A Hybrid Modelling of a Water and Air Injector in a Subsonic Icing Wind Tunnel

TL;DR

This paper combines experimental wind tunnel studies with hybrid mathematical and machine learning models to analyze droplet generation and icing parameters, achieving accurate predictions of Liquid Water Content and Median Volumetric Diameter.

Contribution

It introduces a hybrid modeling approach integrating first principles and machine learning for simulating icing parameters in wind tunnel experiments.

Findings

Model accurately predicts LWC and MVD within experimental ranges

Hybrid approach enhances understanding of icing system behavior

Experimental data validates the simulation results

Abstract

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.