Adaptive Data-Driven Model Order Reduction for Unsteady Aerodynamics

A data-driven adaptive reduced order modelling approach is presented for the reconstruction of impulsively started and vortex-dominated flows. residual-based error metric first time in framework approach. The Reduced Order Modelling selects locally most accurate model on basis lowest residual produced by substituting reconstructed flow field into a finite volume discretisation Navier–Stokes equations. study such an was performed to assess performance resulting with respect single-ROM based classical proper orthogonal decomposition, as number modes varied. Two- three-dimensional unsteady flows were considered demonstrate key features method its performance.