There are four specific projects recruiting students at this time.
- Integrating machine learning with experimental mechanics
- Using machine learning for time integration methods
- Experimental methods to characterize the nonlinear dynamics of structures with frictional interfaces
- Wave propagation and shock mechanics experiments.
In addition to these projects, there is also a strong need in the lab for new students with expertise in design/manufacturing and experimental methods.
The primary set of research projects in the Tribomechadynamics Lab over the next few years include:
- Data-driven videographic methods
- Wear modeling
- Debonding of multi-material interfaces
- Static and dynamic failure of conventional and additively manufactured components
- Thermal-mechanical modeling and experiments
- In situ measurements of frictional behavior
- Numerical methods involving generalized complex number theory
- Joint mechanics/tribomechadynamics (of course)
For all prospective applicants, a few resources may be of interest: