tension of Semi-Submersible Oil Production and Storage Platform Using a Novel Transformer Neural Network Model

Yang Chen, Lihao Yuan
Proc. ASME. OMAE2024, Volume 1: Offshore Technology, V001T01A001, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-120988
https://doi.org/10.1115/OMAE2024-120988
View Paper titled, Forecasting Mooring Tension of Semi-Submersible Oil Production and Storage Platform Using a Novel Transformer Neural Network Model
Open the PDF for Forecasting Mooring Tension of Semi-Submersible Oil Production and Storage Platform Using a Novel Transformer Neural Network Model in another window
Topics: Artificial neural networks, Mooring, Neural network models, Semi-submersible offshore structures, Storage, Tension, Time series
Proceedings Papers

Optimal Design for the Crashworthiness of the Hull Side Structure Under the Impact of the Raked Bow

Pengyao Yu, Chengyu Zhang, Runbei Zou, Qiang Wang, Boran Zhang
Proc. ASME. OMAE2023, Volume 2: Structures, Safety, and Reliability, V002T02A003, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-102705
https://doi.org/10.1115/OMAE2023-102705
View Paper titled, Optimal Design for the Crashworthiness of the Hull Side Structure Under the Impact of the Raked Bow
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Topics: Artificial neural networks, Collisions (Physics), Crashworthiness, Design, Hull, Optimization, Ships, Damage, Experimental design, Vessels
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
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2020-18325
https://doi.org/10.1115/OMAE2020-18325
View Paper titled, Using Machine Learning to Identify Important Parameters for Flow-Induced Vibration
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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
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2018-78301
https://doi.org/10.1115/OMAE2018-78301
View Paper titled, Real-Time Wellhead Bending Moment Measurement Using Motion Reference Unit (MRU) Sensors and Machine Learning
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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
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2017-62283
https://doi.org/10.1115/OMAE2017-62283
View Paper titled, Prediction of Characteristics of Wave Breaking in Shallow Water Using Neural Network Techniques
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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
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2015-42387
https://doi.org/10.1115/OMAE2015-42387
View Paper titled, Application of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers
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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
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2012-83811
https://doi.org/10.1115/OMAE2012-83811
View Paper titled, AUV Guidance System for Subsurface Ice Intelligence
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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
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2007-29230
https://doi.org/10.1115/OMAE2007-29230
View Paper titled, A Novel Approach to Calibrating Wavemakers and Generating Wavemaker Transfer Functions
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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
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2006-92108
https://doi.org/10.1115/OMAE2006-92108
View Paper titled, Hydrodynamic and Control Analysis of a Self-Controllable Underwater Towed Vehicle
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Topics: Vehicles, Trajectories (Physics), Control systems, Artificial neural networks, Computer simulation, Deflection, Genetic algorithms, Neural network models, Wings, Algorithms