  The study of droplet generation in wind tunnels in conducting icing
experiments is of great importance in determining ice formation on structures
or surfaces, where parameters such as Liquid Water Content (LWC) and Median
Volumetric Diameter (MVD) play a relevant role. The measurement of these
parameters requires specialised instrumentation. In this paper, several
experiments have been carried out in a subsonic wind tunnel facility to study
the parameters that are part of the icing process in structures. Furthermore, a
mathematical modelling of the constituent subsystems of the plant study that
allow us to have a comprehensive understanding of the behaviour of the system
is developed using techniques based on first principles and machine learning
techniques such as regression trees and neural networks. The simulation results
show that the implementation of the model manages to obtain prominent expected
values of LWC and MVD within the range of values obtained in the real
experimental data.
