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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
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Topics: Artificial neural networks, Mooring, Neural network models, Semi-submersible offshore structures, Storage, Tension, Time series
Proceedings Papers

An Approach for Obtaining Dynamic Responses of Slender Marine Structures Assisted by Generalized Artificial Neural Networks

Thiago Rodrigues, Thiago Ângelo Gonçalves de Lacerda, Gabriel Mattos Gonzalez, Luis Volnei Sudati Sagrilo, George Carneiro Campello, Rodolfo Figueira De Souza, Tiago Brun Coser
Proc. ASME. OMAE2024, Volume 2: Structures, Safety, and Reliability, V002T02A024, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-126626
https://doi.org/10.1115/OMAE2024-126626
View Paper titled, An Approach for Obtaining Dynamic Responses of Slender Marine Structures Assisted by Generalized Artificial Neural Networks
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Topics: Artificial neural networks, Dynamic response, Flexible risers, Marine structures
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

Physics-Informed Neural Networks for Modeling Linear Waves

Mohammad Sheikholeslami, Saeed Salehi, Wengang Mao, Arash Eslamdoost, Håkan Nilsson
Proc. ASME. OMAE2024, Volume 9: Philip Liu Honoring Symposium on Water Wave Mechanics and Hydrodynamics; Blue Economy Symposium, V009T12A002, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-125048
https://doi.org/10.1115/OMAE2024-125048
View Paper titled, Physics-Informed Neural Networks for Modeling Linear Waves
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Topics: Artificial neural networks, Modeling, Physics, Water waves, Waves
Proceedings Papers

Real-Time Recognition of Drill Bit Wear State Using Clustering Algorithm and GRU Neural Network

Qilong Zhang, Zeyu Zheng, Zhongwei Huang, Xianzhi Song, Liwei Wu, Ziyue Zhang
Proc. ASME. OMAE2024, Volume 8: Offshore Geotechnics; Petroleum Technology, V008T11A025, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-127871
https://doi.org/10.1115/OMAE2024-127871
View Paper titled, Real-Time Recognition of Drill Bit Wear State Using Clustering Algorithm and GRU Neural Network
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Topics: Algorithms, Artificial neural networks, Bits (Tools), Wear
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

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

Directional Wave Spectrum Estimation Using Ship Motion Data by Improved CGAN With Physics Guided

Xin Li, Ning Ma, Qiqi Shi, Xiechong Gu
Proc. ASME. OMAE2024, Volume 5B: Ocean Engineering, V05BT06A073, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-124857
https://doi.org/10.1115/OMAE2024-124857
View Paper titled, Directional Wave Spectrum Estimation Using Ship Motion Data by Improved CGAN With Physics Guided
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Topics: Artificial neural networks, Physics, Ships, Spectra (Spectroscopy), Waves
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

Research on a Fast Resistance Performance Prediction Method for SUBOFF Model Based on Physics-Informed Neural Networks (PINNs)

Yuliang Wu, Fenglei Han, Xiao Peng, Liangtian Gao, Yiming Zhao, Jiawei Zhang
Proc. ASME. OMAE2024, Volume 6: Polar and Arctic Sciences and Technology; CFD, FSI, and AI, V006T08A041, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-126300
https://doi.org/10.1115/OMAE2024-126300
View Paper titled, Research on a Fast Resistance Performance Prediction Method for SUBOFF Model Based on Physics-Informed Neural Networks (PINNs)
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Topics: Artificial neural networks, Physics, Reynolds-averaged Navier–Stokes equations, Computational fluid dynamics, Hull, Algorithms, Flow (Dynamics), Pressure, Ships, Errors
Proceedings Papers

Static Obstacle Avoidance With Path Following of ASVs by Using Twin Delayed Deep Deterministic Policy Gradients

Vishnu Thirumurugan, Suresh Rajendran, Abhilash Sharma Somayajula
Proc. ASME. OMAE2024, Volume 6: Polar and Arctic Sciences and Technology; CFD, FSI, and AI, V006T08A039, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-122872
https://doi.org/10.1115/OMAE2024-122872
View Paper titled, Static Obstacle Avoidance With Path Following of ASVs by Using Twin Delayed Deep Deterministic Policy Gradients
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Topics: Collisions (Physics), Deep reinforcement learning, Unmanned surface vehicles, Errors, Safety, Algorithms, Navigation, Artificial neural networks, Collision avoidance systems, Deflection
Proceedings Papers

Relative Motion Prediction for Integrated Floating Offshore Wind Turbine Installation Scheme Based on Data-Driven

Can Ma, Taiyu Zhang, Zongyuan Yang, Yang An, Xiang Yuan Zheng, Zhengru Ren
Proc. ASME. OMAE2024, Volume 7: Ocean Renewable Energy, V007T09A017, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-124558
https://doi.org/10.1115/OMAE2024-124558
View Paper titled, Relative Motion Prediction for Integrated Floating Offshore Wind Turbine Installation Scheme Based on Data-Driven
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Topics: Artificial neural networks, Floating wind turbines
Proceedings Papers

