A machine learning study of wind-driven runback/flow-off multiphase flows pertinent to aircraft icing phenomena

Abstract

In-flight icing and ground icing are widely recognized hazards that have a substantial impact on the safety of aircraft during the processes of takeoff, cruising, and landing in cold weather conditions. The unsteady runback behavior of wind-driven runback water film (WDRWF) flows over aircraft surfaces has a significant impact on the aircraft in-flight icing process. Conventional theoretical/numerical methods cannot accurately predict WDRWF flow due to the limited comprehension of the intricate multiphase interactions among freestream airflow, water film motion, and solid airframe surface. Machine learning methodologies can effectively capture intricate physics phenomena through data assimilation, rendering them a compelling substitute for conventional approaches. In the present study, a deep-learning framework ConvLSTM-AE is developed to forecast the intricate spatial-temporal progression of an experimental multiphase WDRWF flow on a flat plate, considering different water flow rates and wind speeds. To predict the WDRWF flow with a long-time evolution without depending on previous ground truth, a physics-guided Fourier neural operator is developed. Then the nonlinear dynamics of the experimental WDRWF flow are identified by utilizing an interpretable data-driven framework known as sparse identification of nonlinear dynamics. Moreover, to extract new physical properties from the experimental WDRWF flow dataset, a physics-informed neural network is utilized to inversely forecast the interfacial shear stress field from thickness field. The last section of the study shifts its attention to the deicing fluids flow-off process during aircraft take-off. Deicing fluids employed on aircraft surfaces serve the purpose of mitigating ice accumulation; however, their presence might have adverse effects on aerodynamic performance during the take-off phase. Consequently, a thorough experimental investigation is conducted to characterize the wind-driven flow-off process of Newtonian deicing fluid over a flat surface utilizing an innovative Digital Image Projection (DIP) technology. In summary, machine learning techniques are proved to be able to forecast complex multiphase flow features related to aircraft icing and gain new physical understanding from current experimental datasets. The experimental study on deicing fluid flow-off can improve ground icing strategy optimization and provide unique datasets for machine learning approaches to predict the flow-off process.