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Proceedings Papers

Maximum Cumulative Fatigue Damage Assessment of Bottom-Fixed Wind Turbine Structures in the Design Phase by AK-MDAmax Approach

Chao Ren, Yihan Xing
Proc. ASME. OMAE2024, Volume 5B: Ocean Engineering, V05BT06A063, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-121006
https://doi.org/10.1115/OMAE2024-121006
View Paper titled, Maximum Cumulative Fatigue Damage Assessment of Bottom-Fixed Wind Turbine Structures in the Design Phase by AK-MDAmax Approach
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Topics: Design, Fatigue damage, Machine learning, Wind turbines
Proceedings Papers

Sensitivity of Ship Voyage Optimizations to Various Energy Cost Models

Yuhan Chen, Wengang Mao
Proc. ASME. OMAE2024, Volume 5A: Ocean Engineering, V05AT06A050, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-127986
https://doi.org/10.1115/OMAE2024-127986
View Paper titled, Sensitivity of Ship Voyage Optimizations to Various Energy Cost Models
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Topics: Energy efficiency, Machine learning, Optimization, Ships
Proceedings Papers

Application of Data Analytics and Machine Learning in Analysing Result of Numerical Ship Manoeuvring Study

Vahid Hassani, Andrew Ross, Leiv Aspelund
Proc. ASME. OMAE2024, Volume 5B: Ocean Engineering, V05BT06A079, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-127763
https://doi.org/10.1115/OMAE2024-127763
View Paper titled, Application of Data Analytics and Machine Learning in Analysing Result of Numerical Ship Manoeuvring Study
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Topics: Machine learning, Ships, Simulation, Computer simulation, Hydrodynamics, Modeling, Tunnels, Water, Collisions (Physics), Dynamics (Mechanics)
Proceedings Papers

Data-Driven Generation of Tailored Wave Sequences

Marco Klein, Mathies Wedler, Marc-André Pick, Robert Seifried, Svenja Ehlers, Merten Stender, Norbert Hoffmann
Proc. ASME. OMAE2024, Volume 5A: Ocean Engineering, V05AT06A080, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-129690
https://doi.org/10.1115/OMAE2024-129690
View Paper titled, Data-Driven Generation of Tailored Wave Sequences
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Topics: Convolutional neural networks, Machine learning, Waves
Proceedings Papers

Bucking Reliability Assessment of Offshore Fluid Tanks Subjected to External Pressure With Active Learning Kriging Approaches

Chao Ren, Frank Eriksen, Stian Gundersen, Yihan Xing
Proc. ASME. OMAE2024, Volume 5B: Ocean Engineering, V05BT06A066, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-123234
https://doi.org/10.1115/OMAE2024-123234
View Paper titled, Bucking Reliability Assessment of Offshore Fluid Tanks Subjected to External Pressure With Active Learning Kriging Approaches
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Topics: Buckling, External pressure, Fluids, Machine learning, Ocean engineering, Reliability, Simulation
Proceedings Papers

Wave Dynamics Run-Up Modelling: Machine Learning Approach

Vinicius L. Vileti, Svein Ersdal
Proc. ASME. OMAE2024, Volume 5B: Ocean Engineering, V05BT06A072, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-124850
https://doi.org/10.1115/OMAE2024-124850
View Paper titled, Wave Dynamics Run-Up Modelling: Machine Learning Approach
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Topics: Dynamics (Mechanics), Machine learning, Modeling, Waves
Proceedings Papers

IBNet: An Iceberg Semantic Segmentation Network Integrating Multiple Scales and Attention

Fenglei Han, Wangyuan Zhao, Yanzhuo Xue, Yuliang Wu, Xiao Peng, Jiawei Zhang
Proc. ASME. OMAE2024, Volume 6: Polar and Arctic Sciences and Technology; CFD, FSI, and AI, V006T07A012, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-123922
https://doi.org/10.1115/OMAE2024-123922
View Paper titled, IBNet: An Iceberg Semantic Segmentation Network Integrating Multiple Scales and Attention
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Topics: Ice, Icebergs, Image segmentation, Semantics, Ships, Polar regions, Navigation, Algorithms, Arctic engineering, Arctic region
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

Prediction of Global Whipping Responses on a Large Cruise Ship Under Unknown Sea States Using an LSTM Based Encoder-Decoder Model

Ruixiang Liu, Hui Li, Muk Chen Ong, Jian Zou
Proc. ASME. OMAE2024, Volume 6: Polar and Arctic Sciences and Technology; CFD, FSI, and AI, V006T08A040, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-126186
https://doi.org/10.1115/OMAE2024-126186
View Paper titled, Prediction of Global Whipping Responses on a Large Cruise Ship Under Unknown Sea States Using an LSTM Based Encoder-Decoder Model
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Topics: Seas, Ships, Safety, Computer simulation, Girders, Hull, Machine learning, Stress, Time series, Vibration
Proceedings Papers

Enhancing Offshore Wind Farm Met-Ocean Data Accessibility: A Machine Learning Approach With Satellite-Derived Wave Measurements in the Celtic Sea

