Dr. John Bennewitz
Propulsion Research Center | jwb0017@uah.edu
Development of a combustion reactor to investigate catalytic enhancement of ammonia burning using copper-based perovskites
Alternative combustion for energy production is of interest to provide increased sustainability and energy security via source diversification. Presently, hydrogen (H2) combustion is an area of interest due to its potential towards reduction of harmful emissions compared to traditional hydrocarbon fuels. However, there are practical challenges with adopting hydrogen due to its storage and transportation as a high-pressure gas, or low-temperature liquid. To combat these transportation and storage issues, ammonia (NH3) has garnered substantial recent attention as a hydrogen carrier fuel. Ammonia is able to be stored and transported as a room temperature liquid under slightly elevated pressure and contains a reasonably high concentration of hydrogen (17.7 wt. %). However, pure ammonia by itself has reduced combustion characteristics including lower flame temperatures, reduced flammability ranges and longer ignition delays than conventional fuels that can potentially increase harmful nitrogen oxide (NOx) emissions. Therefore, this research program aims to implement catalyst enhancement of the fuel for an ammonia-based combustion system; selective catalytic reduction using copper-based perovskites (CuXO3 and CuXxY1-xO3) will be studied within an ammonia-based combustion system for the first time.
The specific goals of this proposed RCEU program entail the sizing and design of an ammonia combustion reactor suitable to serve as a platform for the proposed catalyst combustion experiments at elevated pressure. Additionally, the second goal of the program is to perform preliminary testing of an atmospheric catalyst-enhanced combustion system fueled with the chemically modified ammonia via copper perovskite enhancement.
This proposed RCEU research program is a joint effort with Dr. Natalie Click, who will work with her research group to develop the copper-based perovskite catalyst for use in the catalyst enhanced combustion studies undertaken in this project.
Prerequisites/Requirements: Interested applicants should be upper-level undergraduates pursuing a degree in science or engineering. Previous experience performing research in a laboratory environment in combustion/chemistry related areas is desired, but not necessarily required. As this project will integrate both conceptual understanding and hands-on laboratory work, other previous relevant experiences including design, fabrication, circuit design and system-level automation will contribute to the applicant rankings in the event there are multiple interested students.
Dr. Cheng Chen
Industrial & Systems Engineering and Engineering Management | cc1115@uah.edu
ARCHER: Assured Requirements Change Handling with Explainable Reasoning
Requirements form the foundation of all engineering systems; however, managing engineering changes (ECs) within requirements documents remains a significant challenge, particularly in maintaining compliance with regulatory standards and minimizing human error. The inherent complexity of ECs—often characterized by many-to-many correlations—is a leading cause of project failures, affecting more than 51% of industrial projects. To address this critical issue, this research investigates the integration of retrieval-augmented generation (RAG) frameworks with knowledge representations to structure and constrain the inferencing outputs of large language models (LLMs). The objective is to develop computational tools that enhance requirements representations by reducing hallucinations and improving traceability. Undergraduate researchers will contribute to the design and implementation of the RAG framework, the development of middleware to integrate these innovations with existing SysML software. The anticipated outcomes include building and evaluating a prototype model using established benchmark case studies to improve the reliability and adaptability of requirements documentation. Findings will be disseminated through conference proceedings to advance best practices across the engineering and design communities.
Prerequisites/Requirements: Prerequisites/Requirements: This position is suitable for undergraduate students with a strong interest or relevant background in programming (e.g., Python) and familiarity with data structures (e.g., JSON, XML) and libraries (e.g., LangChain, LlamaIndex, Streamlit). Experience with engineering requirements management is preferred.
Dr. Cheng Chen
Industrial & Systems Engineering and Engineering Management | cc1115@uah.edu
Agentic LLMs for Modeling and Analysis of Engineering Design Change in Complex Systems
Engineering design changes have significant impacts on complex systems such as aircraft, vehicles, and large-scale infrastructure. An initial design modification in these systems can trigger cascading effects across interconnected components and subsystems, often resulting in misinterpretations, late-stage design revisions, and costly schedule delays. Compared to traditional approaches, This study focuses on developing agentic large language models (LLMs) — data-driven models capable of autonomous reasoning, information synthesis, and multi-step task execution—to represent and analyze engineering changes within multimodal datasets. Through this project, the student will gain hands-on experience at the intersection of artificial intelligence, systems engineering, and information science. The research aims to develop advanced digital tools for design traceability and impact analysis, enabling more resilient and responsive engineering workflows in high-consequence industries.
The undergraduate researcher will contribute to several key activities: (1) curating and processing real-world engineering change documentation, such as change orders, requirements updates, and revision logs; (2) developing and refining prompt strategies that enable LLM agents to autonomously extract, categorize, and visualize relationships among design changes, subsystems, and affected requirements; (3) evaluating the ability of agentic LLMs to generate change impact assessments, identify dependencies, and propose mitigation strategies for conflicting changes; and (4) comparing LLM-driven analyses with traditional rule-based or manual approaches to validate their effectiveness and identify any limitations.
Prerequisites/Requirements: Python
Dr. Howard Chen
Industrial & Systems Engineering and Engineering Management | hc0060@uah.edu
Benchmarking Camera-based Navigation Systems
Navigation is the process for establishing a present location and the planning of a route to a future destination within its environment. The navigation problem is an integral area of research within modern robotics. Camera-based navigation systems are appealing given that it is relatively inexpensive, and all necessary sensors are contained within the robot itself (i.e. does not rely on infrastructure modification). Many of the developed algorithms are open source and the algorithms are often benchmarked against standardized datasets, which facilitates ease of replicability and comparison. However, many of the widely cited open-source algorithms, to our knowledge, have not been benchmarked against each other beyond standardized datasets.
The goal of this project, therefore, is to (i) do a brief literature search to identify widely cited open-source vision algorithms, (ii) gather a comprehensive dataset for algorithm benchmarking, and (iii) if time avails, update the relevant algorithms to facilitate use on more up-to-date software stacks.
