Results 181 to 190 of about 13,527,272 (221)
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IEEE Transactions on Industrial Informatics, 2021
The refined estimation of inertia can provide a reliable basis for power system operation and control. In this article, a data-driven approach for the estimation of inertia is proposed, and it can estimate the effective inertia of different areas in the ...
Deyou Yang +6 more
semanticscholar +1 more source
The refined estimation of inertia can provide a reliable basis for power system operation and control. In this article, a data-driven approach for the estimation of inertia is proposed, and it can estimate the effective inertia of different areas in the ...
Deyou Yang +6 more
semanticscholar +1 more source
Higher order dynamic mode decomposition of wind pressures on square buildings
Journal of Wind Engineering and Industrial Aerodynamics, 2021Wind pressures on buildings with different aspect ratios were investigated via higher-order dynamic mode decomposition (HODMD) in this study. Taken’s embedding theorem was used to augment the spatial dimensionality of the original snapshot matrix by ...
Lei Zhou, K. Tse, G. Hu, Yutong Li
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The Physics of Fluids, 2022
The large eddy simulation model coupled with the modified Schnerr-Sauer cavitation model has been used to numerically simulate the unsteady cavitation and noncavitation flow of the three-dimensional NACA66 hydrofoil under different operating conditions ...
Chen Yang, Jinsong Zhang, Zhenwei Huang
semanticscholar +1 more source
The large eddy simulation model coupled with the modified Schnerr-Sauer cavitation model has been used to numerically simulate the unsteady cavitation and noncavitation flow of the three-dimensional NACA66 hydrofoil under different operating conditions ...
Chen Yang, Jinsong Zhang, Zhenwei Huang
semanticscholar +1 more source
Dynamic mode decomposition with memory
Physical Review E, 2023This study proposed a numerical method of dynamic mode decomposition with memory (DMDm) to analyze multidimensional time-series data with memory effects. The memory effect is a widely observed phenomenon in physics and engineering and is considered to be the result of interactions between the system and environment.
Ryoji, Anzaki +4 more
openaire +2 more sources
Mode dynamics in optical cavities
Physical Review A, 1995We consider the dynamical behavior of light in a cavity that contains birefringent elements with time-dependent settings. The evolution of the two-dimensional polarization vector can be described by an equation of the Schr\"odinger type if the intracavity elements change little on the time scale of the round-trip time. A coupled-mode analysis is given,
, Schrama +3 more
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The Physics of Fluids, 2022
The flow through tandem square cylinders was investigated at a Reynolds number of 100 for oscillation amplitudes A = 0.1 D to 0.7 D and gaps L = 2.0 D, 5.0 D, and 6.0 D, where D is the width of the cylinders. A moving reference frame method combined with
Hongfu Zhang +4 more
semanticscholar +1 more source
The flow through tandem square cylinders was investigated at a Reynolds number of 100 for oscillation amplitudes A = 0.1 D to 0.7 D and gaps L = 2.0 D, 5.0 D, and 6.0 D, where D is the width of the cylinders. A moving reference frame method combined with
Hongfu Zhang +4 more
semanticscholar +1 more source
Adaptive dynamic mode decomposition and its application in rolling bearing compound fault diagnosis
Structural Health Monitoring, 2022The decoupling detection of compound faults in rolling bearing is attracting considerable attentions. In recent years, some time-series decomposition methods, such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD ...
Ping Ma, Hongli Zhang, Cong Wang
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arXiv.org
Extended dynamic mode decomposition (EDMD) is a well-established method to generate a data-driven approximation of the Koopman operator for analysis and prediction of nonlinear dynamical systems. Recently, kernel EDMD (kEDMD) has gained popularity due to
Frederik Köhne +4 more
semanticscholar +1 more source
Extended dynamic mode decomposition (EDMD) is a well-established method to generate a data-driven approximation of the Koopman operator for analysis and prediction of nonlinear dynamical systems. Recently, kernel EDMD (kEDMD) has gained popularity due to
Frederik Köhne +4 more
semanticscholar +1 more source
A Low Rank Dynamic Mode Decomposition Model for Short-Term Traffic Flow Prediction
IEEE transactions on intelligent transportation systems (Print), 2021Traffic flow data has three main characteristics: large amount of noise and incompleteness, temporal and spatial correlation, and dynamic sequential property.
Y. Yadong +5 more
semanticscholar +1 more source
PyDMD: A Python package for robust dynamic mode decomposition
Journal of machine learning researchThe dynamic mode decomposition (DMD) is a simple and powerful data-driven modeling technique that is capable of revealing coherent spatiotemporal patterns from data.
Sara M. Ichinaga +7 more
semanticscholar +1 more source