4 papers
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…
Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flow
Carlos E. Pérez De Jesús, Alec J. Linot, Michael D. Graham
Data-driven reduced-order models of the dynamics of complex flows are important for tasks related to design, understanding, prediction, and control. Many flows obey symmetries, and…
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions
Jake Buzhardt, C. Ricardo Constante-Amores, Michael D. Graham
This work explores the relationship between state space methods and Koopman operator-based methods for predicting the time-evolution of nonlinear dynamical systems. We demonstrate…
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…