Prerequisites/Requirements: Basic knowledge of Linux and C++. Knowledge of Robot Operating System would be beneficial.
Dr. Howard Chen
Industrial & Systems Engineering and Engineering Management | hc0060@uah.edu
Systematic validation of a low-cost multi-camera markerless motion capture system
Accurate measurement of human motion is critical for understanding, predicting, diagnosing, and preventing injuries. Human motion is traditionally measured using expensive optical motion capture system (OMC) in a laboratory environment. However, such a system, while accurate, is expensive (tens of thousands of dollars), and requires extensive setup time due to application of reflective markers on the participants. Consequently, OMCs are limited in its operation to traditional laboratory environments. Markerless motion capture systems have been increasingly used by the biomechanics community for motion analysis in naturalistic environments due to its capability to record human movements inexpensively and obtrusively. Markerless motion capture is traditionally accomplished using a single camera due to ease-of-use and computational efficiency. However, this method is prone to obstructions. Recent software packages and hardware advances have provided the capability to inexpensively conduct markerless motion capture with multiple cameras. Our previous RCEU student has successfully developed the capability of using Stereolabs Zed 2i cameras for markerless motion capture using the ZED360 framework to provide an expensive multi-camera markerless motion capture system. The goal of this project is to perform a systematic validation in a study involving human participants using the conventional optical motion capture as the reference.
Prerequisites/Requirements: None. Kinesiology or Industrial Engineering background would be preferred.
Dr. Natalie Click
Mechanical & Aerospace Engineering | nam0015@uah.edu
Investigation of Copper-Based Perovskites for Catalytic N-O Splitting During Ammonia Combustion
Alternative combustion for energy production is of interest to provide increased sustainability and energy security via source diversification. Presently, hydrogen combustion is an area of interest due to its potential towards reduction of harmful emissions compared to traditional hydrocarbon fuels. However, there are practical challenges with adopting hydrogen due to its storage and transportation as a high-pressure gas, or low-temperature liquid. To combat these transportation and storage issues, ammonia (NH3) has garnered substantial recent attention as a hydrogen carrier fuel. Ammonia is able to be stored and transported as a room temperature liquid under slightly elevated pressure and contains a reasonably high concentration of hydrogen (17.7 wt. %). However, pure ammonia by itself has reduced combustion characteristics including lower flame temperatures, reduced flammability ranges and longer ignition delays than conventional fuels that can potentially increase harmful nitrogen oxide (NOx) emissions.
Building off work dating back to 1986, this research will seek to investigate new copper-based perovskites (CuXO3 and CuXxY1-xO3) as novel catalysts for NO decomposition under ammonia combustion conditions. Perovskites are selected as the material of choice for NO conversion due to their abundant catalytic applications in other industries. The Cu-based perovskites for this research will be synthesized utilizing a novel nitrate-free citrate chemistry that avoids the generation of NOx species during synthesis. The perovskite’s advanced crystallographic behavior will then be studied at elevated temperatures and pressures to expand fundamental knowledge of perovskite behavior, phase changes, dealloying, ect. as functions of temperature and pressure. Finally, the catalytic effect of these perovskites on N-O splitting will be investigated in a joint effort with Dr. John Bennewitz for advancing NH3 combustion science and reducing NO emissions from industry.
Prerequisites/Requirements: Gen Chem I and II courses completed
Chemistry, Chemical Engineering, or Mechanical Engineering students
Dr. Natalie Click
Mechanical & Aerospace Engineering | nam0015@uah.edu
Synthesis of Furan-Based Polymeric Materials for Radiation Resistance
Polymers are susceptible to damage from ionizing radiation; however, there are myriad applications where flexible soft materials are needed in environments susceptible to ionizing radiation (example: space, nuclear reactors, medical devices). Benzene rings are known to exhibit increased stability against radiation-induced cleavage; however, other ring structures have not been as widely investigated under radiation environments. Herein, a furan ring (a five-membered structure with one oxygen replacing a carbon atom) polymer will be exposed to ionizing radiation and its response characterized using FT-IR, XRD, and NMR. First polymerization of the furan will be explored utilizing different solvents, catalysts, and monomer ratios. Next, the best-performing polymers (as characterized by their mechanical response and observed molecular weight) will be exposed to a radiation source (accessed through the CAPP laboratory on campus) for a predetermined amount of time. Finally, the polymers will be removed from the radiation exposure, checked for safe handling by CAPP researchers, and analyzed rigorously in my group using FT-IR and XRD. The goal of the project is to map bond breakage in the furan ring as a function of radiation dose and exposure.
Prerequisites/Requirements: Junior standing in Chemical or Mechanical Engineering, or Chemistry
Ideally have completed General Chemistry I and II coursework
Dr. Nicholas Ginga
Mechanical & Aerospace Engineering | njg0008@uah.edu
Stretchable electronics based on electrically conductive liquid metal filled microchannels subjected to uniaxial and multidirectional stretching
Research in the field of flexible electronics has experienced rapid growth in recent years due to their wide range of applications in sectors including consumer electronics, biomedical devices, and the defense industry. An approach demonstrated to create electrical traces with the ability to experience large strains during stretching without failure uses small-scale channels filled with electrically conductive liquids. This project investigates fabricating these microchannels using additive manufacturing of molds for replica molding processes and characterizing the electrical performance of the electrically conductive liquid filled channels subjected to varied mechanical stretching directions.
To create the microchannels, a replica mold is first created using additive manufacturing. A soft polymer is then poured on the mold (PDMS), cured, and then removed from the mold leaving the mold’s pattern in the PDMS surface. A thin PDMS film is plasma bonded to the PDMS part to seal off the channels. Then the channels are filled with gallium based liquid metal. These form the stretchable electrically conductive liquid metal channels. Varied channel geometries will be fabricated including increasing channel width and height (~500 microns to 3 mm) as well as different channel patterns. These patterns include straight, planar coil, serpentine, and new “pinch-valve” features to act as electrical strain switches. The resistance behavior of these channels will be investigated subjected to axial, transverse, and biaxial stretching to understand their strain/resistance behavior. Designs will be compared to see which are more strain-direction sensitive. This will provide design guidelines for wearable sensors, since strain direction sensitivity is desired in some applications and not in others.
