Sparse surface pressure-based reconstruction of the flow around a thick airfoil over a range of angles of attack

TL;DR

This paper introduces a neural network-based method, SNN-POD, for reconstructing the flow around a thick airfoil from limited surface pressure data across various angles of attack, demonstrating accurate predictions with minimal sensors.

Contribution

The paper presents a novel neural approach combining global and local models to accurately estimate flow fields from sparse pressure measurements over a range of angles.

Findings

Maximum MSE error of 2.9% for sampled angles

Maximum MSE error of 6.6% for interpolated angles

Effective sensor placement using variance-based criteria

Abstract

We present an efficient neural-based approach to estimate the instantaneous flow field around an airfoil from limited surface pressure measurements. The model, denoted SNN-POD, relies on two independent shallow neural networks to predict the instantaneous flow over a wide range of angles of attack [10{\textdegree},20{\textdegree}]. At all angles the global model correctly recovers the average characteristics of the flow from single-time sensor data, thus allowing combination with local, angle-dependent models. The method is applied to 2D URANS simulations of a thick airfoil at a Reynolds number of Re=4.5e6. The training set consists of snapshots obtained from a coarse sampling (1-2{\textdegree}) of the angle of attack range. A variance-based criterion is used to determine the number and positions of sensors. Tests are carried out for unseen snapshots at angles of attack within the set (sampled angles) as well as outside the set (interpolated angles). The maximum MSE error of attack for sampled and interpolated angles is respectively 2.9% and 6.6%. This makes it possible to develop adaptive strategies to improve the estimation if necessary.