Scalable physics-guided data-driven component model reduction for steady Navier-Stokes flow

Computational physics simulation can be a powerful tool to accelerateindustry deployment of new scientific technologies.However, it must address the challenge ofcomputationally tractable, moderately accurate prediction at large industry scales,and training a model without data at such large scales.A recently proposed component reduced order modeling (CROM) tackles this challenge by combining reduced order modeling (ROM)with discontinuous Galerkin domain decomposition (DG-DD).While it can build a component ROM at small scales that can be assembled into a large scale system,its application is limited to linear physics equations.In this work, we extend CROM to nonlinear steady Navier-Stokes flow equation.Nonlinear advection term is evaluated via tensorial approach or empirical quadrature procedure.Application to flow past an array of objects at moderate Reynolds number demonstrates$\sim23.7$ times faster solutions with a relative error of $\sim 2.3\\%$, even at scales$256$ times larger than the original problem.