This project provides an opportunity for a student to gain research experience in flexible electronics while obtaining knowledge in mechanics, small-scale fabrication, and metrology.
Prerequisites/Requirements: Students applying to this project should be enrolled in the MAE department. There will be a preference to students who have taken MAE370 and MAE211 with a successful semester modeling project, have an interest and aptitude with hands-on fabrication and working with materials, conducting experiments, and demonstrate good communication skills.
Dr. Henrick Haule
Civil & Environmental Engineering | hjh0023@uah.edu
Simulating Traffic Incident Scenes to Improve Roadside Assistance Providers’ Safety
Traffic incidents, including crashes, vehicle fires, disabled vehicles, and roadway debris pose a significant risk to the safety of road users. Roadside assistance providers working adjacent to moving traffic in response to these incidents are at an even higher risk of being struck by vehicles. Measures such as "Move Over" laws have been implemented to protect roadside assistance providers and other incident responders. Despite these regulations, a high number of providers continue to be struck at incident scenes while assisting individuals involved in crashes or vehicle breakdowns. In the U.S. alone, 123 roadside assistance providers were fatally struck while working between 2015 and 2021. It is critical to understand driver behavior at incident scenes, as well as the protective measures utilized by providers to avoid collisions. Immersive and collaborative virtual reality (VR) has the potential to mimic real-world incident scenarios, engaging providers in hazardous situations without actual risk or harm. VR can also facilitate the collection of data to enhance training and evaluate the effectiveness of existing safety strategies. A collaborative virtual environment replicates the dynamic, complex, and unpredictable nature of an incident response scene, including the necessary coordination among multi-agency responders such as law enforcement, fire, and Emergency Medical Services. This project aims to develop a VR environment to evaluate the efficacy of roadside assistance providers’ protection strategies and identify appropriate measures for various scenarios. The project offers a unique opportunity to improve the safety of roadside assistance providers and develop incident management procedures that reduce traffic delays while ensuring the safety of all road users.
Prerequisites/Requirements: Civil Engineering or Computer Science
Dr. Henrick Haule
Civil & Environmental Engineering | hjh0023@uah.edu
Automating Passenger Counts and Mileage Tracking Using Computer Vision
Transit agencies collect data using Automatic Passenger Counting (APC) and Automatic Vehicle Location (AVL) technologies for transit planning, assessing service quality, and identifying operational issues. These data allow agencies to evaluate system performance across multiple metrics, including passenger waiting time, stop-skipping frequency, bus bunching, travel time, on-time performance, and vehicle occupancy. However, APC systems can experience periodic failures due to hardware malfunctions or communication loss, leading to gaps in passenger-miles data. Given the availability of onboard video feeds, it would be beneficial to utilize image recognition to recover data during these intervals and enhance existing APC datasets. This research aims to employ image recognition to automatically count passengers boarding and departing along each route, as well as calculate the resulting passenger mileage. The project involves integrating an image recognition model with a text recognition system to regenerate missing APC data for buses managed by Huntsville Public Transit. Ultimately, this study will demonstrate the potential of computer vision and artificial intelligence to improve data reliability within the public transportation sector.
Prerequisites/Requirements: Civil Engineering
Dr. Haiyang Hu
Mechanical & Aerospace Engineering | hh0084@uah.edu
Adaptive Flow Control Strategies to Improve the Aerodynamic Performance of UAV Airfoil Under the Strong Gust
Unmanned aerial vehicles (UAVs) have revolutionized military operations, offering a multifaceted array of applications across various missions by providing enhanced surveillance, reconnaissance, and combat capabilities in areas where troops are unable to go or are not safely deployed, such as the expanse of the sea area away from the vessels and with strong gusts of wind. Unlike manned flights in clear air, UAV operations at slow speeds and with lighter payload carriers cannot treat wind gusts during storms, severe weather, and atmospheric turbulence as small disturbances, since they can directly cause loss of control of the system. Therefore, it is highly desirable to develop innovative, effective gust alleviation strategies tailored to UAV wing systems to ensure safer, more efficient operation in strong gust conditions. In this project, an experimental study will be conducted to develop closed-loop adaptive flow-control techniques for the UAV airfoil under various gust conditions (i.e., freestream velocity, gust ratio, and AOAs). The UAV airfoil model with various active flow control techniques will be tested at the newly upgraded low-speed gust wind tunnel at the MAE department. The force sensor, pressure measurements, and PIV will be used to experimentally quantify the aerodynamic performance of the airfoil with or without control techniques. While the dynamic aerodynamic force and surface pressure distribution were recorded using the force sensor and pressure transducer, a high-resolution PIV system was also used to characterize the behavior of the air flow over the UAV airfoil model. The detailed PIV flow field measurements will be correlated with the dynamic aerodynamic force data to gain further insight into the underlying flow physics. Then the ML model will be used to develop the flow control strategy under the gust conditions.
Prerequisites/Requirements: Prerequisites/Requirements: 1) Basic understanding of fundamental aerodynamics concepts and related sciences. 2) Basic knowledge and experience in MATLAB, Solid Edge/SolidWorks
Dr. Haiyang Hu
Mechanical & Aerospace Engineering | hh0084@uah.edu
UAV Platform to Quantify the Performance Degradation under various Urban Atmospheric Conditions
Unmanned aerial vehicles (UAVs) have revolutionized military operations, offering a multifaceted array of applications across various missions by providing enhanced surveillance, reconnaissance, and combat capabilities in areas where troops are unable to go or are not safely deployed, such as the expanse of the sea area away from the vessels and with strong gusts of wind. Unlike manned flights in clear air, UAV operations at slow speeds and with lighter payload carriers cannot treat wind gusts during storms, severe weather, and atmospheric turbulence as small disturbances, since they can directly cause the loss of control of the system. An abundance of wind tunnel research has been conducted in the PI's lab to quantify the aerodynamic behavior of the UAV component in the presence of a strong gust. The performance of the integrated UAV system is crucial to verify the wind tunnel testing experiments and is used as a database to develop advanced control strategies. In this project, a lab test and a field study will be conducted to examine the behavior of the UAV system under the urban gust condition. Both quadcopter drones and fixed-wing UAV platforms will be built up with automatic launching and return functions. Then, both the lab and field testing will be conducted to study the performance of the UAV platform under the different gust conditions. The field test will be conducted under the guidance of the FAA regulations for the UAV system. The acceleration, power consumption, and the positioning information will be used to quantify the UAV behavior.
