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Proc. ASME. OMAE2022, Volume 5B: Ocean Engineering; Honoring Symposium for Professor Günther F. Clauss on Hydrodynamics and Ocean Engineering, V05BT06A001, June 5–10, 2022

Paper No: OMAE2022-78603

DOI: https://doi.org/10.1115/OMAE2022-78603

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Topics: Artificial neural networks, Excitation, FPSO, Mooring, Seas, State estimation, Waves

Proceedings Papers

Application of Machine Learning for the Generation of Tailored Wave Sequences

Marco Klein, Merten Stender, Mathies Wedler, Svenja Ehlers, Moritz Hartmann, Nicolas Desmars, Marc-André Pick, Robert Seifried, Norbert Hoffmann

Proc. ASME. OMAE2022, Volume 5B: Ocean Engineering; Honoring Symposium for Professor Günther F. Clauss on Hydrodynamics and Ocean Engineering, V05BT12A015, June 5–10, 2022

Paper No: OMAE2022-78601

DOI: https://doi.org/10.1115/OMAE2022-78601

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Topics: Machine learning, Waves, Artificial neural networks, Modeling, Nonlinear waves, Seas, Spectra (Spectroscopy), Time series

Proceedings Papers

Artificial Neural Network Surrogate Modeling for Offshore Wind Turbines Under Multi-Hazards

Xiaowei Wang, YeongAe Heo

Proc. ASME. OMAE2022, Volume 2: Structures, Safety, and Reliability, V002T02A019, June 5–10, 2022

Paper No: OMAE2022-81048

DOI: https://doi.org/10.1115/OMAE2022-81048

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Topics: Artificial neural networks, Hazards, Metamodeling, Offshore wind turbines

Proceedings Papers

A Study on the Error Characteristics and Compensation Algorithm for Strain Gauge in Ship Structure Monitoring Systems

Xueqian Zhou, Yu Yang, Yishi Xu, Chenfeng Li, Huilong Ren

Proc. ASME. OMAE2022, Volume 2: Structures, Safety, and Reliability, V002T02A022, June 5–10, 2022

Paper No: OMAE2022-81437

DOI: https://doi.org/10.1115/OMAE2022-81437

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Topics: Algorithms, Artificial neural networks, Errors, Monitoring systems, Ships, Strain gages

Proceedings Papers

Rate of Penetration Prediction Using Quantile Regression Deep Neural Networks

Adrian Ambrus, Sergey Alyaev, Nazanin Jahani, Felix James Pacis, Tomasz Wiktorski

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A010, June 5–10, 2022

Paper No: OMAE2022-79046

DOI: https://doi.org/10.1115/OMAE2022-79046

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Topics: Artificial neural networks, Drilling, Machine learning, Modeling, Optimization, Uncertainty, Architecture, Artificial intelligence, Climate, Decision making

Proceedings Papers

Global Optimization Workflow for Offshore Drilling Rate of Penetration With Dynamic Drilling Log Data

Jie Cao, Hui Ren, Dan Sui

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A013, June 5–10, 2022

Paper No: OMAE2022-79747

DOI: https://doi.org/10.1115/OMAE2022-79747

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Topics: Drilling, Optimization, Underwater drilling, Workflow, Design, Machine learning, Artificial neural networks, Fluid dynamics, Genetic algorithms, String

Proceedings Papers

New Approach to Predict the Filtrate Invasion of Nanoparticle-Based Drilling Mud Using Artificial Neural Network

Moamen Gasser, Ahmed Naguib, Omar Mahmoud

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A016, June 5–10, 2022

Paper No: OMAE2022-81524

DOI: https://doi.org/10.1115/OMAE2022-81524

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Topics: Artificial neural networks, Drilling, Modeling, Nanoparticles

Proceedings Papers

Application of Recurrent Neural Network Long Short-Term Memory Model on Early Kick Detection

Junzhe Wang, Evren M. Ozbayoglu

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A008, June 5–10, 2022

Paper No: OMAE2022-78739

DOI: https://doi.org/10.1115/OMAE2022-78739

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Topics: Artificial neural networks, Drilling, Time series

Proceedings Papers

Cost-Effective Optimal Solutions for Steel Catenary Riser Using Artificial Neural Network

Joshua T. Abam, Yongchang Pu, Zhiqiang Hu

Proc. ASME. OMAE2022, Volume 3: Materials Technology; Pipelines, Risers, and Subsea Systems, V003T04A031, June 5–10, 2022

Paper No: OMAE2022-79044

DOI: https://doi.org/10.1115/OMAE2022-79044

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Topics: Artificial neural networks, Optimization, Steel catenary risers

Proceedings Papers

Real-Time Ship Motion Prediction Using Artificial Neural Network

Bhushan Taskar, Kie Hian Chua, Tatsuya Akamatsu, Ryo Kakuta, Song Wen Yeow, Ryosuke Niki, Keita Nishizawa, Allan Magee

Proc. ASME. OMAE2022, Volume 5B: Ocean Engineering; Honoring Symposium for Professor Günther F. Clauss on Hydrodynamics and Ocean Engineering, V05BT12A013, June 5–10, 2022

Paper No: OMAE2022-80042

DOI: https://doi.org/10.1115/OMAE2022-80042

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Topics: Artificial neural networks, Ships

Proceedings Papers

Estimation of Ocean’s Currents Acting on a Turret-Moored FPSO Using Machine Learning

Pedro Felipe Lavra Dias, Eduardo Aoun Tannuri, Anna Helena Reali Costa, Glauco Augusto De Paula Caurin, Gustavo Alencar Bisinotto