A Novel Hybrid Transfer Learning Method for Bottom Hole Pressure Prediction

Rui Zhang, Xianzhi Song, Gensheng Li, Zehao Lv, Zhaopeng Zhu, Chengkai Zhang, Chenxing Gong
Proc. ASME. OMAE2023, Volume 9: Offshore Geotechnics; Petroleum Technology, V009T11A048, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-103302
https://doi.org/10.1115/OMAE2023-103302
View Paper titled, A Novel Hybrid Transfer Learning Method for Bottom Hole Pressure Prediction
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Topics: Artificial neural networks, Pressure
Proceedings Papers

A Neural Network Approach to Minimize Line Forces in the Survivability of the Point-Absorber Wave Energy Converters

Zahra Shahroozi, Malin Göteman, Jens Engström
Proc. ASME. OMAE2023, Volume 8: Ocean Renewable Energy, V008T09A065, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-102422
https://doi.org/10.1115/OMAE2023-102422
View Paper titled, A Neural Network Approach to Minimize Line Forces in the Survivability of the Point-Absorber Wave Energy Converters
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Topics: Artificial neural networks, Damping, Wave energy converters, Waves
Proceedings Papers

Cross-Domain Knowledge Discovery and Sharing in Digital Subsurface Based on Federated Learning

Nan Zhang, Ye Liu, Jiahui Geng, Boyu Cui, Jungwon Seo, Jie Cao, Chunming Rong
Proc. ASME. OMAE2023, Volume 9: Offshore Geotechnics; Petroleum Technology, V009T11A010, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-104639
https://doi.org/10.1115/OMAE2023-104639
View Paper titled, Cross-Domain Knowledge Discovery and Sharing in Digital Subsurface Based on Federated Learning
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Topics: Algorithms, Artificial neural networks, Drilling, Energy industry, Innovation, Machine learning, Reservoirs, Trains
Proceedings Papers

A Hybrid Approach to Detect Bad Hole Cleaning

Mandar V. Tabib, Philippe Nivlet, Jan Ole Skogestad, Roar Nybø, Adil Rasheed
Proc. ASME. OMAE2023, Volume 9: Offshore Geotechnics; Petroleum Technology, V009T11A009, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-108151
https://doi.org/10.1115/OMAE2023-108151
View Paper titled, A Hybrid Approach to Detect Bad Hole Cleaning
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Topics: Annulus, Artificial neural networks, Decision making, Dimensions, Drill strings, Drilling, Machine learning, Manufacturing, Modeling, Multiphase flow
Proceedings Papers

A Preliminary Study of Learning a Wave Energy Converter System Using Physics-Informed Neural Network Method

Bo-Chen Chen, Yi-Hsiang Yu
Proc. ASME. OMAE2023, Volume 8: Ocean Renewable Energy, V008T09A077, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-105123
https://doi.org/10.1115/OMAE2023-105123
View Paper titled, A Preliminary Study of Learning a Wave Energy Converter System Using Physics-Informed Neural Network Method
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Topics: Artificial neural networks, Physics, Simulation, Wave energy converters
Proceedings Papers

Well Integrity Operations Experience Transfer Using Long Short-Term Memory (LSTM) Recurrent Neural Network

David Semwogerere, Alexey Pavlov, Sigbjørn Sangesland
Proc. ASME. OMAE2023, Volume 9: Offshore Geotechnics; Petroleum Technology, V009T11A018, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-108149
https://doi.org/10.1115/OMAE2023-108149
View Paper titled, Well Integrity Operations Experience Transfer Using Long Short-Term Memory (LSTM) Recurrent Neural Network
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Topics: Artificial neural networks, Text analytics
Proceedings Papers

Short-Term Sea Surface Elevation Prediction Using Deep Learning Methods

Tak Yin Pang, Boyin Ding, Lingqiao Liu, Nataliia Sergiienko
Proc. ASME. OMAE2023, Volume 5: Ocean Engineering, V005T06A070, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-104377
https://doi.org/10.1115/OMAE2023-104377
View Paper titled, Short-Term Sea Surface Elevation Prediction Using Deep Learning Methods
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Topics: Seas, Time series, Waves, Artificial neural networks, Ocean waves, Oceans, Linear wave theory, Navigation, Noise (Sound), Ocean engineering
Proceedings Papers

Trustworthiness Evaluation Framework for Digital Ship Navigators in Bridge Simulator Environments

Hosna Namazi, Lokukaluge Prasad Perera
Proc. ASME. OMAE2023, Volume 5: Ocean Engineering, V005T06A037, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-104863
https://doi.org/10.1115/OMAE2023-104863
View Paper titled, Trustworthiness Evaluation Framework for Digital Ship Navigators in Bridge Simulator Environments
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Topics: Algorithms, Artificial intelligence, Artificial neural networks, Bridges (Structures), Computer software, Hardware, Machine learning, Reliability, Resilience, Safety