Faculty Mentor:  Dr. Michaelann Tartis, Professor of Chemical Engineering 

This research study will utilize previously developed hydrogel formulations that match the mechanical properties (stiffness) of human cranial tissues to study the role of cavitation and shear deformation in simulated traumatic brain injuries (TBI). Hydrogel formulations will be optimized to include tissue mimics for both anisotropic white matter properties and isotropic grey matter properties, for both optical and acoustic imaging during an impact of a cranial phantom in a drop tower apparatus.  Optimization for simultaneous imaging requires these materials to be transparent, contain tissue-like sound scatter, and tissue-matched attenuation. The REU student will acquire images of high-rate loading experiments using high-speed digital cameras and high-frame rate ultrasound.  To understand which impact circumstances may result in cavitation induced injuries in mild to severe traumatic brain injuries, several parameters will be varied: impact energy, head orientation, location of ultrasound transducer, soft tissue models, and 3D printed skull geometries. The student will use MATLAB and ImageJ softwares to extract cavitation event number, location, persistence, and frequency over a given time period. The student will gain experience in acquiring and quantifying image datasets using image processing.  Research outcomes will include new understanding of impact-induced traumatic brain injury conditions and insights toward mitigation methods.

Faculty Mentor:  Dr. Andrei Zagrai, Professor of Mechanical Engineering

The dramatic increase in the number, nature, and operators of space missions in recent years reinforces the need for techniques and methods to assess and qualify the structural integrity of a vehicle or payload through all stages of spaceflight from launch to orbit to re-entry.  NMT has developed structural health monitoring techniques using electro-mechanical impedance of active piezoelectric sensors through undergraduate design teams and graduate research projects.  The developed systems and experiments have been tested on high altitude balloon and suborbital rocket flights.  The student performing research on this project will perform ground-testing of structural health monitoring systems investigating the integrity of structural joints.  The goal of the project is to determine characteristic electro-mechanical impedance signatures of bolted and welded joints in pristine and “damaged” conditions to train intelligent algorithms to detect damage anomalies.  The project will utilize previously developed hardware, as shown in Figure 5, and the student will construct their own test setups for the joint testing.  The student will be exposed to the science of vibrations and signal processing as well as the technology of piezoelectric and acoustic sensors.  Research outcomes include an understanding of system vibrational response change as a function of various damage states including weld failures or loose bolts

Faculty Mentor:  Dr. Tie Wei, Associate Professor of Mechanical Engineering

This REU project integrates computational fluid dynamics (CFD) and artificial intelligence (AI) to optimize heat transfer and fluid flow in applications such as thermal management, aerodynamic design, and renewable energy systems. Students will use CFD tools (e.g., ANSYS Fluent, OpenFOAM) to simulate complex scenarios and apply machine learning techniques (e.g., neural networks, genetic algorithms) to predict and optimize system performance.

Participants will extract key features from simulation data, develop AI models to improve designs, and validate results against additional simulations or experiments. This interdisciplinary project provides hands-on experience in CFD, AI, and engineering optimization, preparing students for advanced research or industry careers.

Faculty Mentor:  Dr. Frank Austin Mier, Visiting Assistant Professor of Mechanical Engineering

This research project focuses on advancing the technique of Background Oriented Schlieren (BOS) imaging by developing and analyzing the effectiveness of structured background patterns. Traditional BOS imaging often employs randomized speckle patterns, but this project explores whether specific color and image patterns can streamline image processing while maintaining or enhancing quantitative accuracy. The REU student will design, fabricate, and test structured backgrounds using various experimental setups to investigate their impact on flow visualization and refractive index gradients. Key tasks include evaluating the clarity, resolution, and quantitative precision of BOS measurements with varying structured patterns under controlled experimental conditions. This work will expose students to optical imaging techniques, image processing software (e.g., MATLAB or Python), and experimental fluid dynamics principles. Research outcomes may contribute to improved BOS methodologies, enabling simpler and more efficient image analysis for applications in explosives engineering, fluid mechanics, and thermal sciences.

Robotics Applied for use in the Inspection of Transportation Structures
The Dept of Civil and Environmental Engineering at NMT is using robots for the inspection of small culverts on the New Mexico State Highway System. The off the self-robots have proven ineffective or deficient based on the demands. This project involves the retrofitting, rehabilitation, and new development of wheeler rovers for given application. Challenges persist
with accurate distance and inclination measurements, versatile power supply, and data size management. The project is ideal for students interested in hands-on robot construction.

AI Data Management Applied to the Inspection of Transportation Structures
The Dept of Civil and Environmental Engineering at NMT is using robots for the inspection of small culverts on the New Mexico State Highway System. Where to collect data and data storage size reduction both have opportunities to use AI to improve inspection practices. This project seeks to use AI, Convolution Neural Networks (CNN) for use in object detection and/or image
classification to assist in robot development and resolve data management issues. This project is ideal for students interested in AI in a developing real-life application.