Small jet engine reservoir computing digital twin
C. J. Wright N. Biederman B. Gyovai D. J. Gauthier J. P. Wilhelm
Machine learning was applied to create a digital twin of a numerical
simulation of a single-scroll jet engine. A similar model based on the insights
gained from this numerical study was used to create a digital twin of a JetCat
P100-RX jet engine using only experimental data. Engine data was collected from
a custom sensor system measuring parameters such as thrust, exhaust gas
temperature, shaft speed, weather conditions, etc. Data was gathered while the
engine was placed under different test conditions by controlling shaft speed.
The machine learning model was generated (trained) using a next-generation
reservoir computer, a best-in-class machine learning algorithm for dynamical
systems. Once the model was trained, it was used to predict behavior it had
never seen with an accuracy of better than 1.8% when compared to the testing
data.