Prerequisites/Requirements: Prerequisites/Requirements: 1) Basic understanding of fundamental aerodynamics concepts and related sciences. 2) Basic knowledge and experience in MATLAB, Solid Edge/SolidWorks. 3) The students with experience in the operation of the RC UAV will be preferred.
Prof. Yu Lei
Chemical & Materials Engineering | yl0022@uah.edu
Atomic Layer Deposition of Advanced Optical Coatings for High-Energy Laser Systems
This project engages an undergraduate student in experimental research on advanced optical coatings for high-energy laser (HEL) and directed energy systems using Atomic Layer Deposition (ALD). ALD is a precision thin-film technique that enables angstrom-level control of material thickness and composition, making it ideal for optical and photonic applications requiring exceptional uniformity and durability.
The student will participate in the design, fabrication, and characterization of thin-film coatings relevant to laser optics. Research activities will span materials synthesis, experimental planning, data collection, and interpretation of results in the context of real-world engineering constraints. The project emphasizes hands-on training, research independence, and professional skill development, providing a strong foundation for students considering graduate study or research-oriented careers in materials science, optics, or defense-related fields.
Prerequisites/Requirements: Applicants must be undergraduate students in good academic standing at The University of Alabama in Huntsville and U.S. citizens due to the nature of the research. Preferred majors include Chemical Engineering, Mechanical Engineering, Physics, Chemistry, Electrical Engineering, or closely related STEM disciplines.
Prior research or laboratory experience is helpful but not required. Successful applicants should demonstrate curiosity, reliability, attention to detail, and a strong interest in experimental research and graduate-level study.
Prof. Yu Lei
Chemical & Materials Engineering | yl0022@uah.edu
Catalytic Destruction of PFAS (“Forever Chemicals”)
This project engages an undergraduate student in experimental research on the destruction of per- and polyfluoroalkyl substances (PFAS), a class of persistent environmental contaminants commonly referred to as “forever chemicals.” The work is part of an active NSF-funded research effort focused on developing catalytic, thermal, and advanced treatment strategies for PFAS remediation.
The student will participate in laboratory studies involving catalyst preparation, reactor testing, and analysis of PFAS degradation performance. Research activities will include experimental planning, data collection, and interpretation of results in the context of environmental engineering and sustainability challenges. The project emphasizes hands-on research training, problem-solving, and scientific reasoning, providing an authentic research experience for students interested in environmental catalysis, clean technologies, and graduate study in engineering or chemistry.
Prerequisites/Requirements: Applicants must be undergraduate students in good academic standing at The University of Alabama in Huntsville. Preferred majors include Chemical Engineering, Materials Science, Chemistry, Environmental Engineering, Mechanical Engineering, or closely related STEM disciplines.
Prior research or laboratory experience is helpful but not required. Successful applicants should demonstrate curiosity, strong attention to safety, reliability, and an interest in environmental or sustainability-focused research.
Dr. David Pan
Electrical & Computer Engineering | pand@uah.edu
AI-Driven Ball Spin Analysis for Table Tennis Performance Analytics
With easy rules, minimal space requirements, and meaningful health benefits, table tennis has become the sixth most popular sport worldwide. Ball spin is one of the most fascinating and defining aspects of table tennis, shaping the speed, curvature, and bounce of the ball in ways that are often invisible to spectators but critical to elite performance. Several decisive matches in major 2025 ITTF and WTT events demonstrated how professional players use subtle variations in spin and trajectory curvature to control pace and gain tactical advantages.
This project aims analyze ball-trajectory and identify ball spin types from competition and training videos using modern computer-vision and machine-learning methods. To detect and track high-speed balls reliably, we will employ YOLO-based object-detection models, as well as consider alternative frameworks such as Detectron2, Faster R-CNN, SSD, and EfficientDet for cases where higher precision or more stable small-object detection is needed. For multi-frame tracking, the project will explore established tracking systems including Deep SORT, ByteTrack, and CenterTrack, which can link frame-by-frame detections into consistent trajectories even during rapid rallies. Based on ball trajectory tracking, we will develop methods to classify ball-spin types—topspin, sidespin, underspin, mixed spin, and no-spin—by analyzing rotation cues, motion patterns, and stroke characteristics. Optical-flow techniques and physics-based modeling may also be incorporated to improve spin inference and curvature estimation.
The project will integrate object detection, trajectory reconstruction, and spin classification. The deliverables will be video analytic software packages.
Prerequisites/Requirements: The student should preferably have experience in playing table tennis and have a general
interest in sports video analytics. A working knowledge of programming languages (C/C++,
Python, and/or Matlab) is required. Having taken relevant courses in image processing, and/or machine learning is not required but will be very helpful for the project.
Prof. Kyung-Ho Roh
Chemical & Materials Engineering | kr0054@uah.edu
Engineering Calcium Phosphate Nanoparticles via Microfluidic Mixing for Enhanced Plasmid DNA Delivery
Calcium phosphate (CaP) nanoparticles are promising carriers for delivering genetic materials such as plasmid DNA (pDNA) because they are biocompatible, biodegradable, and naturally present in the body. However, traditional methods for preparing CaP nanoparticles often yield particles that are too large or inconsistent in size, reducing their ability to enter cells efficiently and deliver therapeutic genes. Improving the precision and reproducibility of CaP nanoparticle synthesis is essential for advancing next-generation gene-delivery technologies.
