19 citations · 35 across the 9 of their papers we have counts for
5 papers · 1 filter
Elastoinertial turbulence: Data-driven reduced-order model based on manifold dynamics
Manish Kumar, C. Ricardo Constante-Amores, Michael D. Graham
Elastoinertial turbulence (EIT) is a chaotic state that emerges in the flows of dilute polymer solutions. Direct numerical simulation (DNS) of EIT is highly computationally expensi…
Dynamics of a Data-Driven Low-Dimensional Model of Turbulent Minimal Pipe Flow
C. Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham
The simulation of turbulent flow requires many degrees of freedom to resolve all the relevant times and length scales. However, due to the dissipative nature of the Navier-Stokes e…
Data-driven Koopman operator predictions of turbulent dynamics in models of shear flows
C. Ricardo Constante-Amores, Andrew J. Fox, Carlos E. Pérez De Jesús +1
The Koopman operator enables the analysis of nonlinear dynamical systems through a linear perspective by describing time evolution in the infinite-dimensional space of observables.…
Data-driven low-dimensional model of a sedimenting flexible fiber
Andrew J Fox, Michael D. Graham
The dynamics of flexible filaments entrained in flow, important for understanding many biological and industrial processes, are computationally expensive to model with full-physics…
Data-driven state-space and Koopman operator models of coherent state dynamics on invariant manifolds
C. Ricardo Constante-Amores, Michael D. Graham
The accurate simulation of complex dynamics in fluid flows demands a substantial number of degrees of freedom, i.e. a high-dimensional state space. Nevertheless, the swift attenuat…