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Research Opportunities

Mechanical Engineering Research opportunities

If you are interested in one of the opportunities below, please reach out directly to the faculty member listed below each project.

Design, Fabrication, and Implantation of Custom Brain Machine Interfaces

  • Fully funded research assistant
  • Two years funding starting Spring 2021
  • Ph.D. students preferred but MS students with outstanding background will be considered
  • Contact Prof. Lei Chen by email: Lei_Chen@uml.edu

The goal of this project, in collaboration with a neuroscience team, is to develop a universal and 3D-printed rat calvarium replacement system to enable a novel brain-machine interface for brain-wide electrophysiological signal recording and stimulation. The student researcher in this position will be responsible for computer-aided design and manufacturing of the microelectrode system and the 3D-printed interface plate to allow implantation of complex and customizable apparatus into the rat brain. This includes conducting the engineering research on custom medical device design and fabrication (including additive manufacturing, precision machining, and molding), cutting mechanics and buckling analysis of microelectrode penetration of the brain membranes, and working closely with the neuroscience team to identify the needs and advance the engineering design and implantation methods.

Transport Properties Analysis of Composite Membrane Reactor

  • Hourly-paid Research Assistant
  • 10-20 hours a week during the semester, full time during the summer
  • One to Two years
  • Contact Prof. Xinfang Jin by email: Xinfang_Jin@uml.edu

In this project, the goal is to use multiscale modeling tool to guide the microstructure design of the new membrane reactor. The student will study the SEM or CT images from experiments, use statistical homogeneity method to characterize the microchannel, select the representative volume element (RVE) accordingly, and apply random walk diffusion on the RVE domain to obtain homogenized microstructure parameters, such as porosity ɛ, tortuosity τ, volume ratio ϕ. The mean and standard deviation of each parameter should be obtained and input to the continuum-level model. To mitigate the computational effort, the 3D images will be processed in software ImageJ and exported to Comsol 5.4 for diffusion analysis. The perfect candidate should demonstrate an excellent capability in numerical methods and programming.

Strength Analysis of Carbon Fiber Composites

  • Undergraduate Research Assistant (hourly rate dependent on experience)
  • 10-15 hours a week during the semester, full time during the summer
  • Fall 2020
  • Contact Prof. Stapleton by email: Scott_Stapleton@uml.edu

The positions are mainly supporting graduate students with their projects. Unless specified, tasks require normal MECH knowledge and general awesomeness: responsibility, pride in your work, grit, that sort of thing. This project is perfect for students who would be interested in eventually doing an internship with NASA or the Air Force, or would be interested in eventually applying for fellowships for graduate school. Positions needed:

  1. Experimental: chemical formulations, machining, polishing, running testing equipment, etc.
  2. Simulation: run large models on commercial software, including interfacing with a high performance computing cluster (training provided).
  3. Software development: requires existing proficiency in programming and the ability to design, implement, test, and maintain engineering software.

Machine Learning based Optimal Sensor Placement for Aerospace Applications

  • Research Assistant (fully funded)
  • Fall 2020
  • PhD students are preferred, but MS students with outstanding background will be considered.
  • Contact Prof. Murat Inalpolat by email: Murat_Inalpolat@uml.edu

The student in this position is expected to work on developing and testing machine learning algorithms for optimal sensor placement. The algorithms developed will be applied on a variety of applications related to vibrations, acoustics, and other related fields. Matlab/Python will be used to develop supervised and unsupervised machine learning algorithms for damage detection from aerospace structures and related applications using minimum number of optimally-placed sensors. The ideal candidate will have a strong interest in machine learning, probabilistic approaches, optimization, structural health monitoring, algorithm development and coding. Fluency in Matlab (and/or Python), and motivation to learn other modeling and analysis tools is preferred. This position requires a high GPA, and satisfactory GRE and TOEFL scores (International students only), work ethics, integrity, motivation and collegiality.

Analytical and Computational Aeroacoustics for Structural Health Monitoring of Operational Wind Turbine Blades

  • Research Assistant (fully funded)
  • Fall 2020
  • Ph.D. students are preferred, but MS students with outstanding background will be considered.
  • Contact Prof. Murat Inalpolat by email: Murat_Inalpolat@uml.edu

The researcher in this position will develop and use analytical/computational aero-acoustics simulation tools to assess wind turbine blade damage using acoustic measurements/predictions. The computational tools will also be used to optimize the damage detection. Coupled fluid-acoustic and/or high-fidelity predictive tools for aero-acoustics prediction will be considered/developed/used in this research. Analytical and computational modeling will be achieved using tools that will be developed in-house by the student as well as through the use of commercially available tools such as Ansys, Comsol, etc. This student is also expected to help conduct acoustic measurements, analyze and interpret the data collected and help make design decisions related to the acoustic sensing systems. The work is predominantly geared towards wind energy and wind turbine blade structural health monitoring. However, this researcher is expected to continuously learn and help the team with other related work in the near future. The ideal candidate will have a strong interest in scientific computing, aerodynamics and/or aero-acoustics, optimization, structural health monitoring, and wind turbine hardware procurement and testing. Working knowledge of Matlab, and motivation to learn other CFD and acoustic modeling tools, and analytical modeling of acoustic sources and acoustic radiation is preferred. This position requires a high GPA, and satisfactory GRE and TOEFL scores (International students only), work ethics, integrity, motivation and collegiality.

Characterization of novel thermoresponsive fibers for lightweight smart thermal insulation

  • Full research assistant
  • January 1, 2020 – December 31, 2020
  • Ideal candidate is a Master’s level student looking to complete a thesis project
  • Contact Prof. Hongwei Sun by email: Hongwei_Sun@uml.edu

The ultimate goal of the research is to enable the design of fibers with tailored coefficient of thermal expansion (CTEs) for “smart” thermal insulation applications. Fibers with a high CTE are currently being investigated for use in “smart” thermal insulation that adapts to temperature changes. The student is expected to characterize the morphology, structure and CTE of the fibers using techniques such as Differential Scanning Calorimetry, Scanning Electron Microscopy, and ThermoMechanical Analysis and develop apparatus to measure the mechanical and thermal performance of fiber battings for temperature changes ranging from 20 to -50oC. The student will work with researchers from different departments, HEROS and Fiber Discovery Center.

Evaluation of the Mechanical Behavior of Fabric Systems for Use with a Tensile Strain Measurement System

  • 1/2 research assistant for one to two years
  • September 1, 2020 – June 30, 2021
  • Ideal candidate is a Master's level student looking to complete a thesis project.
  • Contact Prof. Alireza Amirkhizi by email: emptyAlireza_Amirkhizi@uml.edu

The student researcher will use and develop experimental tensile and biaxial test equipment to characterize parachute fabrics at both quasi-static and high-speed impact. The student will be expected to gain expertise in the existing setups and work on the development of a new experimental setup. Students may also integrate simulations/models into the research investigation. This project is funded as prat of an NASA SBIR and student will have opportunities to collaborate with industry researchers.