Published: Dec 29, 2021 by Daning Huang
For the coming AIAA SciTech 2022 conference we are able to complete and submit five papers spanning over a range of research fields, with our collaborators. Here are the summaries:
- Aravinth Sadagopan, Daning Huang, Adam Jirasek (USAFA), Jürgen Seidel (USAFA), Anshuman Pandey (SNL), and Katya M. Casper (SNL)
- This is a major collaborative effort between the experimental group from the Sandia National Lab and the computational groups from PSU and US Air Force Academy.
- We studied the coupling between the thermoelastic response and shock wave/boundary layer interaction over a cone-slice-ramp configuration in hypersonic flow, validated the results against experimental measurements, and revealed things not available in experiments.
- Here are the 10-min slides for this paper.
- Carlos Vargas Venegas, Daning Huang
- In this study continued from last year, we extended the physics-infused reduced-order model for the aerothermal load prediction that admits infinite dimensional parametric and control inputs, which makes it very amenable for applications in multidisciplinary optimization and optimal control applications.
- Here are the 10-min slides for this paper.
- Yin Yu, Desirae Major, Daning Huang, Sven Schmitz
- This is our first paper on the application of graph neural networks to the modeling a complex dynamical system on a graph – network of vortex particles. This case is particularly challenging as the graph itself is evolving over time. But, the initial results look promising!
- Here are the 10-min slides for this paper.
Geometric Design of Hypersonic Vehicles for Optimal Mission Performance using Machine Learning
- Brian Coulter, Zhenbo Wang, Daning Huang
- With our collaborators from U. of Tennessee, Knoxville, we continued the study from last year and explored the possibility of coupling the mid-fidelity aerodynamic shape optimization of hypersonic vehicles and the flight trajectory optimization. We believe this is how such advanced vehicles should be designed in the future.
- Damien Guého, Puneet Singla, Daning Huang
- A unified and automatic framework was developed to discover governing equations underlying an unknown dynamical system from data measurements, and this technique is applied to develop thermoelastic reduced-order models in various aerothermoelastic problems.