This project will explore a modern engineering approach to overcome these challenges by using microfluidic micromixing, a technique that rapidly and uniformly mixes reagents in channels of small length scale. Unlike conventional vortex mixing in test tubes, microfluidics enables extremely fast, controlled reaction conditions that can produce nanoparticles that are smaller, more uniform, and better optimized for biological applications.
The goal of this summer project is to design and test a microfluidic process for creating monodisperse CaP nanoparticles capable of carrying plasmid DNA. Students will learn how to design, 3D print, and operate microfluidic devices, prepare CaP nanoparticle formulations, and evaluate particle size and stability using techniques such as dynamic light scattering (DLS). The project will also investigate how mixing conditions, reagent ratios, and flow parameters influence the formation and performance of the nanoparticles.
By the end of the 10-week program, the student will have developed a microfluidic-based method for producing improved CaP gene delivery carriers and will gain hands-on experience at the interface of chemical engineering, nanotechnology, and biotechnology. This project offers an exciting opportunity to contribute to emerging strategies for safer and more effective gene therapies.
Prerequisites/Requirements: This project is open to students at all academic ranks and from any academic discipline. However, the preference will be given to a candidate who meets these criteria: i) strong motivation in biomedical research, ii) experience and willingness to learn wet laboratory experiments, iii) background training in chemical engineering, chemistry, biology, or related fields.
Prof. Kyung-Ho Roh
Chemical & Materials Engineering | kr0054@uah.edu
Injectable, Ion-Tolerant Thermogel: A POEGMA–UPy Feasibility Study
Background: Cells live in soft, water-rich matrices that are viscoelastic: they support shape yet slowly relax stress. Commonly used natural extracellular matrix (ECM) gels, such as collagen, are helpful but inherently variable and thus difficult to fine-tune. Additionally, changing one parameter (e.g., concentration) shifts pore size, stiffness, and transport simultaneously. We propose a fully synthetic alternative built from a PEG-based polymer (POEGMA) that undergoes a gentle, reversible thermo-switch near body temperature. A tiny fraction of reversible hydrogen-bond “stickers” (UPy) endows the network with self-healing and tunable relaxation so the gel can flow during injection, then set and recover once in place.
Motivation: A clear, animal-free, ion-tolerant hydrogel that liquefies below 37 °C and gels at or above 37 °C would simplify the formation of in vivo injection, loading into microfluidic devices, patterning, and harvesting cultivated cells, etc. Because the PEG backbone is bio-inert, we can add only the cues (i.e., biologically active ligands) we want at defined densities, avoiding confounding signals present in natural ECM. Equally important, this material lets us independently tune three levers that matter for cell function: stiffness via how many polymer bridges form, stress-relaxation time via sticker chemistry, and mesh size via polymer composition, capabilities difficult to achieve with natural hydrogel.
Goal: Establish the feasibility of a POEGMA–UPy hydrogel as a platform for in vitro and in vivo 3D cell culture. Specifically, we will (i) create a small library of RAFT-made polymers whose cloud point in water is set to land at ~37 °C in physiological media, (ii) demonstrate reversible sol-gel transition, self-healing, and optical clarity suitable for microscopy, and (iii) verify that mechanics fall in a biologically relevant window while maintaining cytocompatibility.
Prerequisites/Requirements: This project is open to students at all academic ranks and from any academic discipline. However, the preference will be given to a candidate who meets these criteria: i) strong motivation in biomedical and chemical research, ii) experience and willingness to learn wet laboratory experiments, iii) background training in chemistry, chemical engineering, or related fields.
Dr. Abdullahi Salman
Civil & Environmental Engineering | ams0098@uah.edu
Evaluating Risk Mitigation Strategies for Coastal Residential Buildings under Hurricane Winds
The majority of residential buildings in Alabama, Mississippi, and other coastal areas of the U.S. are light-frame wooden structures that are highly vulnerable to intense hurricane winds. Damage from excessive wind pressure and wind-borne debris represents a significant portion of hurricane-related losses. The Congressional Budget Office estimates annual hurricane losses to the residential sector at about $34 billion, including repair costs and temporary housing. These losses are expected to rise with continued population growth and urbanization in coastal regions. Beyond economic impacts, hurricanes also cause serious social disruptions, especially for populations with socioeconomic vulnerabilities.
Project Goal: Use FEMA’s Hazus-MH software to evaluate the effectiveness of various hurricane wind mitigation strategies for residential buildings in Alabama and Mississippi.
Methodology: Two main tasks are defined to achieve the project goal.
Task 1: Quantify economic losses due to wind damage to residential buildings in Alabama and Mississippi using FEMA’s Hazus-MH damage and loss functions, which relate wind speed to physical damage and economic loss.
Task 2: Assess the effectiveness of selected mitigation strategies in reducing wind-related losses. Four strategies will be considered: (1) installation of shutters, (2) reinforcement of garage doors, (3) re-nailing roof deck attachments, and (4) adding hurricane straps to roof-to-wall connections. A cost-benefit analysis will determine the relative effectiveness of these strategies by comparing the cost of mitigation with expected annual losses with and without mitigation.
Prerequisites/Requirements: None
Dr. Abdullahi Salman
Civil & Environmental Engineering | ams0098@uah.edu
Machine-Learning–Enhanced Resilience Assessment for Water Distribution Networks Under Infrastructure Aging
Water distribution networks (WDNs) are critical infrastructure systems that ensure reliable water delivery for households, industry, and emergency services. Aging pipes, increasing demand, and exposure to natural or man-made hazards significantly reduce system reliability and create long-term operation challenges for utilities. Across the United States, nearly one-third of water pipes are more than 50 years old, and failure rates continue to increase annually. Because real-world failure data are often incomplete or difficult to obtain, utilities lack the analytical tools needed to anticipate degradation and plan maintenance proactively.
This project aims to develop and evaluate a simulation-driven, machine-learning-based framework for assessing the resilience of water distribution networks under progressive infrastructure aging. Using the Water Network Tool for Resilience (WNTR), the student will generate synthetic pipe-failure scenarios based on pipe age, hazard functions, and hydraulic response. Performance metrics such as Water Service Availability (WSA), Modified Resilience Index (MRI), and the Combined Performance Index (CPI) will be computed to quantify system functionality under stress.
