NASA is 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 provides 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. The proposed technology will include the AI-based multistage axial compressor performance prediction model, which can be easily incorporated in the system analysis tool and reliably predict the performance with high accuracy across the entire operating range of compressor even with multiple variable guide vanes and the capability to restore the compressor geometry based on the limited number of parameters, dramatically reducing the duration of the development of the compressor and the entire engine thus helping to approach true optimal engine performance and reduce the chances of additional expensive design iterations in real-life projects.
The research is closely aligned with NASA Aeronautics programs in the areas of Compact Gas Turbine and Electrified Aircraft Propulsion and will augment the corresponding Advanced Air Transport Technology Project's Technical Challenges. The use of artificial intelligence (AI) for highly accurate axial compressor performance map generation will help to quickly evaluate the performance of the axial compressor, find the optimal guide vanes angles, and obtain its geometry and eventually improve the performance and power density of the engine.
The AI-based performance prediction model and subsequent compressor geometry restoration is in high demand in the companies designing the airbreathing engines and power generation units, as well as in aerospace manufacturers and defense because of the dramatic reduction of the development time and cost of the airbreathing turbo engines and vehicles.