The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows

TL;DR

The GNAT method is a nonlinear model reduction technique that significantly accelerates large-scale CFD simulations, especially for turbulent flows, by reducing computational costs while maintaining high accuracy.

Contribution

This paper extends the GNAT method with global error bounds and a sample mesh concept, enabling efficient, distributed implementation in CFD codes for turbulent flow problems.

Findings

Reduces CFD computational time by over 100x

Maintains less than 1% error compared to full models

Outperforms other nonlinear reduction methods on benchmark problems

Abstract

The Gauss--Newton with approximated tensors (GNAT) method is a nonlinear model reduction method that operates on fully discretized computational models. It achieves dimension reduction by a Petrov--Galerkin projection associated with residual minimization; it delivers computational efficency by a hyper-reduction procedure based on the `gappy POD' technique. Originally presented in Ref. [1], where it was applied to implicit nonlinear structural-dynamics models, this method is further developed here and applied to the solution of a benchmark turbulent viscous flow problem. To begin, this paper develops global state-space error bounds that justify the method's design and highlight its advantages in terms of minimizing components of these error bounds. Next, the paper introduces a `sample mesh' concept that enables a distributed, computationally efficient implementation of the GNAT method in finite-volume-based computational-fluid-dynamics (CFD) codes. The suitability of GNAT for parameterized problems is highlighted with the solution of an academic problem featuring moving discontinuities. Finally, the capability of this method to reduce by orders of magnitude the core-hours required for large-scale CFD computations, while preserving accuracy, is demonstrated with the simulation of turbulent flow over the Ahmed body. For an instance of this benchmark problem with over 17 million degrees of freedom, GNAT outperforms several other nonlinear model-reduction methods, reduces the required computational resources by more than two orders of magnitude, and delivers a solution that differs by less than 1% from its high-dimensional counterpart.