Load estimation in unsteady flows from sparse pressure measurements: Application of transition networks to experimental data

TL;DR

This paper introduces a novel data-driven method using transition networks to estimate aerodynamic loads from sparse pressure measurements in unsteady flows, demonstrating effectiveness on experimental data with potential for real-time applications.

Contribution

It presents a new approach combining transition networks and weighted averaging for load estimation from limited pressure data in complex, unsteady flows.

Findings

Transition networks effectively model aerodynamic state dynamics.

The WAB strategy accurately estimates loads in experimental flow conditions.

Method shows robustness with sparse and noisy pressure signals.

Abstract

Inspired by biological swimming and flying with distributed sensing, we propose a data-driven approach for load estimation that relies on complex networks. We exploit sparse, real-time pressure inputs, combined with pre-trained transition networks, to estimate aerodynamic loads in unsteady and highly-separated flows. The transition networks contain the aerodynamic states of the system as nodes along with the underlying dynamics as links. A weighted average-based (WAB) strategy is proposed and tested on realistic experimental data on the flow around an accelerating elliptical plate at various angles-of-attack. Aerodynamic loads are then estimated for angles of attack cases not included in the training dataset so as to simulate the estimation process. An optimization process is also included to account for the system's temporal dynamics. Performance and limitations of the WAB approach are discussed, showing that transition networks can represent a versatile and effective data-driven tool for real-time signal estimation using sparse and noisy signals (such as surface pressure) in realistic flows.