Axial Compressor Map Generation Leveraging Autonomous Self-training AI

M. Burlaka
SoftInWay, Inc., Massachusetts, United States

Poster stand number: W107

Keywords: AI, Compressor, Turbofan Engine, Efficiency, Reliability

NASA, DOD, other departments, and companies are looking for improvement in aeropropulsive power density and efficiency in support of its Strategic Thrust in the area of Ultra-Efficient Subsonic Transports, focusing on small core turbofan engines for next-generation and future large commercial transport aircraft. The trend in the design of modern gas turbine engines is for ever-increasing cycle efficiency and reduced specific fuel consumption. To achieve these engine cycle efficiency goals, the low and high-pressure compressors (HPC) are pushed to ever-increasing levels of pressure ratio. Increasing levels of compressor pressure ratio results in higher rotor tip relative Mach number in the HPC front stages, and consequently steeper performance characteristic maps. The compressors with steep characteristics typically require variable geometry inlet guide vanes as well as variable stators in the first few stages to provide the desired performance and stability in an engine system. The design and development time of a modern high-pressure compressor with variable geometry can take years of design-build-test iterations. Determining the optimal combination of vane angle resets that will provide the desired compressor performance in an engine system environment is a time-consuming and expensive part of the development of high-pressure compressors.