Predicting Particle Acceleration Using Flow Feature Extraction in a Louver Particle Separator

Abstract

The goal of this work is to facilitate the design of gas turbine engine particle separators by reducing the computational expense necessary to accurately predict particle motion with machine learning (ML). It is well known that article ingestion has many detrimental impacts on gas turbine engine operation. Particle ingestion is especially of concern in environments where dust, ash, or ice persist. Accordingly, devices called particle separators are used to remove the particles before they enter the engine. The goal of this work is to improve prediction of particle motion in separators. Ice particles are chosen for this study because of their relevance to civil aviation. The louver particle separator developed by [1] with computational studies is used as a baseline for this work. It has been shown that particle acceleration can be reasonably well-predicted in simple laminar flows using deep feed-forward neural networks [2]. However, there exists a gap for predicting particle acceleration in engineering flows such as the louver geometry of [1]. This is the topic of the current paper. The work builds upon work for laminar flows [2]. In the current work, the complex flow around the louver is decomposed into a laminar shear flow and laminar vortex, and results from [2] are used to analyze the decomposed flows. The accuracy of this decomposition for capturing particle trajectory in the louver is evaluated.