Proc. ASME. OMAE2022, Volume 5B: Ocean Engineering; Honoring Symposium for Professor Günther F. Clauss on Hydrodynamics and Ocean Engineering, V05BT06A002, June 5–10, 2022

Paper No: OMAE2022-78607

DOI: https://doi.org/10.1115/OMAE2022-78607

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Topics: Artificial neural networks, Currents, FPSO, Machine learning, Mooring

Proceedings Papers

Advanced Intelligent Control Strategy in Dynamic Positioning (DP) System Applied to a Semi-Submersible Drilling Platform in the North Sea

Mohamad Alremeihi, Rosemary Norman, Kayvan Pazouki, Arun Dev, Musa Bashir

Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A001, June 21–30, 2021

Paper No: OMAE2021-61525

DOI: https://doi.org/10.1115/OMAE2021-61525

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Topics: Artificial neural networks, Control systems, Drilling, Dynamic positioning systems, North Sea, Semi-submersible offshore structures

Proceedings Papers

An Adaptive Method to Further Improve the Tolerance of an ANN-Based Detection System for Mooring Line Failure

Djoni E. Sidarta, Nicolas Tcherniguin, Ho-Joon Lim, Philippe Bouchard, Mengchen Kang, Aurelien Leridon

Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A033, June 21–30, 2021

Paper No: OMAE2021-63326

DOI: https://doi.org/10.1115/OMAE2021-63326

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Topics: Artificial neural networks, Failure, Mooring

Proceedings Papers

Semantic Search in Offshore Engineering With Linguistics And Neural Processing Pipelines

Flavio Jaime Pol Gonçalves, Vinicius Cleves de Oliveira Carmo, Vinicius Toquetti de Melo, Rodrigo da Silva Cunha, Ismael H. F. Santos, Rodrigo Augusto Barreira, Carlos Eduardo Cugnasca, Fabio Gagliardi Cozman, Edson Satoshi Gomi

Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A004, June 21–30, 2021

Paper No: OMAE2021-62979

DOI: https://doi.org/10.1115/OMAE2021-62979

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Topics: Ocean engineering, Pipelines, Semantics, Artificial neural networks, Construction, Damping, FPSO, Ontologies

Proceedings Papers

Neural Network Meta-Models for FPSO Motion Prediction From Environmental Data

Lucas Pereira Cotrim, Henrique Barros Oliveira, Asdrubal N. Queiroz Filho, Ismael H. F. Santos, Rodrigo Augusto Barreira, Eduardo Aoun Tannuri, Anna Helena Reali Costa, Edson Satoshi Gomi

Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A003, June 21–30, 2021

Paper No: OMAE2021-62674

DOI: https://doi.org/10.1115/OMAE2021-62674

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Topics: Artificial neural networks, Design, FPSO, Mooring, Dynamics (Mechanics), Engineering simulation, Simulation, Time series, Architecture, Artificial intelligence

Proceedings Papers

FPSO Mooring Line Failure Detection Based on Predicted Motion

Amir Muhammed Saad, Florian Schopp, Asdrubal N. Queiroz Filho, Rodrigo Da Silva Cunha, Ismael H. F. Santos, Rodrigo Augusto Barreira, Eduardo Aoun Tannuri, Edson Satoshi Gomi, Anna Helena Reali Costa

Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A002, June 21–30, 2021

Paper No: OMAE2021-62413

DOI: https://doi.org/10.1115/OMAE2021-62413

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Topics: Artificial neural networks, Failure, FPSO, Mooring, Multilayer perceptron, System failures

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ROP Optimization of Lateral Wells in SW Oklahoma: Artificial Neural Network Approach

An Artificial Neural Network Approach for Fatigue Analysis of Slender Marine Structures

Predicting the Dynamic Response of a Steel Lazy Wave Riser in the Time Domain Using Artificial Neural Networks

Physics-Informed Neural Network With Numerical Differentiation for Modelling Complex Fluid Dynamic Problems

Assessment of Sea State Estimation With Convolutional Neural Networks Based on the Motion of a Moored FPSO Subjected to High-Frequency Wave Excitation

Application of Machine Learning for the Generation of Tailored Wave Sequences

Artificial Neural Network Surrogate Modeling for Offshore Wind Turbines Under Multi-Hazards

A Study on the Error Characteristics and Compensation Algorithm for Strain Gauge in Ship Structure Monitoring Systems

Rate of Penetration Prediction Using Quantile Regression Deep Neural Networks

Global Optimization Workflow for Offshore Drilling Rate of Penetration With Dynamic Drilling Log Data

New Approach to Predict the Filtrate Invasion of Nanoparticle-Based Drilling Mud Using Artificial Neural Network

Application of Recurrent Neural Network Long Short-Term Memory Model on Early Kick Detection

Cost-Effective Optimal Solutions for Steel Catenary Riser Using Artificial Neural Network

Real-Time Ship Motion Prediction Using Artificial Neural Network

Estimation of Ocean’s Currents Acting on a Turret-Moored FPSO Using Machine Learning

Advanced Intelligent Control Strategy in Dynamic Positioning (DP) System Applied to a Semi-Submersible Drilling Platform in the North Sea

An Adaptive Method to Further Improve the Tolerance of an ANN-Based Detection System for Mooring Line Failure

Semantic Search in Offshore Engineering With Linguistics And Neural Processing Pipelines

Neural Network Meta-Models for FPSO Motion Prediction From Environmental Data

FPSO Mooring Line Failure Detection Based on Predicted Motion

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