Related references
Note: Only part of the references are listed.
Article
Engineering, Marine
Andrea Serani et al.
Summary: This study presents a data-driven and equation-free modeling approach based on dynamic mode decomposition (DMD) for forecasting ship trajectories, motions, and forces in waves. The statistical analysis of DMD forecasting capabilities on ships in waves shows that state augmentation improves the forecasting performance, and DMD provides insights into the physics of the underlying system dynamics.
Article
Mechanics
Tingting Liu et al.
Summary: In this study, a new surrogate model called CNN-HODMD is proposed to predict the unsteady fluid force time history for twin tandem cylinders. The results show that CNN-HODMD performs well in predicting the lift force at different aspect ratios and gap spacing within 5% error.
Article
Engineering, Marine
Chang-Zhe Chen et al.
Summary: To reveal the dynamic characteristics and achieve rapid prediction of ship maneuvering motion, a reduced-order DMD algorithm is applied to reconstruct and predict the zig-zag and turning circle maneuvering motions. A case study of a KVLCC2 tanker using model test data is conducted, and the dominant DMD modes are selected based on their contributions to the dynamical systems. The effects of truncation rank and input samples on prediction accuracy are analyzed, and computational time is compared and analyzed among different algorithms.
Review
Mechanics
Peter J. Schmid
Summary: Dynamic mode decomposition (DMD) is a technique for factorization and dimensionality reduction of data sequences, which simplifies complex evolution processes to their dominant features and essential components. It has been widely applied in various fields beyond fluid dynamics as well.
ANNUAL REVIEW OF FLUID MECHANICS
(2022)
Article
Engineering, Mechanical
Ming-Wei Li et al.
Summary: This paper proposes a hybrid forecasting model based on empirical mode decomposition (EMD) and deep learning for ship motion (SHM) time series, considering its strong nonlinear characteristics. It also improves the butterfly optimization algorithm (BOA) and introduces a quantum butterfly optimization algorithm (QBOA). Experimental results show that the proposed method achieves significant accuracy improvement compared to other models.
NONLINEAR DYNAMICS
(2022)
Article
Engineering, Mechanical
Cruz Y. Li et al.
Summary: This study focuses on the nuances of dynamic mode decomposition (DMD) sampling and investigates how sampling range and resolution affect the convergence of DMD modes. The results show that the stabilization state is the optimal state for modal convergence, while oversampling leads to algorithmic instability. The convergence of sampling resolution depends on mode-specific dynamics.
NONLINEAR DYNAMICS
(2022)
Article
Biology
Nourelhouda Groun et al.
Summary: This study investigates the performance of Higher Order Dynamic Mode Decomposition (HODMD) technique when applied to echocardiography images. The results demonstrate that HODMD is a robust and suitable tool for identifying characteristic patterns and classifying different cardiac diseases.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Engineering, Electrical & Electronic
C. N. S. Jones et al.
Summary: Concern for climate change is driving an increased use of electricity and renewable energy supply, leading to larger and interconnected power systems. The need for stability analysis and short-term prediction of power system output is urgent and complex. Data-driven techniques such as HODMD and THDMD are applied to modal analysis and prediction of frequency and power exchange deviations. These techniques are used to analyze blackouts in Europe and the UK, as well as a separation event in Australia, demonstrating their usefulness in understanding and addressing power system issues.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Automation & Control Systems
Shijie Li et al.
Summary: This paper presents a data-driven control strategy for path-following of underactuated ships. By utilizing experiment data to learn the characteristics of unknown ship dynamics, a linear model is constructed and integrated with a model predictive control framework to achieve good path-following performance.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Energy & Fuels
Matteo Diez et al.
Summary: This paper presents a data-driven and equation-free approach to forecast ship responses in waves using dynamic mode decomposition (DMD). Results show that DMD, which provides a linear finite-dimensional representation based on available data, has the potential to accurately predict ship trajectories, motions, and forces in wave conditions.
JOURNAL OF OCEAN ENGINEERING AND MARINE ENERGY
(2022)
Article
Mechanics
P. Gutierrez-Castillo et al.
Summary: This study investigates the decay process of trailing vortices using modal-decomposition technique, revealing that the whole wake can be divided into three consecutive regions. By performing higher-order dynamic mode decomposition, it is found that the decay can be approximated by at most three modes. Additionally, evidence of multiple instabilities after vortex roll up is presented.
Article
Chemistry, Analytical
Zhiwen Lu et al.
Summary: A novel crack localization method for operating rotors is proposed, which can accurately and robustly achieve multi-crack localization without baseline information, and eliminate common interferences.
Article
Energy & Fuels
Danny D'Agostino et al.
Summary: The prediction capability of recurrent-type neural networks for real-time short-term prediction of ship motions in high sea state is investigated. Three models, recurrent neural networks, long short-term memory, and gated recurrent units, are assessed and compared. The results show that all three methods provide promising and comparable results in predicting ship motions.
JOURNAL OF OCEAN ENGINEERING AND MARINE ENERGY
(2022)
Article
Engineering, Marine
Yuchao Wang et al.
Summary: Research on ship roll prediction methods based on deep learning, proposing single input single output and multiple input single output methods, analyzing and testing with real data to verify the accuracy and effectiveness of the models.
Article
Mechanics
Yunqing Liu et al.
Summary: This study investigates the dominant coherent structures in cavitating flow around a Clark-Y hydrofoil using data-driven modal decomposition methods (POD and DMD) and explores the interaction between cavitation and vortex. The results show that the main coherent structures include large-scale cavity-vortex, re-entrant jet, shear layer, and small-scale vortex in the wake, and the flow field can be reconstructed from the most energetic POD or DMD modes.
Article
Engineering, Ocean
Xiaoxian Guo et al.
Summary: The study developed an LSTM-based machine learning model to accurately predict motions of semi-submersibles, achieving close to 90% accuracy in predicting future motions up to 46.5 seconds. The trained model effectively worked with high noise levels and was able to predict vessel motions based solely on the motion itself.
APPLIED OCEAN RESEARCH
(2021)
Review
Engineering, Aerospace
Jiaqing Kou et al.
Summary: Aerodynamic modeling plays a crucial role in unstable aerodynamics, with traditional methods restricted by theoretical and empirical research. Data-driven methods offer high accuracy, low computational cost, and great potential for flow control and engineering optimization.
PROGRESS IN AEROSPACE SCIENCES
(2021)
Article
Mathematics
Nicola Demo et al.
Summary: This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. Reduced order models are adopted to improve efficiency and a dynamic mode decomposition enhancement is used to reduce computational costs. Real-time computation is achieved through proper orthogonal decomposition with Gaussian process regression technique, enabling convergence towards the optimal shape using a genetic optimization algorithm.
BOLLETTINO DELLA UNIONE MATEMATICA ITALIANA
(2021)
Article
Computer Science, Artificial Intelligence
Cheng Cheng et al.
Article
Computer Science, Information Systems
Yanming Zhang et al.
Article
Mechanics
Soledad Le Clainche et al.
Article
Energy & Fuels
Soledad Le Clainche et al.
Article
Mechanics
Jiaqing Kou et al.
Article
Mechanics
Jiaqing Kou et al.
EUROPEAN JOURNAL OF MECHANICS B-FLUIDS
(2017)
Article
Mathematics, Applied
Soledad Le Clainche et al.
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS
(2017)
Article
Mechanics
Peter J. Schmid
JOURNAL OF FLUID MECHANICS
(2010)
Article
Mechanics
Clarence W. Rowley et al.
JOURNAL OF FLUID MECHANICS
(2009)
