Model reduction for flow analysis and control
Advances in experimental techniques and the ever-increasing fidelity of numerical
simulations have led to an abundance of data describing fluid flows. This review discusses a …
simulations have led to an abundance of data describing fluid flows. This review discusses a …
Data-driven modeling for unsteady aerodynamics and aeroelasticity
Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …
addition to experiment and numerical simulation, due to its low-dimensional representation …
[BOOK][B] Dynamic mode decomposition: data-driven modeling of complex systems
The integration of data and scientific computation is driving a paradigm shift across the
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …
Dynamic mode decomposition with control
We develop a new method which extends dynamic mode decomposition (DMD) to
incorporate the effect of control to extract low-order models from high-dimensional, complex …
incorporate the effect of control to extract low-order models from high-dimensional, complex …
Data-driven sparse sensor placement for reconstruction: Demonstrating the benefits of exploiting known patterns
Optimal sensor and actuator placement is an important unsolved problem in control theory.
Nearly every downstream control decision is affected by these sensor and actuator …
Nearly every downstream control decision is affected by these sensor and actuator …
Generalizing Koopman theory to allow for inputs and control
We develop a new generalization of Koopman operator theory that incorporates the effects
of inputs and control. Koopman spectral analysis is a theoretical tool for the analysis of …
of inputs and control. Koopman spectral analysis is a theoretical tool for the analysis of …
A kernel-based approach to data-driven Koopman spectral analysis
A data driven, kernel-based method for approximating the leading Koopman eigenvalues,
eigenfunctions, and modes in problems with high dimensional state spaces is presented …
eigenfunctions, and modes in problems with high dimensional state spaces is presented …
CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers
K Hasegawa, K Fukami, T Murata… - Fluid Dynamics …, 2020 - iopscience.iop.org
We investigate the capability of machine learning (ML) based reduced order model (ML-
ROM) for two-dimensional unsteady flows around a circular cylinder at different Reynolds …
ROM) for two-dimensional unsteady flows around a circular cylinder at different Reynolds …
An improved criterion to select dominant modes from dynamic mode decomposition
Dynamic mode decomposition (DMD) has been extensively utilized to analyze the coherent
structures in many complex flows. Although specific flow patterns with dominant frequency …
structures in many complex flows. Although specific flow patterns with dominant frequency …
Data-driven modal decomposition of transient cavitating flow
Y Liu, J Long, Q Wu, B Huang, G Wang - Physics of Fluids, 2021 - pubs.aip.org
The objective of this paper is to identify the dominant coherent structures within cavitating
flow around a Clark-Y hydrofoil using two data-driven modal decomposition methods, proper …
flow around a Clark-Y hydrofoil using two data-driven modal decomposition methods, proper …