  Reconstruction of unsteady vortical flow fields from limited sensor
measurements is challenging. We develop machine learning methods to reconstruct
flow features from sparse sensor measurements during transient vortex-airfoil
wake interaction using only a limited amount of training data. The present
machine learning models accurately reconstruct the aerodynamic force
coefficients, pressure distributions over airfoil surface, and two-dimensional
vorticity field for a variety of untrained cases. Multi-layer perceptron is
used for estimating aerodynamic forces and pressure profiles over the surface,
establishing a nonlinear model between the pressure sensor measurements and the
output variables. A combination of multi-layer perceptron with convolutional
neural network is utilized to reconstruct the vortical wake. Furthermore, the
use of transfer learning and long short-term memory algorithm combined in the
training models greatly improves the reconstruction of transient wakes by
embedding the dynamics. The present machine-learning methods are able to
estimate the transient flow features while exhibiting robustness against noisy
sensor measurements. Finally, appropriate sensor locations over different time
periods are assessed for accurately estimating the wakes. The present study
offers insights into the dynamics of vortex-airfoil interaction and the
development of data-driven flow estimation.
