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Undergraduate Programs

Xerox Engineering Research Fellows

2019 Research Opportunities

Mechanical Engineering

Professor Renato Perucchio
Departments of Mechanical Engineering, Biomedical Engineering and Archaeology, Technology and Historical Structures

Project Description

My research and teaching interests are in computational solid and structural mechanics, in the development of engineering practices in antiquity, and in the study of heritage buildings in earthquake-prone areas. Ongoing research projects open to qualified undergraduates are in the structural analysis of historic monumental masonry buildings subjected to earthquake loading.

My principal collaborators are University of Rochester Professor Chris Muir, ME, and Professor Michael Jarvis, History; Professor Christopher DeCorse, Anthropology, Syracuse University; Professors Kodzo Gavua and William Gblerkpor, Archaeology and Heritage Studies, University of Ghana.

Specific objectives for summer 2019:

  1. Determine the constructions history, assess the damage state, and determine the seismic vulnerability of the Elmina Castle, Ghana (1482, Portuguese, Dutch, English). Built in 1482 by the Portuguese Crown, St. Jorge Castle at Elmina is the oldest permanent structure introduced by Europeans in Sub-Saharan Africa. Recognized as a UNESCO World Heritage Site, the Elmina Castle is a monument of extraordinary, unique importance for understanding four centuries of interactions between West Africa, Europe, and the Americas beginning with the late 15th century and culminating with the Atlantic Slave Trade of the 17th and 18th centuries. Furthermore, due to its meticulous planning and continuous restoration, the building itself is the best-preserved and most complete example of European late-medieval masonry construction transplanted in SubSaharan Africa. The overarching goal of this multidisciplinary research is to perform an integrated archaeological, historical, and engineering study of the Elmina Castle using state-of-the-art methodologies and instrumentation. Summer Field School  of several engineering students (two fully supported by Xerox fellowships in 2018). 2018 Field School participants are currently involved in constructing a detailed model using Siemens NX CAD system to serve as the basis for building construction analysis and structural FEM numerical modeling. Xerox Fellows are strongly encouraged to apply for Summer 2019.
  2. Determine the structural response and the conditions for structural collapse for the Frigidarium of the Bath of Diocletian in Rome (298-305 AD) subjected to a horizontal acceleration. This is a gigantic vaulted structure built on unreinforced pozzolanic concrete on which we have already done extensive linear and nonlinear finite element modeling (FEM). We have also applied kinematic limit analysis based on the damaged configurations predicted by the nonlinear FEM models. Since 2010, the Xerox Fellowship has supported six students working on the static and dynamic analysis of Roman concrete vaults, leading to several journal and conference (national and international) publications. Xerox Fellows are welcome for Summer 2019.
  3. Determine the structural response and the conditions for seismic collapse for concrete vaulted structures built by the Maya in the Puuc region of Yucatan, Mexico (Late Classic, ~1,000 AD). Puuc architecture is characterized by the usage of excellent lime concrete with mechanical and physical properties similar to modern Portland concrete. Maya vaulted structures – ranging from relatively small halls inside temples or palaces to large gateway arches – are often incorrectly assumed to behave structurally as corbelled arches, held in equilibrium by superimposed projecting stone blocks. In reality, due to their inner solid concrete core, Maya vaults behave structurally like an elastic continuum, quite similar to Roman concrete vaults. We are beginning a project aiming at investigating the static and dynamic (seismic) response and failure mechanisms of typical Puuc vaults using FEM models and kinematic limit analysis. There are openings for undergraduate participation and Xerox Fellows are welcome in Summer 2019.

Engineering undergraduates participating in these projects are trained in the application of fundamental modeling techniques widely used for research and product development in many areas of modern engineering: solid modeling reconstruction of complex geometries, 3D FEM linear and nonlinear analysis, kinematic limit analysis simulating 2D and 3D collapse mechanism. Students will be encouraged – and guided - to submit conference and journal papers based on their research results. Please contact Professor Perucchio if you are interested in applying.

