1 citations · 2 across the 4 of their papers we have counts for
4 papers
Stochastic reduced-order Koopman model for turbulent flows
Tianyi Chu, Oliver T. Schmidt
A stochastic data-driven reduced-order model applicable to a wide range of turbulent natural and engineering flows is presented. Combining ideas from Koopman theory and spectral mo…
Parametric reduced-order modeling and mode sensitivity of actuated cylinder flow from a matrix manifold perspective
Shintaro Sato, Oliver T. Schmidt
We present a framework for parametric proper orthogonal decomposition (POD)-Galerkin reduced-order modeling (ROM) of fluid flows that accommodates variations in flow parameters and…
Linear model reduction using spectral proper orthogonal decomposition
Peter Frame, Cong Lin, Oliver Schmidt +1
Most model reduction methods reduce the state dimension and then temporally evolve a set of coefficients that encode the state in the reduced representation. In this paper, we inst…
Linear stability and spectral modal decomposition of three-dimensional turbulent wake flow of a generic high-speed train
Xiao-Bai Li, Simon Demange, Guang Chen +4
This work investigates the spatio-temporal evolution of coherentstructures in the wake of a high-speed train. SPOD is used to extract energy spectra and empirical modes for both sy…