MATH-DT: Advancing Digital Twins for Jet Engines Through Mathematical and Computational Innovation

This project advances predictive digital twins of jet engines. The digital twin is a virtual object representing the jet engine and will be used to inform decisions such as design, optimization, operation and maintenance. This is crucial to enhancing aircraft safety whereby the digital twin enables a proactive approach to identifying and resolving potential issues, and to scheduling preventive maintenance. The digital twin allows a better understanding of the physical behaviors that an engine would exhibit under many operational scenarios, some too dangerous for physical experimentation. The research directly engages undergraduate students through teaching laboratories and a more in-depth engagement program targeted toward students from underrepresented and under-served groups in engineering. The project also involves training of doctoral students in Computer Science, Mathematics, and Engineering. The overarching goal of this project is to expand the mathematical foundations of digital twins with application to jet engines, and to increase their predictive simulation capabilities by fusing information from advanced modeling and state-of-the-art measurements. It develops high-fidelity multiphysics models for jet engine combustion and flow, as well as scalable reduced-order models, both with quantified uncertainties. Particle-surface interaction models are constructed to quantify erosion and deposition effects on engine performance. An array of novel hierarchical data assimilation algorithms are developed using variational approaches, ensemble Kalman filters, and transport map particle filters, all in the context of a hierarchy of models. An innovative experimental setting at the Virginia Tech Advanced Propulsion and Power Laboratory with a JetCatP100-RX engine allows testing with the physical twin. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.