Enabling Rapid Flight Integration of Advanced Hypersonic Thermal Protection System Materials
- Goal: Develop the scientific underpinning needed to reliably, rapidly, and accurately assess the performance of advanced thermal protection systems (TPS) in hypersonic flight. If successful, reduce the pre-qualification design integration time from about five years to six months, enabling new material solutions that can rapidly impact current national security hypersonic missions.
- Sandia Collaborators (selected): Justin Wagner (PI), Jonathan Murray, John Tencer, David Ching, Emily Dreyer, Ross Wagnild
- Research topics: nonlinear model reduction; surrogate modeling; thermal and ablation modeling; coupled Aerothermal simulations
- Funding source: Sandia National Laboratories’ Grand Challenge Laboratory Directed Research and Development
Data propagation components for the Sandia Parallel Aerodynamics and Reentry Code
- Goal: Develop and improve the adjoint capability in the Sandia Parallel Aerodynamics and Reentry Code in support of inverse problems and design optimization.
- Sandia collaborators: Travis Fisher (PI), Eric Phipps, Jared Crean
- Research topics: adjoint methods; inverse problems, high performance computing
- Funding source: Sandia National Laboratories’ Advanced Simulation and Computing Physics and Engineering Models program.
Projection-based reduced-order modeling for uncertainty quantification (co-PI)
- Goal: This project will improve projection-based reduced-order models (P-ROMs) for forward uncertainty quantification (UQ) and facilitate their adoption by application analysts via integrated and automated workflows. It will achieve this by focusing on the coupling of Pressio, a model reduction software ecosystem, and Dakota, a widely used software for optimization and UQ.
- Sandia Collaborators: Eric Parish (co-PI), Elizabeth Krath, Gianluca Geraci, Michael Eldred
- External Collaborator: Francesco Rizzi (NexGen, Pressio lead developer)
- Research topics: nonlinear model reduction; Uncertainty Quantification; high performance computing
- Funding source: Sandia National Laboratories’ Advanced Simulation and Computing Verification and Validation program.
Enabling Efficient Inverse Solutions in Sierra/InverseOpt Including UQ and ROMs
- Goal: This project will develop methods for solving inverse problems with UQ and ROMs via an integrative approach that teams with the relevant subject matter experts and integrates these research tools with the InverseOpt toolkit in Sierra, thus providing gradient-based optimization tools with UQ and ROM functionality
- Sandia Collaborators: Tim Walsh (PI), Vicente Romero, Drew Kouri, John Tencer
- Research topics: nonlinear model reduction; inverse problems; error-estimation techniques
- Funding source: Sandia National Laboratories’ Advanced Simulation and Computing Verification and Validation program.
Full-Airframe Sensing Technology for Hypersonic Aerodynamics Measurements
- Goal: Develop scientific machine learning approaches to infer the pressure distribution on a hypersonic flight vehicle given internal strain gauge and thermocouple readings
- Sandia Collaborator: Bryan Morreale
- External collaborators (selected): Noel Clemens (UT Austin, PI), Karen Willcox (UT Austin), Julie Pham (UT Austin), Carlos Cesnik (U Michigan)
- Research topics: scientific machine learning; inverse problems; model order reduction; aerothermodynamics modeling
- Funding source: AFOSR/NASA University Leadership Initiative
- Project website: https://fast.ae.utexas.edu/