Robust model identification of actuated vortex wakes

TL;DR

This paper introduces a robust low-order modeling technique for actuated flows that regularizes an inverse problem to accurately predict flow dynamics and control responses across different scenarios.

Contribution

It presents a novel regularization approach for inverse problems in flow modeling, improving robustness and predictive capability over traditional Galerkin-based models.

Findings

The proposed method is robust to variations in control laws.

Traditional Galerkin models are ill-posed or inaccurate for new cases.

Numerical evidence demonstrates improved prediction accuracy.

Abstract

We present a low-order modeling technique for actuated flows based on the regularization of an inverse problem. The inverse problem aims at minimizing the error between the model predictions and some reference simulations. The parameters to be identified are a subset of the coefficients of a polynomial expansion which models the temporal dynamics of a small number of global modes. These global modes are found by Proper Orthogonal Decomposition, which is a method to compute the most representative elements of an existing simulation database in terms of energy. It is shown that low-order control models based on a simple Galerkin projection and usual calibration techniques are not viable. They are either ill-posed or they give a poor approximation of the solution as soon as they are used to predict cases not belonging to the original solution database. In contrast, numerical evidence shows that the method we propose is robust with respect to variations of the control laws applied, thus allowing the actual use of such models for control.