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Keywords: machine learning
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Proceedings Papers

Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A005, June 21–30, 2021
Paper No: OMAE2021-62991
... in mooring lines for floating vessels, validating the initial hypothesis that the difference in a natural period appears when a line breaks and that this difference may detect line break. mooring system failure detection floating offshore platforms vessel’s natural period machine learning...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 2: Structures, Safety, and Reliability, V002T02A037, June 21–30, 2021
Paper No: OMAE2021-62304
... Proceedings of the ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering OMAE2021 June 21-30, 2021, Virtual, Online OMAE2021-62304 ADAPTIVE CONSTRAINT HANDLING IN OPTIMIZATION OF COMPLEX STRUCTURES BY USING MACHINE LEARNING Yuecheng Cai Jasmin Jelovica Department...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 6: Ocean Engineering, V006T06A018, June 21–30, 2021
Paper No: OMAE2021-62395
... but they are either expensive or lack accuracy. Nowadays, a lot of ship performance-related parameters have been recorded during a ship’s sailing, and different data driven machine learning methods have been applied for the ship speed-power modelling. This paper compares different supervised machine learning...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 6: Ocean Engineering, V006T06A019, June 21–30, 2021
Paper No: OMAE2021-62658
...Abstract Abstract This paper presents the analysis to reduce carbon emission from tugboat operations by utilizing a proposed unsupervised machine learning operational scoring system. The time-series analysis is performed by transforming data into a common domain for clustering. The data...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A036, June 21–30, 2021
Paper No: OMAE2021-62407
... the experimental data to estimate the diffusion coefficient. The current study brings in the capability of machine learning as a replacement of the computational part for prediction of the diffusion coefficient of the process. The feasibility of various machine learning models such as Gradient boosting, Gaussian...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A006, June 21–30, 2021
Paper No: OMAE2021-62987
... the fluids properties in real- Keywords: Cuttings transport, Mechanistic model, HTHP time operations. From several drilling fluid functionalities and rheology, Cross-validation, machine learning operational parameters, we describe advancements to improve hole cleaning predictions and high-pressure high...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A007, June 21–30, 2021
Paper No: OMAE2021-63094
... transport, well hydraulics, event detection) are computationally expensive, difficult to integrate for real-time analysis, and not always applicable for all drilling conditions. For this reason, researchers have shown extensive interest in machine learning (ML) approaches to alleviate their fluid-related...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A008, June 21–30, 2021
Paper No: OMAE2021-63653
... the independent drilling variables directly as the model inputs provided more accurate results when compared with dimensional groups are used as the model inputs. cuttings transport machine learning adaboost random forest artificial neural networks Proceedings of the ASME 2021 40th International...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A017, June 21–30, 2021
Paper No: OMAE2021-62964
... to evaluate, characterize and predict the performance of tight UCRs. In this study, permeability and viscosity are used to develop the Unconventionality Index (UI) to reflect the combined causes of low mobility from UCRs. Machine learning is applied to synthesize a novel comprehensive understanding of UCRs...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 2: Structures, Safety, and Reliability, V002T02A024, June 21–30, 2021
Paper No: OMAE2021-65524
... of the platform’s motions can be predicted with higher accuracy. This proposed SADA method takes advantage of numerical-experimental method, basin experimental data and the machine learning technology, which brings a new and promising solution for overcoming the handicap impeding direct use of conventional basin...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 4: Pipelines, Risers, and Subsea Systems, V004T04A019, June 21–30, 2021
Paper No: OMAE2021-62033
... the full combination of uncertainties and to capture the worst-case scenario. Rather than applying the deterministic solution, this paper uses machine learning prediction to significantly improve the efficiency of the design process. In addition, thanks to the fast predictive model using machine learning...
Proceedings Papers