Professor Jong-Hoon Nam
Department of Mechanical Engineering and Biomedical Engineering

Project Description

Research Project: Mechano-transduction of the inner ear sensory organ

We study the mechano-transduction of the inner ear.  The cochlea, the mammalian hearing organ, turns mechanical stimuli (sounds) into neural signals. The identification of mechanical properties of cochlear sensory cells and tissues is crucial to better understand how we hear (or fail to hear). To measure the mechanical properties, we need to apply calibrated pressures in the order of mPa and measure displacements in nanometers at the frequency of up to tens of kHz.  Students will participate in measuring mechanical responses of artificial and biological microstructures in a micro-fluidic chamber system. Through this project, students will learn how the principles of acoustics, fluid dynamics, solid mechanics, and vibrations are applied to micro-mechanical experiments with biological tissues. Also, students will gain experiences with vibration measurement, imaging, and data acquisition devices.

Professor Hussein Aluie
Department of Mechanical Engineering

Project Description

Research Project Description

There are two components that regulate Earth's climate. These are fluid systems: (1) the atmosphere and (2) the ocean, which can transport heat from the equator toward the poles, thereby maintaining a habitable planet. This project involves working with satellite observations of oceanic flow that has been revolutionizing research in oceanic fluid dynamics over the past decade. The student will have the opportunity to learn and work with Python and/or Matlab, learn about and work with satellite datasets of the global ocean that is collected by NASA, ESA and other space agencies. The primary project objective is to implement novel analysis and diagnostics that our group has been developing on this data to understand some of the intriguing aspects of oceanic flow that is currently the subject of active research in the oceanography community. There is also the opportunity to port this data into different formats, so that it can be incorporated into the "Science on the Sphere" repository.

Professor Niaz Abdolrahim
Department of Mechanical Engineering

Project Description

Research Project Description

Project 1: Nanomechanics deformation of solids D2/T2 at cryogenic temperatures This project intends to examine the plastic deformation of solid hydrogen (D2/T2) at cryogenic temperatures near the triple point (about 19 K), for application in the optimization of capsule fuel targets for inertial confinement fusion. When isomechanical dimensionless groups for D2 are compared to other face-centered-cubic (FCC) and hexagonal-close-packed (HCP) materials, D2 behaves like the FCC and HCP counterparts in just about all aspects of their mechanical behavior, including elastic anisotropy, activation energy, and diffusion. The goal here is to identify the nucleation and dislocation multiplication mechanisms, as they are driven by the constraint of the plastic shell around D2/T2 and the imposed subcooling. We need to study the nanomechanics of dislocation/defect generation in HCP D2/T2, by using molecular dynamics simulations. We specifically are interested in understanding how defects generate and grow during subcooling and growth of ice layer. The students will investigate the effect of cooling rate and identify the role of dislocation activities on the grain boundary formation and phase transformation phenomena during the subcooling process. Through this project, students will learn about the fundamentals of phase transformation, solid mechanics and plastic deformation at the atomistic level. The students acquire skills on using the software LAMMPS for molecular dynamics simulation and they often need to write their own codes for analyzing the raw data.

Project 1: Predicting mechanical properties of nanoporous materials by recognizing microstructure using deep learning methods Nanoporous (NP) materials are 3D structure with randomly interconnected pores and ligaments. Their open-cell structure and high surface area per unit volume offer great potential for applications in electrical, mechanical, and catalytic devices. The performance of NP material in all these applications is connected to the mechanical properties and deformation behavior of the structure during operation. The deformation behavior of NP materials is often controlled by their microstructure. Therefore, it is possible to create NP metals with enhanced mechanical properties by optimizing their microstructure if we understand the structure-property relationship. The goal of the project is to reveal the role of the microstructure on the mechanical properties of NP materials. NP materials are a complex structure with various structural and morphological features: pore size, ligament size, surface area, connectivity, randomness, curvature We need to find the based-level feature that controls the mechanical properties of NP materials. In this project we will use a deep learning method to solve the problem. Through this project, the students will investigate the effects of structural and morphological features and identify their positive/negative effects on the mechanical properties. Students will learn about the fundamental concepts of solid mechanics, probability theory, and essentials in neural network architecture. The students will acquire skills on preparing the training data, using the deep learning tools for implementing the model, training model on GPUs and using visualization methods for interpreting the data.