The student will then help build and test a machine learning model that predicts system-level resilience indicators using network topology, hydraulic characteristics, and simulated aging conditions. The goal is to determine how accurately machine learning can forecast long-term service performance and identify early signs of resilience loss. This research supports the development of scalable predictive tools that help utilities prioritize maintenance and strengthen community resilience.
Prerequisites/Requirements: Knowledge of statistics and some experience with coding or machine learning are preferred but not required.
Dr. Judy Schneider
Mechanical & Aerospace Engineering and Industrial & Systems Engineering and Engineering Management | jas0094@uah.edu
Applying numerical tools to optimize an advanced manufacturing process
This project aims to advance the understanding of defect formation in the solid-state joining process of friction stir welding (FSW). Defects, or incomplete consolidation, reduce joint efficiency and lead to non-optimized weld schedules. At present, controllable process parameters are correlated empirically with monitored data, and selecting an appropriate process-parameter window requires destructive characterization—an approach that becomes impractical for routine quality control. This limitation prevents full realization of the potential benefits of FSW in manufacturing.
To address this challenge, we will develop physics-based machine learning models that integrate heat-conduction and material-flow equations with experimental data to enable real-time, non-destructive prediction of defect formation and mechanical performance. These algorithms will be developed and validated using previously collected datasets. Once verified, the approach can be applied to quality control of completed welds and ultimately enable real-time, closed-loop feedback control of the FSW process, reducing both cost and fabrication time.
Prerequisites/Requirements: Junior l standing in either Math or Engineering programs
Dr. Yooseob Song
Civil & Environmental Engineering | ys0029@uah.edu
Dynamic Testing of 3D-Printed Concrete Using SHPB Techniques
3D-printed concrete is rapidly emerging as a transformative technology for construction, enabling faster, more automated building on Earth and offering a promising pathway for future off-world construction on the Moon or Mars. Despite this growing interest, surprisingly little is known about how these additively manufactured materials behave under extreme loading rates, including the types of high-speed events that occur during impacts, blasts, or accidental overloads. Understanding their dynamic response is critical for ensuring the safety and reliability of 3D-printed structures.
This project will investigate the dynamic tensile behavior and fracture mechanisms of 3D-printed concrete using Split Hopkinson Pressure Bar (SHPB) techniques. In the proposed experiments, a controlled compression pulse will be generated in an incident bar and transmitted into a cylindrical concrete specimen mounted at the bar end. When the compressive stress wave reaches the free surface of the specimen, it reflects as a tensile wave. Under sufficiently high loading rates, this reflected tensile wave can cause rapid fracture within the specimen, allowing direct characterization of the material’s dynamic tensile strength. To achieve well-controlled stress states, the project will incorporate pulse-shaping methods so that interacting stress waves produce a short period of nearly uniform tensile stress within the specimen.
Undergraduate researchers will gain hands-on experience in specimen fabrication, SHPB experimental setup, instrumentation, and high-speed data acquisition. Students will analyze stress wave propagation, calculate dynamic stresses and strain rates, and examine fracture surfaces to understand how the 3D-printing process influences failure behavior. The outcomes of this project will improve fundamental understanding of high-rate concrete mechanics and support the development of advanced construction materials for extreme terrestrial and space environments.
Prerequisites/Requirements: This project is open to Civil and Environmental Engineering (CEE), and Mechanical and Aerospace Engineering (MAE) students at ALL-ACADEMIC RANKS.
Dr. Nathan Spulak
Mechanical & Aerospace Engineering | ncs0023@uah.edu
High Rate Testing of Lunar Concrete for Space Exploration and Habitation Purposes
For this project, the student will perform dynamic tension testing on concrete specimens, particularly those made with advanced 3D printing techniques and/or from lunar regolith simulant materials. For long-term space exploration purposes, structures must be constructed from concrete made from in-situ extraterrestrial materials (i.e., lunar regolith), and potentially with the use of 3D printing. This material will be used to construct structures such as rocket landing pads and livable habitation structures. Such structures would have to withstand loading under high-rate, dynamic conditions – such as landing pads during rocket takeoffs/landing, or habitats that must withstand micrometeoroid and debris impacts.
The student will perform dynamic spall testing on concrete materials of interest, using a split-Hopkinson bar (SHB) high strain rate testing apparatus. An initial compressive wave will be imparted to a long cylindrical concrete specimen. The compression wave will travel down the concrete cylinder and reflect off the free end as a tension wave – which will then result in deformation and fracture of the concrete under dynamic tension loading. The student will also utilize high-speed imaging to capture the deformation of the concrete using digital image correlation (DIC), and high-speed infrared thermography (IR) to measure the temperature rise due to adiabatic deformation induced heating. The measured behavior will be incorporated into finite element simulations using Ansys LS-DYNA, to construct fracture models that match the observed dynamic tensile response of the concrete specimens.
This dynamic tensile strength of the concrete specimens will be used to inform concrete 3D printing methods and design concrete mixtures that can withstand the dynamic loading they may experience during space exploration and habitation uses. This will ensure human life and safety is protected, and prevent critical unwanted failures of space structures.
Prerequisites/Requirements: The RCEU student applicant should be a sophomore level or higher studying engineering, who has taken MAE 370: Mechanics of Materials.
Dr. Nathan Spulak
Mechanical & Aerospace Engineering | ncs0023@uah.edu
For this project, the student will create educational modules that use martial arts techniques to demonstrate scientific principles such as kinetic energy, leverage, Newton’s second law, and biomechanics. As an example, an effective striking technique (such as a punch or kick) requires the martial arts practitioner to generate the maximum possible kinetic energy. This entails moving the striking limb quickly to maximize velocity, while simultaneously shifting weight and locking the body behind the strike at the moment of impact to maximize the effective mass. Imparting this generated momentum quickly to the target is then used to create peak impact force, as decreasing the amount of time over which the generated momentum is transferred from the practitioner to the target results in the corresponding impact force to be increased. The biomechanics and musculoskeletal alignment during the strike is also critically important, in order to prevent the practitioner from sustaining injuries.
