Small jet engine reservoir computing digital twin
This provides a digital twin for jet engine monitoring and control, but is incremental as it applies an existing method to a new domain.
The researchers created a digital twin of a jet engine using a next-generation reservoir computer, achieving prediction accuracy better than 1.8% on unseen test data.
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.