Data assimilation for control of a plane mixing layer

N Kumar,L Cordier,F Kerhervé

doi:10.1051/matecconf/201928607013

Abstract

The design of active model-based flow controllers requires the knowledge of a dynamical model of the flow. However, real-time and robust estimation of the flow state remains a challenging task when only limited spatial and temporal discrete measurements are available. In this study, the objective is to draw upon the methodologies implemented in meteorology to develop dynamic observers for flow control applications. Well established data assimilation (DA) method using Kalman filter is considered. These approaches are extended to both estimate model states and parameters. Simple non-linear dynamical models are first considered to establish quantitative comparisons between the diﬀerent algorithms. An experimental demonstration for the particular case of a plane mixing layer is then proposed.

Highlights

Despite decades of intensive research in shape optimization, aerodynamic mechanisms such as separation and mixing still represent an important source of energy expenditure in transport vehicles
The manufacturers have developed, over the years, a range of strategies to improve the aerodynamic performance of their vehicles. One of these strategies consists in inserting passive control devices like vortex generators directly in the flow-path. These actuators are optimized considering the most likelihood inflow condition which potentially leads to bad performance for varying initial conditions and/or unsteady upstream perturbations
This approach requires the knowledge of a dynamical model of the flow that one wants to control in real-time. The identification of such a model is challenging because it is generally obtained from limited spatial and time discrete measurements. This problem of state estimation is well-known in the field of meteorology where data is collected at several locations, with spatial and temporal scales varying in orders of magnitude, to estimate the evolution of the weather [7]

Summary

Introduction

Despite decades of intensive research in shape optimization, aerodynamic mechanisms such as separation and mixing still represent an important source of energy expenditure in transport vehicles. The manufacturers have developed, over the years, a range of strategies to improve the aerodynamic performance of their vehicles One of these strategies consists in inserting passive control devices like vortex generators directly in the flow-path. The classical remedy is to consider active devices that can be actuated on and off based on the change of the inflow conditions To drive such actuators, command laws and more globally valid controllers are required. The derived physical reduced-order model (ROM) can be used in conjunction with DA techniques to design dynamic observers able to predict the flow state from limited information [4] In this work, such dynamic observers will be developed, building on the state-of-the-art in meteorology. The second part will focus on the experimental implementation by considering the canonical case of a plane mixing layer in the perspective of real-time control

Assessment of Data Assimilation method

Findings

Experimental Demonstration