Sophie Whistler, Ian G. C. Ashton, Ajit C. Pillai, Graham D. Quartly
Proc. ASME. OMAE2024, Volume 7: Ocean Renewable Energy, V007T09A042, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-127479
https://doi.org/10.1115/OMAE2024-127479
View Paper titled, Enhancing Offshore Wind Farm Met-Ocean Data Accessibility: A Machine Learning Approach With Satellite-Derived Wave Measurements in the Celtic Sea
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Topics: Machine learning, Ocean engineering, Oceans, Satellites, Seas, Waves, Wind farms
Proceedings Papers

Spectral Graph-Based Networks for Mooring Line Failure Detection on FPSO

José Lucas De Melo Costa, Fernando Kurike Matsumoto, Caio F. Deberaldini Netto, Marcel R. de Barros, Asdrubal do Nascimento Queiroz Filho, Edson S. Gomi, Eduardo A. Tannuri, Anna Helena Reali Costa
Proc. ASME. OMAE2024, Volume 1: Offshore Technology, V001T01A051, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-136899
https://doi.org/10.1115/OMAE2024-136899
View Paper titled, Spectral Graph-Based Networks for Mooring Line Failure Detection on FPSO
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Topics: Failure, FPSO, Graph neural networks, Machine learning, Mooring
Proceedings Papers

A Neural Operator for the Estimation of Wave Heights From Wind Data

Marlon S. Mathias, Carlos H. S. Thiersch, Luca R. Patitucci, Marcel R. de Barros, Felipe M. Moreno, Artur Jordão, Marcelo Dottori, Anna H. R. Costa, Edson S. Gomi, Eduardo A. Tannuri
Proc. ASME. OMAE2024, Volume 1: Offshore Technology, V001T01A049, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-136409
https://doi.org/10.1115/OMAE2024-136409
View Paper titled, A Neural Operator for the Estimation of Wave Heights From Wind Data
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Topics: Machine learning, Waves, Wind
Proceedings Papers

An Indirect Measurement Methodology to Identify Fatigue Damage in The Structure of a 2800TEU Container Ship

Xiao Lang, Mingyang Zhang, Da Wu, Wengang Mao
Proc. ASME. OMAE2024, Volume 2: Structures, Safety, and Reliability, V002T02A025, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-126797
https://doi.org/10.1115/OMAE2024-126797
View Paper titled, An Indirect Measurement Methodology to Identify Fatigue Damage in The Structure of a 2800TEU Container Ship
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Topics: Container ships, Fatigue, Fatigue damage, Ships, Vessels, Maintenance, Sensors, Ambiguity, Atlantic Ocean, Containers
Proceedings Papers

Recognition of Surface Corrosion Morphology on Coastal Engineering Structures Using Machine Vision Technology

Qifeng Yu, Yudong Han
Proc. ASME. OMAE2024, Volume 3: Materials Technology; Subsea Technology, V003T03A012, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-127951
https://doi.org/10.1115/OMAE2024-127951
View Paper titled, Recognition of Surface Corrosion Morphology on Coastal Engineering Structures Using Machine Vision Technology
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Topics: Corrosion, Harbor and coastal engineering, Machine learning, Machine vision
Proceedings Papers

Machine Learning Based Prediction of Buckling in Composite Cylindrical Shells Under External Pressure Using Design Code and FEM Analysis

Hyung Gi Lee, Sang Jin Kim, Jung Min Sohn, Aditya Rio Prabowo
Proc. ASME. OMAE2024, Volume 2: Structures, Safety, and Reliability, V002T02A092, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-127261
https://doi.org/10.1115/OMAE2024-127261
View Paper titled, Machine Learning Based Prediction of Buckling in Composite Cylindrical Shells Under External Pressure Using Design Code and FEM Analysis
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Topics: Buckling, Composite materials, Design, External pressure, Finite element methods, Finite element model, Machine learning, Pipes, Pressure
Proceedings Papers

Development of Time Series Drilling Datasets for Stuck Pipe Prediction Using Volve Field Data

Cao Bo, Song Yu, Xu Feng, Peng Fukang
Proc. ASME. OMAE2024, Volume 8: Offshore Geotechnics; Petroleum Technology, V008T11A027, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-128386
https://doi.org/10.1115/OMAE2024-128386
View Paper titled, Development of Time Series Drilling Datasets for Stuck Pipe Prediction Using Volve Field Data
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Topics: Drilling, Machine learning, Pipes, Time series
Proceedings Papers

Unsupervised Machine Learning for Well Log Depth Alignment

Sushil Acharya, Karl Fabian
Proc. ASME. OMAE2024, Volume 8: Offshore Geotechnics; Petroleum Technology, V008T11A019, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-123362
https://doi.org/10.1115/OMAE2024-123362
View Paper titled, Unsupervised Machine Learning for Well Log Depth Alignment
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Topics: Machine learning, Dimensions, Interpolation, Optical quality control, Signals
Proceedings Papers

Early Lost Circulation Monitoring Using a Hybrid CNN-LSTM Model

Liwei Wu, Liang Han, Gensheng Li, Xianzhi Song, Qilong Zhang, Rui Zhang, Ziyue Zhang
Proc. ASME. OMAE2024, Volume 8: Offshore Geotechnics; Petroleum Technology, V008T11A026, June 9–14, 2024
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2024-128345
https://doi.org/10.1115/OMAE2024-128345
View Paper titled, Early Lost Circulation Monitoring Using a Hybrid CNN-LSTM Model
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Topics: Convolutional neural networks, Machine learning
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