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Proceedings Papers

Deterministic Sea Wave Prediction Based on Radar Images Via End-To-End Deep Learning Methods

Yaokun Zheng, Zhiliang Lin
Proc. ASME. OMAE2024, Volume 5B: Ocean Engineering, V05BT06A065, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-123221
https://doi.org/10.1115/OMAE2024-123221
View Paper titled, Deterministic Sea Wave Prediction Based on Radar Images Via End-To-End Deep Learning Methods
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Topics: Deep learning, Ocean waves, Radar
Proceedings Papers

A Deep Learning Method for UUV to Detect Undersea Landform Features

Yao Xiao, Shuang Huang, Hongjian Wang, Dan Yu, Ling Chu, Yutong Huang, Jinmu Tian
Proc. ASME. OMAE2024, Volume 5B: Ocean Engineering, V05BT06A082, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-129780
https://doi.org/10.1115/OMAE2024-129780
View Paper titled, A Deep Learning Method for UUV to Detect Undersea Landform Features
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Topics: Deep learning, Feature extraction, Unmanned underwater vehicles
Proceedings Papers

An Attention-Based Deep Learning Model for Phase-Resolved Wave Prediction

Jialun Chen, David Gunawan, Wenhua Zhao, Paul H. Taylor, Yunzhuo Chen, Ian A. Milne
Proc. ASME. OMAE2024, Volume 5B: Ocean Engineering, V05BT06A078, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-127605
https://doi.org/10.1115/OMAE2024-127605
View Paper titled, An Attention-Based Deep Learning Model for Phase-Resolved Wave Prediction
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Topics: Deep learning, Waves
Proceedings Papers

Physics-Informed Neural Networks for Flow-Induced Vibration Prediction of a Subsea Pipeline

Guang Yin, Marek Jan Janocha, Muk Chen Ong
Proc. ASME. OMAE2024, Volume 5B: Ocean Engineering, V05BT06A080, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-128147
https://doi.org/10.1115/OMAE2024-128147
View Paper titled, Physics-Informed Neural Networks for Flow-Induced Vibration Prediction of a Subsea Pipeline
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Topics: Artificial neural networks, Deep learning, Flow-induced vibrations, Physics, Underwater pipelines
Proceedings Papers

Predicting Wave Propagation for Varying Bathymetry Using Conditional Convolutional Autoencoder Network

Indu Kant Deo, Akash Vankateshwaran, Rajeev Jaiman
Proc. ASME. OMAE2024, Volume 6: Polar and Arctic Sciences and Technology; CFD, FSI, and AI, V006T08A038, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-128032
https://doi.org/10.1115/OMAE2024-128032
View Paper titled, Predicting Wave Propagation for Varying Bathymetry Using Conditional Convolutional Autoencoder Network
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Topics: Wave propagation, Noise (Sound), Architecture, Artificial neural networks, Convolutional neural networks, Deep learning, Engineering simulation, Marine engineering, Navier-Stokes equations, Oceans
Proceedings Papers

Machine Learning and Deep Learning for Enhanced Spatio-Temporal Wave Parameters Prediction

Tian Tan, Vengatesan Venugopal
Proc. ASME. OMAE2024, Volume 6: Polar and Arctic Sciences and Technology; CFD, FSI, and AI, V006T08A043, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-127930
https://doi.org/10.1115/OMAE2024-127930
View Paper titled, Machine Learning and Deep Learning for Enhanced Spatio-Temporal Wave Parameters Prediction
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Topics: Deep learning, Machine learning, Waves
Proceedings Papers

A Hybrid Deep Learning Approach to Predict Dynamic Mooring Tension of a Wave Energy Converter

Sheng Xu, Shan Wang, Carlos Guedes Soares
Proc. ASME. OMAE2024, Volume 7: Ocean Renewable Energy, V007T09A078, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-129671
https://doi.org/10.1115/OMAE2024-129671
View Paper titled, A Hybrid Deep Learning Approach to Predict Dynamic Mooring Tension of a Wave Energy Converter
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Topics: Convolutional neural networks, Deep learning, Mooring, Tension, Wave energy converters
Proceedings Papers

Monte Carlo Dropout Based Active Learning for Deep Learning in Structural Simulation

Chunhao Jiang, Nian-Zhong Chen, Zhimin Zhao
Proc. ASME. OMAE2024, Volume 2: Structures, Safety, and Reliability, V002T02A023, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-126087
https://doi.org/10.1115/OMAE2024-126087
View Paper titled, Monte Carlo Dropout Based Active Learning for Deep Learning in Structural Simulation
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Topics: Deep learning, Simulation
Proceedings Papers

Causal Convolution LengthNet Model for Slender Marine Structures

Vinicius Machado, Luis Sagrilo
Proc. ASME. OMAE2024, Volume 3: Materials Technology; Subsea Technology, V003T04A016, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-128830
https://doi.org/10.1115/OMAE2024-128830
View Paper titled, Causal Convolution LengthNet Model for Slender Marine Structures
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Topics: Convolutional neural networks, Deep learning, Marine structures, Waves, Artificial neural networks, Fatigue, Structural response analysis, Algorithms, Approximation, Cutting
Proceedings Papers

Extraction of Ship Collision Avoidance Patterns From AIS Data

Akash Vijayakumar, Amar Nath Singh, Abhilash Somayajula
Proc. ASME. OMAE2024, Volume 5A: Ocean Engineering, V05AT06A069, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-126767
https://doi.org/10.1115/OMAE2024-126767
View Paper titled, Extraction of Ship Collision Avoidance Patterns From AIS Data
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Topics: Collisions (Physics), Deep learning, Ships
Proceedings Papers

Intelligent Identification Workflow of Drilling Conditions Combining Deep Learning and Drilling Knowledge

Zihao Liu, Xianzhi Song, Shanlin Ye, Baodong Ma, Chengkai Zhang, Zheng Wang, Xuezhe Yao, Zhaopeng Zhu, Yifan Wang
Proc. ASME. OMAE2024, Volume 8: Offshore Geotechnics; Petroleum Technology, V008T11A024, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-127683
https://doi.org/10.1115/OMAE2024-127683
View Paper titled, Intelligent Identification Workflow of Drilling Conditions Combining Deep Learning and Drilling Knowledge
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Topics: Deep learning, Drilling, Workflow
Proceedings Papers

AI-Based Surrogate Model for the Prediction of Ship Fuel Consumption Reflecting Hydrometeorological Conditions

Mingyang Zhang, Nikolaos Tsoulakos, Pentti Kujala, Spyros Hirdaris
Proc. ASME. OMAE2024, Volume 9: Philip Liu Honoring Symposium on Water Wave Mechanics and Hydrodynamics; Blue Economy Symposium, V009T13A016, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-121992
https://doi.org/10.1115/OMAE2024-121992
View Paper titled, AI-Based Surrogate Model for the Prediction of Ship Fuel Consumption Reflecting Hydrometeorological Conditions
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Topics: Artificial intelligence, Deep learning, Fuel consumption, Ships