The student will use tools such as a dynamic striking force sensor, inertial measurement units (IMUs), and potentially motion capture systems to quantitatively relate the velocity, acceleration, and weight transference to the generated striking force. Software such as OpenSim will also be used to create graphics showing proper musculoskeletal structure to avoid injury.
The student will then use these graphics and instruments in demonstration modules to attempt to teach the fundamental scientific concepts to martial arts students of varying age ranges. Surveys before and after the educational module will be used to assess the effectiveness of the educational modules.
Overall project mentorship, technical help, and connections with local martial arts studios will be managed through Dr. Nathan Spulak (MAE), and additional guidance will be available from Dr. Howard Chen (ISEEM) and/or Dr. Sara Harper (Kinesiology).
Prerequisites/Requirements: The RCEU student applicant should be a sophomore level or higher studying engineering, science, or kinesiology. A personal background or familiarly with martial arts will also be highly useful.
Dr. Anu Subramanian
Chemical & Materials Engineering | as0305@uah.edu
Ovine Mesenchymal Stromal Cells as a Translational Model for Human Cartilage Repair
Cartilage injuries have limited capacity for self-repair, and early degeneration often progresses to osteoarthritis (OA), a major clinical and socioeconomic burden. Existing cell-based repair strategies rely heavily on chondrocytes, which are associated with donor-site morbidity and limited regenerative potential. Mesenchymal stromal cells (MSCs), in contrast, are more abundant, easier to isolate, and capable of differentiating into chondrocytes, making them an attractive therapeutic alternative. A critical challenge, however, is ensuring that MSCs maintain their chondrogenic potential in the inflammatory environments characteristic of joint injury. Recent work from our group demonstrates that continuous low-intensity ultrasound (cLIUS) can enhance chondrogenesis and reduce inflammatory signaling in human MSCs. Translating this therapy into clinically relevant large-animal models, particularly sheep, widely used for cartilage repair studies, requires determining whether ovine MSCs exhibit conserved molecular responses to cLIUS. Currently, this cross-species comparison has not been established, representing a gap in the preclinical development pipeline.
This project aims to address this gap by analyzing transcriptomic datasets generated from human and ovine MSCs cultured in 3D hydrogels under homeostatic and inflammatory conditions, with and without cLIUS stimulation. Students will use established bioinformatics workflows, including differential expression analysis, pathway enrichment, clustering, correlation analysis, and cross-species ortholog mapping, to identify conserved and divergent molecular signatures. Statistical comparisons will quantify the similarity of cLIUS-mediated pathways across species, helping determine the suitability of the sheep for future in vivo studies. This project provides hands-on experience in computational biology while contributing directly to the translational development of a promising non-pharmacologic therapy for cartilage repair.
Prerequisites/Requirements: Preliminary understanding of Python programming, probability and statistics, Basic cell and molecular biology
Dr. Agnieszka Truszkowska
Chemical & Materials Engineering | at0175@uah.edu
Exploring mass and heat transfer in large networks of microreactors
Microreactors appeared over 30 years ago as a more efficient, sustainable, and safer alternative to traditional chemical processing equipment. They became promising candidates for many traditionally challenging processes, including the synthesis of new chemicals that are either not economical or even impossible with established industrial equipment. Despite its promise and some successful commercial applications, microreactor technology is still facing challenges that prevent its broader use. One difficulty is the practical integration of hundreds, thousands, and tens of thousands of those small devices that would boost their productivity to commercial levels. We have previously researched constructing small networks of microreactors using concepts from graph theory, focusing on the uniformity of flow through the devices and their resilience to damage and ease of repair. We found that while all networks were performing satisfactorily, some were superior, which we explained by their unique topological features. Using an in-house C++ software, we are now investigating networks with tens of thousands of microreactors, yet so far, our efforts have focused on the flow of the reactants, which is only a proxy for good performance. In this project, we will introduce chemical reactions that either consume or release heat, allowing us to study the networks under real conditions directly relevant to commercialization. We expect uneven distribution of reactants, products, and temperatures, and hypothesize that some of the designs will fail while others will outperform all the alternatives. We will investigate the networks both under normal operating conditions and when, inevitably, some of the microreactors start malfunctioning. Our project will give new, vital insights into opportunities and challenges in the commercialization of microtechnology and provide a path for new investigations.
Prerequisites/Requirements: The only requirement is that the student's major should be in science or engineering. If a student outside of these majors has a sufficient background in science or engineering, they are welcome to apply.
Dr. Ana Wooley
Industrial & Systems Engineering and Engineering Management | acw0047@uah.edu
Digitally Enabled Advanced Assembly Bench
Modern assembly environments often rely on fragmented manual processes, static work instructions, and disconnected tools that provide limited insight into system performance. Operators receive little real-time feedback on process speed, quality trends, or workflow disruptions, making it difficult to identify inefficiencies or support continuous improvement. These challenges are especially impactful in low-mix, high-volume manufacturing, where small inefficiencies can quickly scale into significant losses. As production systems are pushed to operate more efficiently and flexibly, there is a growing need for sensor-enabled assembly systems that provide real-time performance visibility and actionable data.
The Digitally Enabled Advanced Assembly Bench (DEAAB) project aims to enhance and demonstrate a smart, data-driven manual assembly workstation that integrates core digital manufacturing technologies in a representative electromechanical assembly environment. The system operates entirely on-premise, with all data collection, storage, and visualization handled locally to ensure data security and control.
This project builds on an existing modular assembly line in the Digital X Lab (ISEEM) that is already equipped with load-cell-based smart inventory bins. The goal of this continuation effort is to expand the system’s sensing and data-collection capabilities to monitor key manufacturing performance metrics such as cycle time, throughput, work-in-process (WIP), and downtime.
