If you are interested in one of the opportunities below, please reach out directly to the faculty member listed below each project.
Strength Analysis of Carbon Fiber Composites
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.
Dynamic Modeling, Analysis and Testing of Automotive Transmission Components and Subsystems
Machine Learning based Optimal Sensor Placement for Aerospace Applications
Analytical and Computational Aeroacoustics for Structural Health Monitoring of Operational Wind Turbine Blades
Acoustics-based Damage Detection from Operational Wind Turbine Blades
The student in this position will develop and test an acoustic sensor network that is needed for damage detection and identification from operational wind turbine blades. This student is expected to have, or motivated to develop, background in acoustics and vibrations, signal processing and machine learning as well as testing and data analysis. The ideal candidate will have a strong interest in data collection, analysis, and algorithm development. Working knowledge of Matlab/Python, and motivation to learn signal processing, structural health monitoring and machine learning algorithms is preferred. This position requires a high GPA, and satisfactory GRE and TOEFL scores (International students only), work ethics, integrity, motivation and collegiality.
Automotive Powertrain Dynamics
Structural Dynamics and Acoustic Systems Laboratory (SDASL) is looking for an undergraduate student to work on development of a user interface for the test setup using LabVIEW and helping a grad student with the experimental studies. This project will be on vibration absorber systems for automotive transmissions and requires skills and interest in (a) LabVIEW, (b) Solidworks, (c) Manufacturing and assembly of fixtures, components and subsystems,(d) Mechatronics (controlling motors etc.).
Stochastic Lagrangian-Eulerian method form the backbone of combustion application spray simulations due to their relatively low computational cost. The numerical errors and convergence properties of these mixed methods, however, has never been formally established, and there is evidence that solution errors may be larger than initially thought. This project focuses on establishing robust error convergence criteria for Lagrangian-Eulerian simulations to be used as a basis for robust error control algorithms to be developed later. This position will involve a significant amount of math and programming.
The student researcher will be responsible for running simulations of sprays using both standard and alternative fuels at conditions defined as part of the Engine Combustion Network (https://ecn.sandia.gov), and comparing simulation results against available experimental results and calibrating model constants as necessary. This student will focus on diesel-like sprays, collaborating with other students working on gasoline-type sprays. Simulations will be run using the CONVERGE CFD software on the Massachusetts Green High Performance Computing Cluster (MGHPCC). Help will be available get started on MGHPCC and running CONVERGE simulations. Programming experience needed to plot results.
This project develops new spray simulations of pressure-swirl atomizers for gas turbine applications. The student researcher will be responsible for setting up and running simulations of a research simplex atomizer, comparing simulation results against experimental data and tuning simulation parameters to improve the match. Simulations will be run using the CONVERGE CFD software on the Massachusetts Green High Performance Computing Cluster (MGHPCC). Help will be available get started on MGHPCC and running CONVERGE simulations. Programming experience needed to plot results.
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.
The student researcher will use computational tools to understand how fluid-structure interaction impacts the generation, evolution and persistence of leading edge vortices in bio-inspired flight. The student will be expected to run existing computer code, as well as develop new computer code. Students may also integrate control into the simulations/models to determine whether wing structural compliance reduces controller complexity. Opportunities exist in this project to collaborate with researchers at other universities.
The student in this position is expected to work on optimizing the military helmet designs by applying machine learning approaches. The ideal candidate will have a strong interest in developing software tools using Matlab and/or Python. This position requires a high GPA, work ethics, integrity, motivation and collegiality.
The student in this position will develop and test sensor placement and optimization related signal processing and machine learning algorithms to be used for damage detection and identification from operational full scale wind turbines. This position requires a high GPA, work ethics, integrity, motivation and collegiality.
The researcher in this position will help collect, analyze and interpret data (acceleration, pressure, and project-related physiological signals) from laboratory tests that the team will collect and leverage in developing Matlab based algorithms that will enable improved exoskeleton controls. The work is predominantly geared towards testing, analysis and interpretation of the results from the tests at the NERVE center, and at the SDASL. 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 data collection, analysis, and algorithm development. Working knowledge of Matlab, and motivation to learn signal processing, structural health monitoring and machine learning algorithms is preferred. This is an hourly position, and has flexibility to accommodate candidate’s classes, however candidates with flexible working hours are preferred. The candidates can potentially become a graduate student later on, which requires high GPA, work ethics, integrity, motivation and collegiality.