Proc. ASME. OMAE2020, Volume 4: Pipelines, Risers, and Subsea Systems, V004T04A011, August 3–7, 2020
Paper No: OMAE2020-18325
...USING MACHINE LEARNING TO IDENTIFY IMPORTANT PARAMETERS FOR FLOW-INDUCED VIBRATION Leixin Ma Massachusetts Institute of Technology Cambridge, MA, USA Themistocles L. Resvanis Massachusetts Institute of Technology Cambridge, MA, USA Prof. J. Kim Vandiver Massachusetts Institute of Technology...
Proceedings Papers

Proc. ASME. OMAE2020, Volume 4: Pipelines, Risers, and Subsea Systems, V004T04A040, August 3–7, 2020
Paper No: OMAE2020-18685
...MACHINE LEARNING FOR SUBSEA PIPELINE REELING MECHANICS Eric Giry1, Vincent Cocault-Duverger1 Martin Pauthenet2, Laurent Chec2 1Saipem SA, Montigny-Le-Bretonneux, France 2Datadvance, Toulouse, France ABSTRACT Installation of subsea pipelines using reeling process is an attractive method...
Proceedings Papers

Proc. ASME. OMAE2020, Volume 1: Offshore Technology, V001T01A003, August 3–7, 2020
Paper No: OMAE2020-18354
...APPLICATION OF MACHINE LEARNING ALGORITHM IN OPTIMIZATION OF PSV FOR 110000DWT OIL TANKER LIU Xiyang, CHEN Jingpu, SUN Wenyu, XU Wei. Shanghai Branch, China Ship Scientific Research Center Shanghai, China ABSTRACT The pre-shrouded vane (PSV) in front of propeller is a kind of energy-saving device...
Proceedings Papers

Proc. ASME. OMAE2020, Volume 11: Petroleum Technology, V011T11A001, August 3–7, 2020
Paper No: OMAE2020-18060
..., accessible from anywhere, can be automatically updated for continuous improvement, and can be deployed easily and quickly. It can also be extended to further applications. real-time data analytics drilling engineering drilling operations offshore onshore safety digital machine learning...
Proceedings Papers

Proc. ASME. OMAE2020, Volume 11: Petroleum Technology, V011T11A026, August 3–7, 2020
Paper No: OMAE2020-19169
... of lithology of drilling layer is also helpful for both scientific and operational aspects. However, there is no direct information regarding the core recovery rate and lithology. The recovery rate and lithology can be determined after retrieving a coring tool. Therefore, this study applies a machine learning...
Proceedings Papers

Proc. ASME. OMAE2020, Volume 11: Petroleum Technology, V011T11A003, August 3–7, 2020
Paper No: OMAE2020-18895
...Abstract Abstract Machine learning is gaining rapid popularity as a tool of choice for applications in almost every field. In the oil and gas industry, machine learning is used as a tool for solving problems which could not be solved by traditional methods or for providing a cost-effective...
Proceedings Papers

Proc. ASME. OMAE2020, Volume 6B: Ocean Engineering, V06BT06A025, August 3–7, 2020
Paper No: OMAE2020-19011
... that were not included in training with high accuracy. Keywords: Artificial Neural Network (ANN); Machine learning; Gillnet; Damage detection; Real-time monitoring; Sensors. 1. INTRODUCTION Marine pollution by marine debris has been considered as a serious problem. In specific, a ghost gear, which remains...
Proceedings Papers

Proc. ASME. OMAE2019, Volume 3: Structures, Safety, and Reliability, V003T02A023, June 9–14, 2019
Paper No: OMAE2019-95352
...-defined probability of, e.g., one expected exceedance per life cycle, as well as the further development of numerical methods to predict the ship response to the selected wave events. extreme loads HOSM machine learning CFD WAVE LOAD AND RESPONSE PREDICTIONS COMBINING HOSM, CFD AND MACHINE...
Proceedings Papers

Proc. ASME. OMAE2019, Volume 3: Structures, Safety, and Reliability, V003T02A070, June 9–14, 2019
Paper No: OMAE2019-96411
... of the current range of monitoring techniques are discussed, including well established technologies such as load cells, sonar, or visual inspection, within the context of the growing mainstream acceptance of data science and machine learning. Due to the large fleet of floating production platforms currently...