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Keywords: deep learning
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Proceedings Papers

Proc. ASME. OMAE2023, Volume 1: Offshore Technology, V001T01A010, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-107794
... high-fidelity CFD is computationally expensive, it is difficult to optimize the complicated three-dimensional blade shape in various wind conditions efficiently and robustly. To solve the issue, we present an efficient wind turbine blade shape optimization method using deep learning. The optimization...
Proceedings Papers

Proc. ASME. OMAE2023, Volume 6: Polar and Arctic Sciences and Technology, V006T07A005, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-101757
... Research Institute. Second, as a test case, the study evaluates simulated ASSIST observations conducted on optical images by an ice expert, three non-experts, and a deep learning model. Results from the comparisons indicate a good agreement between ASSIST observations and the sea ice charts, with ∼80...
Proceedings Papers

Proc. ASME. OMAE2023, Volume 5: Ocean Engineering, V005T06A069, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-104286
..., machine and deep learning in this field is new. So, the objectives of the current research are twofold; the first is to take advantage of temporal and spectral information for seaway estimation in a deep learning model. The second is to propose a solution addressing two significant issues of WBA: the time...
Proceedings Papers

Proc. ASME. OMAE2023, Volume 5: Ocean Engineering, V005T06A070, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-104377
... with different levels of non-linearity for the experiment. The experiment results illustrate the efficacy of two deep learning models as influenced by nonlinearity and noise levels, but TCNs do not outperform ANNs in the same point wave prediction. wave prediction deep learning temporal convolutional...
Proceedings Papers

Proc. ASME. OMAE2023, Volume 6: Polar and Arctic Sciences and Technology, V006T07A014, June 11–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2023-102855
... Abstract Accurate prediction of ice resistance plays an important role in ensuring the safety of ship navigation when sailing in ice regions. In recent years, machine learning has been widely applied in the field of ships, among which deep learning is a common method. This study combines...
Proceedings Papers

Proc. ASME. OMAE2022, Volume 5B: Ocean Engineering; Honoring Symposium for Professor Günther F. Clauss on Hydrodynamics and Ocean Engineering, V05BT06A045, June 5–10, 2022
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2022-79891
... Vehicles (AUVs). In recent years autonomous vehicles have benefited immensely from the advancements in the area of Artificial Intelligence (AI) and particularly in Deep Learning (DL). Although DL models and applications are extensively used in different areas, such as in Aerial Unmanned Vehicles...
Proceedings Papers

Proc. ASME. OMAE2022, Volume 7: CFD and FSI, V007T08A001, June 5–10, 2022
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2022-81237
... deep learning automatic differentiation numerical differentiation Navier-Stokes equations inverse problem Proceedings of the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering OMAE2022 June 5-10, 2022, Hamburg, Germany OMAE2022-81237 PHYSICS-INFORMED NEURAL NETWORK...
Proceedings Papers

Proc. ASME. OMAE2022, Volume 3: Materials Technology; Pipelines, Risers, and Subsea Systems, V003T04A018, June 5–10, 2022
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2022-79494
... rather than these model-based methods to analyze the kinetic experimental data during the hydrate formation. Deep learning models, specifically LSTM (Long Short-Term Memory) were used to be trained based on the lab-scale experiment data to make the real-time prediction. The transition trend of hydrate...
Proceedings Papers

Proc. ASME. OMAE2022, Volume 1: Offshore Technology, V001T01A009, June 5–10, 2022
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2022-79757
... and equipment. Images of the Gulf of Mexico oil spill were used to test the methodology and the successful detection of the leak demonstrates the potential of the methodology for underwater leak detection. leak detection deep learning subsea inspection Proceedings of the ASME 2022 41st International...
Proceedings Papers

Proc. ASME. OMAE2022, Volume 5B: Ocean Engineering; Honoring Symposium for Professor Günther F. Clauss on Hydrodynamics and Ocean Engineering, V05BT06A004, June 5–10, 2022
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2022-79126
... from three different vessels, each having a different level of data completeness. For these cases it is concluded that the consistency can be assessed when the sea state is not too extreme. The method may be improved by using higher fidelity seakeeping codes. machine learning deep learning...
Proceedings Papers

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A010, June 5–10, 2022
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2022-79046
... the advantages of combining deep learning with quantile regression compared to using machine learning models which only generate single-point predictions for the ROP. ROP model deep learning quantile regression Proceedings of the ASME 2022 41st International Conference on Ocean, Offshore and Arctic...
Proceedings Papers

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A012, June 5–10, 2022
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2022-79623
... Abstract In this study, a deep learning model is proposed that can accurately predict the rate of penetration during geothermal or oil and gas well construction operations. Also, a genetic algorithm is applied and used together with the deep learning model to determine the optimum values...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A072, June 21–30, 2021
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2021-62621
... during tripping operation. The data generated from this model, field data, and experimental data are used to train and test the FFBP-DNN networks. The network is developed used Keras’s deep learning framework. After testing the models, the most optimal arrangement of FFBP-DNN is the ReLU algorithm...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 2: Structures, Safety, and Reliability, V002T02A037, June 21–30, 2021
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2021-62304
... artificial neural networks machine learning deep learning structural optimization 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...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A006, June 21–30, 2021
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2021-63018
... steadily. However, a sharp augmentation in number of articles is observed from 2011 up to now. More than that, Artificial Neural Networks (ANN) is the most employed algorithm with 23 applications out of 38 studied papers. In addition, for the first time, deep learning- multiple fully connected networks...
Topics: Pressure
Proceedings Papers

Proc. ASME. OMAE2020, Volume 1: Offshore Technology, V001T01A059, August 3–7, 2020
Publisher: American Society of Mechanical Engineers
Paper No: OMAE2020-18844
... optimization. There are three main components of the proposed architecture for the mathematical formulation of the DP sub-systems based on individual sensor arrangements within the sub-system, computation of reliability of sub-systems and optimized LSTM deep learning algorithm for prediction of its...