From resolvent to Gramians: extracting forcing and response modes for control

TL;DR

This paper introduces a method to derive frequency-independent forcing and response modes from resolvent analysis using Gramians, enabling more effective flow control and sensor placement without needing data snapshots.

Contribution

It presents a novel approach to obtain orthogonal, frequency-independent modes from resolvent analysis via eigenvectors of controllability and observability Gramians, applicable to flow control.

Findings

Gramians provide robust, frequency-independent modes for flow control.

Modes effectively identify disturbances and coherent structures in turbulent flows.

The method does not require data snapshots, only steady or mean flow information.

Abstract

During the last decade, forcing and response modes produced by resolvent analysis have demonstrated great potential to guide sensor and actuator placement and design in flow control applications. However, resolvent modes are frequency-dependent, which, although responsible for their success in identifying scale interactions in turbulence, complicates their use for control purposes. In this work, we seek orthogonal bases of forcing and response modes that are the most responsive and receptive, respectively, across all frequencies. We show that these frequency-independent bases of \emph{representative} resolvent modes are given by the eigenvectors of the observability and controllability Gramians of the system considering full state inputs and outputs. We present several numerical examples where we leverage these bases by building orthogonal or interpolatory projectors onto the dominant forcing and response subspaces. Gramian-based forcing modes are used to identify dynamically relevant disturbances, to place point sensors to measure disturbances, and to design actuators for feedforward control in the subcritical linearized Ginzburg--Landau equation. Gramian-based response modes are used to identify coherent structures and for point sensor placement aiming at state reconstruction in the turbulent flow in a minimal channel at $\mathrm{Re}_{\tau}=185$. The approach does not require data snapshots and relies only on knowledge of the steady or mean flow.