Session: 01-01 Conceptual Design and Optimization I

Paper Number: 102024

102024 - Aero-Engines Ai - a Machine-Learning App for Aircraft Engine Concepts Assessment 

Effective deployment of trained machine-learning models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a Windows app that has been created to deploy trained machine-learning models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of machine-learning application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). Current version of the app focuses on the performance prediction of conventional turbofans. The app gets user input for a turbofan design, preprocesses the input data, and deploys trained machine-learning models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The machine-learning predictive models were built by employing supervised deep-learning algorithm to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these machine-learning models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage.

Presenting Author: Michael T. Tong NASA Glenn Research Center

Presenting Author Biography: Michael joined NASA Glenn Research Center in 1997. He is an aerospace engineer in the Propulsion Systems Analysis Branch at NASA Glenn Research Center. He has over thirty years of experience in propulsion systems, thermal, and structural analyses for aerospace and space applications, which have included aircraft engines, space-shuttle main engine, and nuclear propulsion system. Prior to joining NASA, he held senior engineering positions at TRW and Babcock and Wilcox. He received a Masters in Mechanical Engineering from Case Western Reserve University.

Authors:

Michael T. Tong NASA Glenn Research Center