This report presents a numerical study of reduced‐order representations for simulating incompressible Navier–Stokes flows over a range of physical parameters. The reduced‐order representations combine ideas of approximation for nonlinear terms, of local bases, and of least‐squares residual minimization. To construct the local bases, temporal snapshots for different physical configurations are collected automatically until an error indicator is reduced below a user‐specified tolerance. An adaptive time‐integration scheme is also employed to accelerate the generation of snapshots as well as the simulations with the reduced‐order representations. The accuracy and efficiency of the different representations is compared with examples with parameter sweeps. Copyright © 2015 John Wiley & Sons, Ltd.

Reduced-order representations for simulating incompressible Navier-Stokes flows can improve accuracy and efficiency when trained using temporal snapshots and adaptive time-integration schemes.

Adaptive training of local reduced bases for unsteady incompressible Navier–Stokes flows

Key Takeaway: Reduced-order representations for simulating incompressible Navier-Stokes flows can improve accuracy and efficiency when trained using temporal snapshots and adaptive time-integration schemes.