Active control of liquid film flows: beyond reduced-order models

TL;DR

This paper develops a multi-level modeling framework combining analytical models and simulations to improve active control of liquid film flows, demonstrating that analytically-informed feedback strategies outperform traditional transfer methods.

Contribution

It introduces a novel multi-level modeling approach for liquid film control, showing the effectiveness of analytically-informed feedback strategies beyond reduced-order model limitations.

Findings

Offline control transfer is ineffective.

Analytically-informed feedback strategies perform well.

Detailed analysis of control effects on stability and nonlinearity.

Abstract

The ability to robustly and efficiently control the dynamics of nonlinear systems lies at the heart of many current technological challenges, ranging from drug delivery systems to ensuring flight safety. Most such scenarios are too complex to tackle directly and reduced-order modelling is used in order to create viable representations of the target systems. The simplified setting allows for the development of rigorous control theoretical approaches, but the propagation of their effects back up the hierarchy and into real-world systems remains a significant challenge. Using the canonical setup of a liquid film falling down an inclined plane under the action of active feedback controls in the form of blowing and suction, we develop a multi-level modelling framework containing both analytical models and direct numerical simulations acting as an in silico experimental platform. Constructing strategies at the inexpensive lower levels in the hierarchy, we find that offline control transfer is not viable, however analytically-informed feedback strategies show excellent potential, even far beyond the anticipated range of applicability of the models. The detailed effects of the controls in terms of stability and treatment of nonlinearity are examined in detail in order to gain understanding of the information transfer inside the flows, which can aid transition towards other control-rich frameworks and applications.