Page 1 | Search Results | J. Offshore Mech. Arct. Eng.

1-4 of 4

Sort by

Sort Order Select

Journal Articles

Investigating the Impact of System Parameters on Flow-Induced Vibration Hard Galloping Based on Deep Neural Network

Dahai Zhang, Weijie Li, Shuai Zhang, Zhang Bai

Publisher: ASME

J. Offshore Mech. Arct. Eng. August 2025, 147(4): 041901.

Paper No: OMAE-24-1057

https://doi.org/10.1115/1.4066755

Published Online: October 23, 2024

View Article titled, Investigating the Impact of System Parameters on Flow-Induced Vibration Hard Galloping Based on Deep Neural Network

Topics: Artificial neural networks, Cylinders, Damping, Deep neural networks, Flow-induced vibrations, Vibration, Regression models, Machine learning, Vortex-induced vibration, Flow (Dynamics)

Journal Articles

Prediction of Wave Spectral Parameters Using Multiple-Output Regression Models to Support the Execution of Marine Operations

Jonathan Prócel, Marco Guamán Alarcón, Wilson Guachamin-Acero

Publisher: ASME

J. Offshore Mech. Arct. Eng. June 2024, 146(3): 031204.

Paper No: OMAE-23-1028

https://doi.org/10.1115/1.4063938

Published Online: November 22, 2023

View Article titled, Prediction of Wave Spectral Parameters Using Multiple-Output Regression Models to Support the Execution of Marine Operations

Topics: Algorithms, Regression models, Vessels, Waves, Machine learning, Statistics

Journal Articles

Support Vector Machines Model of the Nonlinear Hydrodynamics of Fixed Cylinders

Yu Ma, Paul D. Sclavounos

Publisher: ASME

J. Offshore Mech. Arct. Eng. October 2021, 143(5): 051701.

Paper No: OMAE-20-1106

https://doi.org/10.1115/1.4049731

Published Online: February 12, 2021

View Article titled, Support Vector Machines Model of the Nonlinear Hydrodynamics of Fixed Cylinders

Topics: Stress, Support vector machines, Waves, Algorithms, Regression models, Cylinders, Seas, Nonlinear waves

Journal Articles

Experimental Validation of the Adaptive Gaussian Process Regression Model Used for Prediction of Stress Intensity Factor as an Alternative to Finite Element Method

Arvind Keprate, R. M. Chandima Ratnayake, Shankar Sankararaman

Publisher: ASME

J. Offshore Mech. Arct. Eng. April 2019, 141(2): 021606.

Paper No: OMAE-17-1120

https://doi.org/10.1115/1.4041457

Published Online: October 18, 2018

View Article titled, Experimental Validation of the Adaptive Gaussian Process Regression Model Used for Prediction of Stress Intensity Factor as an Alternative to Finite Element Method

Topics: Finite element methods, Finite element model, Fracture (Materials), Regression models, Stress

Journal citation

NARROW

Investigating the Impact of System Parameters on Flow-Induced Vibration Hard Galloping Based on Deep Neural Network

Prediction of Wave Spectral Parameters Using Multiple-Output Regression Models to Support the Execution of Marine Operations

Support Vector Machines Model of the Nonlinear Hydrodynamics of Fixed Cylinders

Experimental Validation of the Adaptive Gaussian Process Regression Model Used for Prediction of Stress Intensity Factor as an Alternative to Finite Element Method

ASME Journals

ASME Conference Proceedings

ASME eBooks

Resources

Opportunities

Investigating the Impact of System Parameters on Flow-Induced Vibration Hard Galloping Based on Deep Neural Network

Prediction of Wave Spectral Parameters Using Multiple-Output Regression Models to Support the Execution of Marine Operations

Support Vector Machines Model of the Nonlinear Hydrodynamics of Fixed Cylinders

Experimental Validation of the Adaptive Gaussian Process Regression Model Used for Prediction of Stress Intensity Factor as an Alternative to Finite Element Method

ASME Journals

ASME Conference Proceedings

ASME eBooks

Resources

Opportunities

This Feature Is Available To Subscribers Only