1-20 of 118
Machine learning
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

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

Available to Purchase

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
Open the PDF for Maximum Cumulative Fatigue Damage Assessment of Bottom-Fixed Wind Turbine Structures in the Design Phase by AK-MDAmax Approach in another window
Topics: Design, Fatigue damage, Machine learning, Wind turbines
Proceedings Papers

Sensitivity of Ship Voyage Optimizations to Various Energy Cost Models

Available to Purchase

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
Open the PDF for Sensitivity of Ship Voyage Optimizations to Various Energy Cost Models in another window
Topics: Energy efficiency, Machine learning, Optimization, Ships
Proceedings Papers

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

Available to Purchase

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
Open the PDF for Application of Data Analytics and Machine Learning in Analysing Result of Numerical Ship Manoeuvring Study in another window
Topics: Machine learning, Ships, Simulation, Computer simulation, Hydrodynamics, Modeling, Tunnels, Water, Collisions (Physics), Dynamics (Mechanics)
Proceedings Papers

Data-Driven Generation of Tailored Wave Sequences

Available to Purchase

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
Open the PDF for Data-Driven Generation of Tailored Wave Sequences in another window
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

Available to Purchase

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
Open the PDF for Bucking Reliability Assessment of Offshore Fluid Tanks Subjected to External Pressure With Active Learning Kriging Approaches in another window
Topics: Buckling, External pressure, Fluids, Machine learning, Ocean engineering, Reliability, Simulation
Proceedings Papers

Wave Dynamics Run-Up Modelling: Machine Learning Approach

Available to Purchase

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
Open the PDF for Wave Dynamics Run-Up Modelling: Machine Learning Approach in another window
Topics: Dynamics (Mechanics), Machine learning, Modeling, Waves
Proceedings Papers

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

Available to Purchase

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
Open the PDF for IBNet: An Iceberg Semantic Segmentation Network Integrating Multiple Scales and Attention in another window
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

Available to Purchase

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
Open the PDF for Machine Learning and Deep Learning for Enhanced Spatio-Temporal Wave Parameters Prediction in another window
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

Available to Purchase

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
Open the PDF for Prediction of Global Whipping Responses on a Large Cruise Ship Under Unknown Sea States Using an LSTM Based Encoder-Decoder Model in another window
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

Available to Purchase

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
Open the PDF for Enhancing Offshore Wind Farm Met-Ocean Data Accessibility: A Machine Learning Approach With Satellite-Derived Wave Measurements in the Celtic Sea in another window
Topics: Machine learning, Ocean engineering, Oceans, Satellites, Seas, Waves, Wind farms
Proceedings Papers

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

Available to Purchase

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
Open the PDF for Spectral Graph-Based Networks for Mooring Line Failure Detection on FPSO in another window
Topics: Failure, FPSO, Graph neural networks, Machine learning, Mooring
Proceedings Papers

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

Available to Purchase

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
Open the PDF for A Neural Operator for the Estimation of Wave Heights From Wind Data in another window
Topics: Machine learning, Waves, Wind
Proceedings Papers

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

Available to Purchase

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
Open the PDF for An Indirect Measurement Methodology to Identify Fatigue Damage in The Structure of a 2800TEU Container Ship in another window
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

Available to Purchase

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
Open the PDF for Recognition of Surface Corrosion Morphology on Coastal Engineering Structures Using Machine Vision Technology in another window
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

Available to Purchase

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
Open the PDF for Machine Learning Based Prediction of Buckling in Composite Cylindrical Shells Under External Pressure Using Design Code and FEM Analysis in another window
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

Available to Purchase

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
Open the PDF for Development of Time Series Drilling Datasets for Stuck Pipe Prediction Using Volve Field Data in another window
Topics: Drilling, Machine learning, Pipes, Time series
Proceedings Papers

Unsupervised Machine Learning for Well Log Depth Alignment

Available to Purchase

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
Open the PDF for Unsupervised Machine Learning for Well Log Depth Alignment in another window
Topics: Machine learning, Dimensions, Interpolation, Optical quality control, Signals
Proceedings Papers

Early Lost Circulation Monitoring Using a Hybrid CNN-LSTM Model

Available to Purchase

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
Open the PDF for Early Lost Circulation Monitoring Using a Hybrid CNN-LSTM Model in another window
Topics: Convolutional neural networks, Machine learning
Proceedings Papers

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

Available to Purchase

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
Open the PDF for Cross-Domain Knowledge Discovery and Sharing in Digital Subsurface Based on Federated Learning in another window
Topics: Algorithms, Artificial neural networks, Drilling, Energy industry, Innovation, Machine learning, Reservoirs, Trains
Proceedings Papers

A Hybrid Approach to Detect Bad Hole Cleaning

Available to Purchase

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
Open the PDF for A Hybrid Approach to Detect Bad Hole Cleaning in another window
Topics: Annulus, Artificial neural networks, Decision making, Dimensions, Drill strings, Drilling, Machine learning, Manufacturing, Modeling, Multiphase flow