Analysis of satellite-derived data for the study of fouling in aircraft engines

Abstract

Abstract Atmospheric particulate is one of the main causes of performance degradation in gas turbine engines, especially in the aeronautical field where filter barriers are absent. The ingested particles can stick to the blade surfaces of the engine, varying their shape and roughness. As a consequence, engine performance degradation takes place. The type and the amount of the particles ingested depend on the flight zones and altitude. During their missions, aircrafts follow a prescribed path defined in terms of altitude, longitude, and latitude. During its route, the aircraft engine encounters different environments characterized by different temperature, pressure, and air composition. Regarding the latter issue, the knowledge of this characteristic can be key information when these statistics are needed for obtaining data useful for engine degradation assessment or prediction. Many satellites, such as the environmental satellite CALIPSO, are employed to study the terrestrial aerosol and clouds profile by using a LIDAR (Laser Detection and Ranging). This technology is commonly used to determine the distance between a light emitter and an object and it is based on the light refraction phenomenon. Backscatter coefficients profiles data, which characterize the distribution of particles and aerosols in the atmosphere, are available in the open literature from the findings of CALIPSO. In this work, a new methodology to estimate the aerosol type and concentration encountered by an aircraft during a mission is proposed. To test the feasibility of this method, two aircraft missions for different length scales (medium and long haul) are analyzed and an estimate of the particulate encountered by the engines is provided. The mission analysis has been conducted by discretizing the altitude profile, longitude, and latitude coordinates of each flight and then cross-referencing them with the particulate concentration obtained from CALIPSO data.