A Physics-Infused, Machine Learning Framework to Study Wind-Driven Runback Water Flows Pertinent to Aircraft Icing Phenomena

Aircraft icing is one of the most dangerous weather hazards to threaten flight safety in cold weather. The transient behavior of wind-driven runback water film/rivulet (WDRWF/R) flows over aircraft wings would affect the dynamic glaze ice accretion process significantly. In the present study, a novel, flow-physics-infused, machine learning (ML) framework is developed for more accurate prediction of the transient characteristics of WDRWF/R flows pertinent to aircraft icing phenomena. A comprehensive experimental campaign is conducted in a wind tunnel by using a novel Digital Image Projection (DIP) technique to achieve spatiotemporal measurements of the film thickness fields of WDWF/R flows over a flat plate under different test conditions. The massive experiment data is used to train and test a specialized Physics-Guided Fourier Neural Operator (PGFNO) to learn the intricate characteristics of WDRWF/R flows. Physical knowledge is infused through a composite loss function, ensuring accurate pointwise flow reconstruction and mass conservation. The trained model was then used to predict the spatiotemporal evolution of WDRWF/R flows over a wide range of flow conditions. It was demonstrated that the physics-infused ML model can accurately predict the transient characteristics of WDRWF/R flows, such as film thickness height and its spectrum, for unseen wind speeds, water flow rates, and initial conditions. The model can aid in the accurate glaze ice accretion prediction when combined with freezing models.