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

TL;DR

This work tackles structural icing in a subsonic wind tunnel by building a hybrid nonlinear representation that couples lumped-parameter physics (tanks, valves, and flow) with data-driven components (nozzle temperature, LWC, MVD) to predict icing metrics. Implemented in Matlab/Simulink, the model blends first-principles equations with regression-tree and neural-network components to link injection settings to $\Lambda$ and $M$ under realistic operating conditions. Validation against 30 experiments shows the approach captures typical ranges of $\Lambda$ ($[0.3,2.5]$ g/m^3) and $M$ ($[16.9,48.8]$ µm) and reproduces observed trends, enabling simulation-based exploration of icing scenarios. The work lays groundwork for optimization and advanced control strategies (e.g., MPC, reinforcement learning) to achieve desired icing levels while reducing resource use, with potential applications in ice protection system testing and defrost device assessment.

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.

Figures (14)

• Figure 1: Testbed scheme.
• Figure 2: Wind tunnel facilities.
• Figure 3: Water tank.
• Figure 4: Nozzle's geometry.
• Figure 5: MIG and f-score for $T_{\text{n}}$.

Theorems & Definitions (2)

• Remark 1
• Remark 2