INTENSE REU Research Projects
The research project summaries presented here are representative of the work REU participants will perform at New Mexico Tech. Students are encouraged to identify specific projects of interest during the application process, and acceptance notifications will indicate the faculty mentor and general project the acceptance offer is relative to. The specific projects students will complete during the summer may vary from these descriptions as the research path and interests evolve.
Applications are accepted starting December 1, 2021! APPLY HERE: HTTPS://WWW.NSFetap.ORG/
Faculty Mentor: Dr. Michael Hargather, Associate Professor of Mechanical Engineering
Post-explosion environments are fluid-dynamically complex, involving turbulent mixing between ambient air and explosion product gases, shock and expansion wave propagations, and interactions of solid particles from unexploded material or structural cases. Characterizing and understanding the turbulent environment and how it scales with or is dependent on explosive material and size is important for development of computational simulations for explosions and understanding the complicated post-blast environment. In this INTENSE REU project the student will use the NMIMT Tunnel for High-speed Optical Research (THOR) with high-speed schlieren imaging to study shock wave and product gas propagation from small-scale explosions as shown here in the schlieren image of a 500 mg explosion. The student will develop MATLAB image processing algorithms to extract turbulent structure size statistics and compare turbulent quantities between explosions of different sizes.
Faculty Mentor: Dr. Mostafa Hassanalian, Assistant Professor of Mechanical Engineering
Faculty Mentor: Dr. Jamie Kimberley, Associate Professor of Mechanical Engineering
Intelligent microstructural materials-by-design approaches provide a new pathway to achieving materials that are resistant to dynamic fracture/spall failure. The resulting materials will provide a realization of blast/impact resistant structures that will provide improved protection of people and critical infrastructure exposed to extreme dynamic environments. A critical component for the development of these advanced materials systems is an improved understanding of the active failure mechanisms. For this project, the REU student will investigate the effect of microencapsulated viscous liquid inclusions on the dynamic fracture response of polymer specimens. The image here shows the viscous microcapsule/polymer composite with existing crack. As the crack extends it ruptures the fluid-filled microcapsules, resulting in a viscous bridging traction applied to the crack faces as the crack propagates. The bridging traction tends to reduce the crack tip stress intensity factor, providing an increase in apparent facture toughness when the crack is growing dynamically. The REU student will conduct dynamic fracture experiments utilizing high speed video coupled with quantitative optical techniques (e.g. digital image correlation (DIC)) to characterize the fracture parameters, such as the dynamic stress intensity factor and crack growth rates.
Faculty Mentor: Dr. Michaelann Tartis, Associate Professor of Chemical Engineering
Mechanisms involved in primary blast-induced traumatic brain injuries that plague our returning military servicemen remain poorly understood. Materials to simulate tissues of the cranium are needed to produce models that are readily reproducible in blast studies. Test objects that are both biofidelic and reproducible provide the opportunity to investigate dominant mechanisms at varying blast parameters. Fabricating materials that are transparent allow for optical diagnostics with the material during the blast event, such as PIV and DIC. Using tissue simulants, it may be possible to reproduce post-mortem diagnostics used in the clinic for adequate comparison of the observed injuries. The mechanisms elucidated from these studies may be used to inform the design of protective gear to mitigate blast injuries. The REU student working on this project will focus on performing mechanical and material characterization of potential biofidelic simulants for skull, vasculature, grey and white matter using both conventional materials characterization and newly developed microscopy techniques.
Faculty Mentor: Dr. Chelsey Hargather, Assistant Professor of Materials Engineering
Solid composite propellant rocket motors are traditionally fabricated using a cast and cure method with formulations involving an oxidizer such as ammonium perchlorate (AP) and a binder such as hydroxyl-terminated polybutadiene (HTPB). As lead times and costs for traditional motors have risen, a need to change the way solid rocket propellants are formulated and manufactured has developed in the industry. Adapting additive manufacturing techniques for manufacturing solid composite rocket motors involves research into propellant and binder formulations that are compatible with these techniques. In this INTENSE REU project, the student will perform preliminary feasibility testing of alternative fuel-oxidizer-binder combinations to be used in an additive manufacturing process. The student will also assist in the creation and burn testing of miniature rocket motors.
Faculty Mentor: Dr. Curtis O'Malley, Assistant Professor of Mechanical Engineering
Faculty Mentor: Dr. Kooktae Lee, Assistant Professor of Mechanical Engineering
Distributed networked control schemes are an area of active research and implementation in manufacturing plants, power grids, autonomous vehicles and robots. This trend is due to rapid advancements of technologies including computing power, embedded platforms, and communication networks. In this INTENSE REU project, the student will learn the basics of networked control systems and develop cooperative control algorithms for a multi-agent robot platform such as the one shown here. The student will learn how to intelligently control multiple robots to successfully achieve a given cooperative-control mission. This project will expose the student to broad experiences in mechanics, electrical circuits, communications, control, and programming, which will eventually improve student’s overall knowledge and understanding of control systems.
Faculty Mentor: Dr. Arvin Ebrahimkhanlou, Assistant Professor of Mechanical Engineering
Computer vision (CV) and artificial intelligence (AI) are fast-growing fields of research that are transforming several other fields, such as robotics. Specifically, CV has enabled robots to “see” their surrounding environment, and AI has made them capable of “thinking” about their interaction with the environment. As the interest in intelligent and autonomous systems is ever increasing, new applications for such systems need to be explored and investigated. Examples of such new applications include condition assessment of civil infrastructures, maintenance of aerospace systems, and support and rescue in underground mines. In this INTENSE REU project, the student will learn basic CV and AI algorithms, such as object recognition and path planning, and implement them on ground or flying robots. In particular, the objective is to adapt such algorithms to new applications mentioned above. The student will be embedded in the NMT Visual and Acoustic Monitoring Laboratory.
Faculty Mentor: Dr. Michael Hargather, Associate Professor of Mechanical Engineering
Flame propagation through a reactant mixture of powders is fundamentally one way to characterize the energy transfer and generation behavior of a mixture. Flame speeds can be measured by loading powders into an open ended transparent tube and igniting the powder at one of the tube using a hot wire or other igniter. High speed imaging cameras are used to monitor the progression of the leading edge of light as the flame propagates through the tube. In this INTENSE REU project the student will design and construct a flame tube apparatus that will enable consistent ignition and measurements of flame speeds. The apparatus will need to include a housing that will allow a powder filled tube to be constrained during ignition and combustion experiments, and allow viewing of the tube throughout the reaction. High speed cameras will be used to capture the flame dynamics throughout the ignition and combustion process. The design will then be tested using standard mixture formulations ignited with a hot wire. The student will develop MATLAB image processing algorithms to extract information on flame propagation behavior, such as planar versus stochastic flame fronts and transition to stead state propagation.
Faculty Mentor: Dr. Andrei Zagrai, Professor of Mechanical Engineering
Monitoring spacecraft and satelites for damage is critical to ensure performance and stability throughout launch, deployment, and the spacecraft lifetime. Non-destructive structural health monitoring techniques offer unique opportunities to perform real-time assessment of structure status. This project will build on Dr. Zagrai's ongoing work with NASA to assess spacecraft health in orbit and on sub-orbital launch platforms. The student will develop programming algorithms to extract structural health metrics from acoustic measurements in space structures. The student will work with existing data and ground-based test platforms to develop new characterization methods for in orbit measurements.