The William E. Boeing Department of Aeronautics & Astronautics is pleased to announce the 2022 cohort of students honored by our department-wide Student Excellence Awards.

These awards recognize outstanding contributions from students in three critical areas of academic work – Research, Teaching and Service. Students were nominated by faculty, staff, supervisors, peers and students. The recipients each receive an award of $1000.

Meet the 2022 recipients of the A&A Student Excellence Awards!

Doctoral level: Abhiram Aithal (PhD Candidate)

Abhiram’s new solver FastRK3 (Journal of Computational Physics 2020) is the first numerical solver of the Navier-Stokes equations for viscous flows over curved walls that accurately reproduces experimental results. FastRK3 is an important contribution to the field of fluid mechanics and aeronautics since it now allows us to perform Direct Numerical Simulations (DNS) for turbulent flows over curved ramps similar to a fuselage and explain the physical mechanisms of flow separation via the investigation of the turbulence kinetic energy budgets over the ramps. This opens opportunities to improve modeling and design in aerodynamic shapes such as fuselage and wings.

FastRK3 relies on an incredibly elegant mathematical formulation of the Navier-Stokes equations written in curvilinear coordinate which is used in general relativity. It allows accurate solutions via a third-order Runge-Kutta time integration scheme which is fast by relying on FFT-based Poisson solver and is required only once per time step (vs three times in the past) while preserving the second order temporal accuracy for wall-bounded flows (and third-order for free-shear flows). Abhiram is currently working on explaining the physical mechanisms occurring in separated turbulent flows by performing DNS of a separating turbulent boundary layer and by analyzing the budgets of turbulence kinetic energy and Reynolds stresses. This will be the first DNS study to explain the dynamics of wall-turbulence in the presence of a large separation region.

Masters level: Jeremy Brockmann (MS, 2022)

Jeremy's research addresses a very important question in modern composite aerostructures: what is the effect of crack-parallel stress on the failure process? For more than 100 years, parallel stresses have been neglected in fracture mechanics but novel results suggest that they might be important.

Combining novel experimental techniques and computational modeling, Jeremy showed for the first time that crack parallel stress can reduce the capacity of the structure of resisting fracture, making it dangerously weaker than expected. He showed that traditional cohesive zone models used in current structural design are not equipped to capture this phenomenon. He demonstrated that fully tensorial formulations are needed to capture the effects of parallel stresses. By taking advantage of the Crack Band Model, Jeremy was able to predict the behavior of composite structures subjected to crack parallel stress and he formulated design guidelines and recommendations to mitigate their effects.

Thanks to Jeremy's work, we will be able to develop more accurate models and certification guidelines which will ultimately lead to safer and more damage tolerant composite aerostructures.

Undergraduate level: Alexander Scott Javor (BS, 2022)

In addition to his outstanding academic standing, Alexander is a very curious student who has worked in the Laboratory for Multiscale Analysis of Materials and Structures helping with mechanical testing and morphological analysis of Discontinuous Fiber Composites (DFCs). DFCs are composites made of randomly oriented strands of carbon fibers. The use of strands makes the manufacturing more efficient and cost-effective. However, it also makes the mechanical behavior significantly more complex and difficult to predict. Alexander is helping the group get a better understanding of the link between strand morphology (e.g. strand orientation, thickness, width) and overall mechanical performance. He is sectioning hundreds of samples of material and analyzing them under the microscope to characterize the material morphology. To do so, Alexander wrote a very sophisticated image recognition software in Matlab that can identify porosity and fiber orientation automatically.

Thanks to Alexander's code, the time required to perform an analysis went from one day to a few minutes, and the team will be able to significantly increase the analysis output and develop novel models to correlate morphology and performance.

Shahriar Talebi (PhD Candidate)

For excellence in teaching and dedication to students.

This quarter (Spring 22), Shahriar taught AA 597 Networked Dynamical Systems involving consensus algorithms, graph theory, and the application of these concepts in control theory. The homework was fun, informative, and offered the right amount of challenge. Shahriar is an excellent lecturer. He answered any question and always gave additional insights. During office hours, he always helped each student and would sit with them for long periods of time to help them understand a problem or and assist them in their final project. Students genuinely had a lot of fun doing their final project and utilizing the various concepts they learned in class, not an easy feat!

Michael Gabalis (BS Candidate)

For service to the department through his leadership and mentorship of SARP and the A&A undergraduate community.

Michael is a perfect example of a great A&A community member. He is often speaking or working with others in the community, taking active interest in other people’s projects and questions. He is a great leader in SARP, actively encouraging his members to step up and innovate. When he could have easily laid back and coasted on the previous designs, he instead decided to push for a radical and advanced new ignition concept which will likely be critical in the feasibility of SARPs ambitions. Most of all, Michael’s leadership and mentorship of SARP and the A&A community members is truly remarkable and he has improved the A&A experience for many undergrads.