Scaled-up prediction of steady Navier-Stokes equation with component reduced order modeling

TL;DR

This paper extends component reduced order modeling to steady Navier-Stokes equations, enabling efficient large-scale nonlinear flow simulations with significant speedup and maintained accuracy.

Contribution

It introduces a novel extension of component reduced order modeling to nonlinear Navier-Stokes flow, incorporating pressure stabilization and comparing nonlinear evaluation methods.

Findings

Achieved 23 times faster predictions with less than 4% error.

Demonstrated stability and accuracy in large-scale flow simulations.

Compared tensor projection and empirical quadrature, favoring the latter.

Abstract

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, component reduced order modeling method is developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has been only demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier-Stokes flow, with application to general nonlinear physics in view. Large-scale, global domain is decomposed into combination of small-scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected at small scale unit component. Velocity bases are augmented with pressure supremizer, in order to satisfy inf-sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: 3rd-order tensor projection operator and empirical quadrature procedure. The proposed method is demonstrated on flow over arrays of five different unit objects, achieving $23$ times faster prediction with less than $4\%$ relative error up to $256$ times larger scale domain than unit components. Furthermore, a numerical experiment with pressure supremizer strongly indicates the need of supremizer for stable pressure prediction. A comparison between tensorial approach and empirical quadrature procedure is performed, which suggests a slight advantage for empirical quadrature procedure.