The Graham-Brady research group focuses on the problem of modeling the effects that material randomness has on structural performance. This material variability plays a key role in localizations that lead to large-scale structural failure. Recent application areas include ceramics and concrete under high-rate loading, characterization error in high-strength metal alloys, composite materials under impact, and lightweight porous materials.

  • Former Ph.D. student and current Postdoctoral Scholar Anindya Bhaduri has been working with collaborators at the University of Delaware and US Army Research Laboratory to develop surrogate models that efficiently represent impact on composite plates. These surrogate models are particularly helpful in developing probability of penetration for the composite layer, as a function of the input microstructure parameters.
  • Prof. Graham-Brady has chaired the annual Mach Conference, focused on materials and structures in extreme environments, since 2013. The conference has a strong focus on collaborative discussion, maintaining a maximum of 4 parallel sessions and <225 attendees. Unfortunately, the 2020 Mach Conference was cancelled due to Covid-19 concerns, but we will be back next year. Hold the date for Mach 2021: April 7-9, 2021 in Annapolis, MD.
  • Explicitly modeling the process of failure and fragmentation in brittle materials presents a major challenge to understanding the performance of these materials. The material undergoes a process of microcrack initiation, growth and coalescence that ultimately leads to fragmentation of the material. This transition from a damaged solid medium to a granular material happens is an unstable transition, which makes it difficult to represent these mechanisms in a larger-scale constitutive model. Ph.D. student Amartya Bhattacharjee and former Postdoctoral Scholar Mehmet Cil are collaborating with others at JHU and at ARL to develop a constitutive model that is capable of capturing this behavior.
  • Characterization of three-dimensional microstructure often serves as the basis for computational models of material response. While three-dimensional characterization techniques have become more sophisticated, there is still error in the resulting microstructural image. Ph.D. student Noah Wade looks at quantifying the error associated with characterization, and the implications of that error in subsequent computational models.
  • In order to understand variability in material performance, many computational models require multiple instantiations of material microstructure. Obtaining these samples experimentally can be very costly, and it is not possible to consider the effects of varying microstructural statistics. Digitally generated microstructures - generation of microstructures that match given target statistic - provide an efficient alternative. These figures show work by Ph.D. student Ashwini Gupta and postdocs Anindya Bhaduri and Audrey Olivier, who are using machine learning to address this challenge for 3D multi-phase material microstructures.
  • The research group had an outing for lunch at Gertrude's followed by a stroll around the Baltimore Museum of Art. Unfortunately, we said good bye to our visiting Ph.D. student Sebastian Geyer (from TU Munich, Germany). Pictured left to right: Sebastian Geyer, Audrey Olivier, Anindya Bhaduri, Amartya Bhattacharjee Noah Wade, Lori Graham-Brady, Ashwini Gupta.

 

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