The student will help integrate additional low-cost sensors (e.g., proximity and position sensors), connect them to microcontrollers, link them to a local networking and supervisory system and develop a browser-based operator interface with digital work instructions and automated task confirmation. Through this hands-on experience, the student will gain practical exposure to digital manufacturing, data-driven decision-making, and Industry 4.0/5.0 concepts in a realistic assembly environment.
Prerequisites/Requirements: This position is ideal for undergraduate students with an interest in manufacturing systems, automation, or engineering design. Preferred (but not required) experience includes: (i) Basic familiarity with CAD tools (e.g., AutoCAD, SolidWorks, or similar), (ii) Introductory knowledge of sensors and embedded systems, (iii)Exposure to programming concepts (e.g., Python, or similar languages), (iv) Experience with microcontrollers such as Arduino or ESP32, including basic sensor interfacing or data collection, and (v) Interest in hands-on prototyping, troubleshooting, and experimentation. Formal experience with all of the above is not required. Motivation to learn and work in a lab-based manufacturing environment is the most important qualification.
Prof. Yue Xiao
Mechanical & Aerospace Engineering | yx0007@uah.edu
Lightweight Polymer Composite for Space Radiation Shielding and Electronics Heat Dissipation
For the space radiation shielding of Small/Cute Satellites, the current mainstream technology is bulk shielding using aluminum alloy that encloses the entire structure. However, it is well recognized that the aluminum bulk shielding suffers from high Size, Weight, and Power (SWaP) requirements as the shield thickness is determined by the electronics that are most vulnerable to radiation damage. This disadvantage becomes the bottleneck to employing commercial-off-the-shelf (COTS) electronics in space applications due to their low radiation tolerance but high performance and short lead time.
To tackle the challenge, we intend to continue the development of a lightweight multi-layered Radiation Shielding Polymer Composite (RSPC) material to reduce the SWaP requirement while improving the electronics' heat dissipation. Specifically, RSPC adopts polydopamine (PDA) coated high-atomic number (high-Z) metal oxides crosslinked with the base polyurethane (PU) material and other additives such as carbon fiber for further improved mechanical strength, radiation survivability, and heat dissipation.
The major objectives for the applicant of this RCEU project are: 1) Perform material synthesis, characterization, and data processing, including thermal property and mechanical strength measurements; 2) Coordinate with external collaborators on the irradiation testing of the material; 3) Coordinate with the UAH space club for action items related to the potential flight test.
The applicant’s work will be a part of a journal or conference paper, and the applicant will be exposed to various parties of interest from both government and industry.
Prerequisites/Requirements: Applicant majored in Mechanical Engineering, Aerospace Engineering, Chemical Engineering, or Material Science and have interests in Advanced Material and Manufacturing Technologies (AMMT), heat transfer, space technologies, etc., are welcome to apply.
Prof. Yue Xiao
Mechanical & Aerospace Engineering | yx0007@uah.edu
Plasma Decontamination System for Planetary Protection of Space Missions
Planetary Protection (PP), a critical step for all major space missions, is the practice of sanitizing the space component before launch to protect planetary bodies in the space from contamination by Earth life. Currently, Dry Heat Microbial Reduction (DHMR) and Vapor Hydrogen Peroxide (VHP) are utilized to perform the sanitation, but they are incompatible with electronics and has other restrictions. As a result, NASA current adopts manual cleaning of the space component that is high-cost, labor-intensive, and repetitive.
To resolve the above technical challenges in PP, we are developing an ultra-lightweight plasma decontamination and storage system for planetary protection and contamination control for space missions. Such a system is expected to achieve high-performance, energy-efficient, and low-temperature decontamination of spacecraft components of various materials and sizes, particularly for enclosures with electronics. Specifically, the spacecraft components will be placed inside a Flexible Pouch Plasma Reactor (FPPR), and ozone will be generated from air at close vicinity of the components to maximize the decontamination efficacy. For hard-to-reach areas, the Versatile Plasma Reactors (VPRs), a custom-shaped low temperature plasma reactor, can be inserted inside the space component to perform local, targeted decontamination.
The student applicant for this project will get into the world of the low-temperature plasma (a lot of purple glowing!) and prototype design driven by needs in the space industry. The applicant is expected to 1) perform mechanical design to optimize the performance of the plasma reactor; 2) conduct experiments to quantify the system performance, energy consumption, and the decontamination effects; 3) Analyze and compile data. The applicant’s work will be a part of a journal or conference paper, and the applicant will be exposed to various parties of interest from both government and industry.
Prerequisites/Requirements: Applicant majored in Mechanical Engineering, Aerospace Engineering, Chemical Engineering, or Material Science and have interests in space technologies, plasma, heat transfer, etc., are welcome to apply. Applicants with good hands-on skills and CAD skills are strongly encouraged to apply.
Prof. Gabe Xu
Mechanical & Aerospace Engineering / Propulsion Research Center | kgx0001@uah.edu
Electromagnetic Nozzles for Laser Produced Plasmas
This project studies the effect of a confining magnetic nozzle for enhancing the thrust generated by laser ablation propulsion. Interest in laser ablative propulsion has focused on its applications as a possible tool for removing orbital debris, using the debris itself for reaction mass. To date, research at UAH has used Langmuir probes to characterize the plasma generated during ablation under different magnetic nozzle conditions. This has been supplemented by ICCD imaging to observe the behavior of the generated plume under different target conditions. Lastly, an impulsive pendulum thrust stand with a micronewton resolution has been used to directly measure the thrust generated during experiments.
At present, only permanent magnets have been used in the previous tests. This proposal is motivated by the need to understand the propulsive characteristics of magnetic nozzles under a variety of field strengths and divergence configurations. Testing across a wide range of parameters will provide insight into the mechanisms governing momentum transfer in this process while also allowing for optimal nozzle configurations to be identified. As such, electromagnet-based nozzles need to be modeled, constructed, and validated for use in future experiments.
The primary challenge in this project will lie in designing nozzles that are lightweight and small enough to be attached to the thrust stand.
Prerequisites/Requirements: Completed physics II (E&M)