Research Projects

This is a list of research projects that may have opportunities for undergraduate students. You can browse all the projects, or view only projects in the following categories:

Industrial Engineering


Bio/Pharmaceutical Lyophilization

Research categories:  Aerospace Engineering, Bioscience/Biomedical, Chemical, Computational/Mathematical, Industrial Engineering, Innovative Technology/Design, Mechanical Systems
School/Dept.: AAE
Professor: Alina Alexeenko
Preferred major(s): AAE, ME, CE, BME, Physics
Desired experience:   Fluid dynamics coursework, programming experience.
Number of positions: 2

Freeze-drying, also called lyophilization, is widely used in manufacturing of injectable pharmaceuticals, vaccines, biotech products, food and probiotic cultures. The research involves first-principles modeling of fluid dynamics and heat transfer in industrial lyophilizers and validation of models by comparison with experimental data collected in lab and pilot production settings. The summer undergraduate researcher will be involved in developing computational models and analyzing experimental data.


Characterization of Fiber Reinforced Composite Materials

Research categories:  Aerospace Engineering, Chemical, Civil and Construction, Computational/Mathematical, Computer Engineering and Computer Science, Industrial Engineering, Material Science and Engineering
School/Dept.: School of Aeronautics and Astronautics
Professor: Sangid Michael
Preferred major(s): AAE, ME, MSE, IE, ChE, CE, NE, CS
Desired experience:   Preferably junior standing
Number of positions: 2

We are looking for motivated, hard-working undergraduate students interested in experimental composite materials research. This position is on a team investigating fiber orientation and length measurements in thermoplastic composites. These long fiber composites have a direct application to replace steel and aluminum structural alloys in the aerospace and automotive industries. Our team is comprised of Pacific Northwest National Lab, Autodesk, Plasticomp, Magna, Toyota, University of Illinois, and Purdue. Applicants will work under the mentorship of a graduate student and faculty member. The position includes hands on specimen preparation, in the form of extracting and polishing samples for fiber orientation measurements and melting samples and isolating the pertinent fibers for length measurements.


Design and development of a low pressure drop and low flow rate airflow sensor

Research categories:  Agricultural, Electronics, Environmental Science, Industrial Engineering, Innovative Technology/Design, Mechanical Systems
School/Dept.: Agricultural and Biological Engineering
Professor: Jiqin (Jee-Chin) Ni
Preferred major(s): Agricultural, mechanical, or electronic engineering
Desired experience:   Laboratory and hands-on experience on mechanical and basic electronic work.
Number of positions: 1

Measuring low rate of airflow with low pressure drop is important for some high quality research projects. However, commercially available sensors for these measurements are either expensive or not highly accurate. This project will involve designing an innovative airflow sensor that is suitable for low pressure drop (e.g., <50 Pa) and low flow rate (e.g., <50 mL per hour) airflow sensor. The principle of the sensor can be mechanical, electronic, or combination of the both. A workable prototype sensor based on the new design will also be built. The sensor will provide output signals that can be acquired to a computer for on-line and continuous airflow monitoring. The successful design can be disclosed as an invention to Purdue Office of Technology Commercialization.


Detecting workload effects and cognitive control mode changes in continuous aircraft state data

Research categories:  Aerospace Engineering, Industrial Engineering
School/Dept.: Industrial Engineering
Professor: Steven Landry
Preferred major(s): IE, AAE
Desired experience:   Statistics knowledge would be very helpful. Experience or interest in aviation is preferred.
Number of positions: 1

We have generated hypotheses regarding how to detect high and low workload conditions within recorded aircraft state data. Specifically, conditions of high workload result in low delay, high lag, and high gain. In this work, a human subjects experiment will be conducted where we test those hypotheses. In the experiment, pilot participants will fly a simulator under conditions of high, medium, and low workload, with the aircraft states recorded. Delay, lag, and gain will be recorded and analyzed to see if statistically-significant differences exist across the workload levels. We will also generate data to help us generate hypotheses on how discrete shifts in cognitive control mode (strategic, tactical, opportunistic, or scrambled) can be detected within the continuous aircraft state data.