Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models
could drive a high level of efficiency in aircraft engine
conceptual design. Aero-Engines AI is a user-friendly app that
has been created to deploy trained machine-learning (ML)
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 ML application as an executable file
which can be run on Windows machines (without the need to
have Python or any library installed). The app gets user input
for a turbofan design, preprocesses the input data, and deploys
trained ML models to predict turbofan thrust specific fuel
consumption (TSFC), engine weight, core size, and
turbomachinery stage-counts. The ML predictive models were
built by employing supervised deep-learning and K-nearest
neighbor regression algorithms 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 ML
models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space
exploration during the conceptual design stage. Current version
of the app focuses on the performance prediction of
conventional turbofans. However, the scope of the app can
easily be expanded to include other engine types (such as
turboshaft and hybrid-electric systems) after their ML models
are developed. Overall, the use of a machine-learning app for
aircraft engine concept assessment represents a promising area
of development in aircraft engine conceptual design.