This $769,412 federal Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research to advance predictive digital twins of jet engines. The key products and services to be delivered include: Developing high-fidelity multiphysics models for jet engine combustion and flow, as well as scalable reduced-order models with quantified uncertainties. Constructing models for particle-surface interactions to quantify erosion and deposition effects on engine performance. Designing novel hierarchical data assimilation algorithms using variational approaches, ensemble Kalman filters, and transport map particle filters. Establishing an experimental test facility at Virginia Tech's Advanced Propulsion and Power Laboratory to validate the digital twin models against a JetCatP100-RX engine. The project also includes training programs to engage undergraduate students, particularly from underrepresented groups, as well as doctoral students in computer science, mathematics, and engineering. No sub-awards are planned for this 3-year award, which begins on Sep 1, 2024.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $769.4k | 8/9/24 |