Quadratic approximation manifold for mitigating the Kolmogorov barrier in nonlinear projection-based model order reduction

A quadratic approximation manifold is presented for performing nonlinear, projection-based, model order reduction (PMOR). It constitutes a departure from the traditional affine subspace that aimed at mitigating Kolmogorov barrier nonlinear PMOR, particularly convection-dominated transport problems. builds on data-driven approach underlying construction of projection-based reduced-order models (PROMs); application-independent; linearization-free; and therefore robust highly Most importantly, this leads to PROMs deliver same accuracy as their counterparts using however much smaller dimension – typically, n2?n1, where n2 n1 denote dimensions PROMs, respectively. The computational advantages proposed high-order PMOR over are highlighted detached-eddy simulation-based prediction Ahmed body turbulent wake flow, which popular CFD benchmark problem in automotive industry. For fixed level, these include: total offline cost by factor greater than five; its online wall clock time 32; high-dimensional two orders magnitude.