The researcher in this position is expected to work on data collection, processing, analysis and interpretation in the laboratory environment. The work is predominantly geared towards structural health monitoring of aerospace structures. 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 structural health monitoring, algorithm development and hardware procurement and testing. Working knowledge of Matlab, and motivation to learn signal processing, structural health monitoring and machine learning algorithms is required. This is an hourly position, and has flexibility to accommodate candidate’s classes, however candidates with flexible working hours are preferred. The candidates can potentially become a graduate student later on, which requires high CGPA, work ethics, integrity, motivation and collegiality.
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.
The student researcher will develop a wireless sensor system (embedding a radar and an inertial measurement unit, IMU) to measure the relative position (i.e., distance and tilt/pitch/yaw angles) between a pair of cameras installed on two remotely paired unmanned aerial vehicles (UAVs). The student will be expected to refine an existing sensor board, integrate the radar unit and the IMU on a sensor board, develop computer codes for the sensors controls/operations, as well as run laboratory tests to characterize the accuracy of the developed system. The student may also integrate computer vision and digital image correlation algorithms to combine the position calculated from the sensor system with the pictures taken from the cameras to perform triangulation of the recorded images resulting in quick photogrammetry measurements of large areas, structures, and infrastructure. Opportunities exist in this project to continue as a doctoral candidate after the end of the first year.
To enable a future based in renewable electricity, electric power has to be efficiently directed to produce the material transformations that fuel society. Plasmas – electrically-conducting gases formed by electrical discharges – are ideal means to produce such reactivity, as amply exploited in varied applications in materials, manufacturing, and energy. An especially important research frontier is the interaction of plasma with liquids, encountered in novel applications in materials and chemical synthesis, environmental remediation, food processing, bioengineering and medicine.
The project consists on the development of a Computational Fluid Dynamics (CFD) model of plasma-on-liquid interactions. The model will be based on the TPORT code developed at UMass Lowell’s Re-Engineering Energy Laboratory (REng|Lab). TPORT is a C++ massively parallel in-house code that is been used in a wide range of fluid flow problems, especially plasma flows.
The rapid increase in atmospheric greenhouse gases, especially CO2, represents one of the most pressing existential threats faced by humanity. The conversion of CO2 and other low-value feedstock into high-value compounds has the potential to mitigate environmental emission while fulfilling the global needs for fuels and chemicals. The Re-Engineering Energy Laboratory (REng|Lab) at UMass Lowell has developed novel approaches that combine concentrated solar and electrical energy for the sustainable conversion of CO2 and other chemicals into valuable products.
The project consists on the investigation of solar-enhance plasmachemical conversion of CO2, water, methane, and other gases into fuels and other valuable products.
The student researcher will run 0D and 1D combustion simulations to generate training data for a neural network-based tabulation method for chemical kinetics calculations. The student will also help train the neural network, and evaluate network performance. This project will require significant programming in Python using the Cantera and TensorFlow libraries for chemical kinetics and machine learning calculations. The student will use the Massachusetts Green High Performance Computing Cluster (MGHPCC) for all calculations.
To guide the engineering design of Potential Driven Plug Flow Membrane Reactors, we will develop a high-fidelity multiphysics model to simulate reactor’s behavior under practical operating conditions and predict PFMRs’ optimal performance. The model will couple transmembrane electrochemical CO2/O2 co-transport with surface catalytic chemical reactions. Opportunities exist in this project to collaborate with researchers at other universities.
The objective of this project is to develop multiscale Multiphysics models to understand the correlation between the electrochemical performance of the bilayer oxygen evolution electrodes and their degradation mechanisms, identify delamination and fracture failure modes, and plan mechanical failure mitigation strategies. Opportunities exist in this project to collaborate with researchers at other universities.