Data-Driven Closure of Projection-Based Reduced Order Models for Unsteady Compressible Flows

TL;DR

This paper introduces a data-driven closure method for projection-based reduced order models of unsteady compressible flows, improving stability and accuracy through POD modes, hyper-reduction, and non-linear calibration.

Contribution

It presents a novel data-driven closure approach using POD modes, combined with hyper-reduction and non-linear calibration, for stable and accurate ROMs of complex compressible flows.

Findings

Linear and non-linear closure coefficients yield high accuracy for canonical flows.

Non-linear calibration outperforms linear in turbulent flow with fewer modes.

Hyper-reduction enhances computational efficiency with maintained accuracy.

Abstract

A data-driven closure modeling based on proper orthogonal decomposition (POD) temporal modes is used to obtain stable and accurate reduced order models (ROMs) of unsteady compressible flows. Model reduction is obtained via Galerkin and Petrov-Galerkin projection of the non-conservative compressible Navier-Stokes equations. The latter approach is implemented using the least-squares Petrov-Galerkin (LSPG) technique and the present methodology allows pre-computation of both Galerkin and LSPG coefficients. Closure is performed by adding linear and non-linear coefficients to the original ROMs and minimizing the error with respect to the POD temporal modes. In order to further reduce the computational cost of the ROMs, an accelerated greedy missing point estimation (MPE) hyper-reduction method is employed. A canonical compressible cylinder flow is first analyzed and serves as a benchmark. The second problem studied consists of the turbulent flow over a plunging airfoil undergoing deep dynamic stall. For the first case, linear and non-linear closure coefficients are both low in intrusiveness, capable of providing results in excellent agreement with the full order model. Regularization of calibrated models is also straightforward for this case. On the other hand, the dynamic stall flow is significantly more challenging, specially when only linear coefficients are used. Results show that non-linear calibration coefficients outperform their linear counterparts when a POD basis with fewer modes is used in the reconstruction. However, determining a correct level of regularization is more complicated with non-linear coefficients. Hyper-reduced models show good results when combined with non-linear calibration and an appropriate sized POD basis.