Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

Tong, Michael T

关键词: TURBOFAN ENGINES;    ALGORITHMS;    DATA BASES;    ENGINE DESIGN;    AIRCRAFT DESIGN;    LESSONS LEARNED;    MACHINE LEARNING;    NEURAL NETWORKS;    RISK ASSESSMENT;    NASA PROGRAMS;   

RP-ID  :  NASA/TM-2019- 220307,E-19727,ASME GT2019–91432,GRC-E-DAA-TN71886

美国|英语

来源: NASA Technical Reports Server

【 摘 要 】

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and data science, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.