Student Profile

Amjad Aman

Mechanical Engineering PhD student Amjad Aman won first place in poster presentations at MAE Research Day Spring 2015. He was selected to be a student at the 16th National School on Neutron and X-ray Scattering, Summer 2014. Aman’s research, along with his advisors Dr. Nina Orlovskaya and Dr. Yunjun Xu, is focused on solid oxide fuel cells. These fuel cells are versatile, scalable, and efficient; they can be used as energy conversion devices for vehicles or stationary power generators. Aman and his research group are studying the phase transition in cathode material used in solid oxide fuel cells under temperature changes and the effects of creep using in-situ neutron diffraction, X-ray diffraction and Raman spectroscopy. They're also developing an educational software on fuel cells that can be used as a teaching tool for undergraduate students. Recently, the team was selected into the I-Corps for Learning program organized by NSF for Summer 2015.

Daniel Geiyer

Daniel Geiyer’s work focuses on improving the operating bandwidth of energy harvesters using piezoelectric materials. Typical piezoelectric energy harvester technology is only efficient at resonance excitation seen in highly specific environments. Daniel and his supervisor, Dr. Jeffrey L. Kauffman, are working on finding a way to broaden the operating bandwidth using nonlinear phenomena to target more dynamic environments for application.

Whereas other researchers in nonlinear harvesting consider the disorder of chaos undesirable, Daniel’s work challenges this misconception by embracing chaotic oscillations and using a low power controller to stabilize a desired trajectory within a chaotic attractor. The flexibility provided through real time updates allows the algorithm to maintain a large displacement orbit across a wide range of excitation frequencies, and ensures a net positive power output of the harvesting system.

An added benefit is that the system model, desired trajectory, and control perturbation can be prescribed solely from a single measured state of the system. This then leads Daniel’s team closer to their goal of creating an ideal harvesting device independent of prior tuning.

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