Research Projects

Projects are posted below; new projects will continue to be posted through February. To learn more about the type of research conducted by undergraduates, view the 2017 Research Symposium Abstracts.

This is a list of research projects that may have opportunities for undergraduate students. Please note that it is not a complete list of every SURF project. Undergraduates will discover other projects when talking directly to Purdue faculty.

You can browse all the projects on the list, or view only projects in the following categories:

Agricultural

 

Drinking water safety and sampling in buildings

Research categories:  Agricultural, Bioscience/Biomedical, Civil and Construction, Environmental Science, Life Science
School/Dept.: Civil Engineering -AND- Environmental and Ecological Engineering
Professor: Andrew Whelton
Preferred major(s): Open
Desired experience:   Science or engineering background Prior lab or field experience with chemical or microbiological analysis preferred, but not required. Students will be trained with all necessary methods. Clear motivation to make a difference Able to effectively work in diverse teams Work hours will be based on the time of day and actual date of prescheduled sampling

The student will assist graduate students, a postdoctoral research association, and the professor conduct drinking water sampling in buildings. The project's focus is to better understand how drinking water quality changes during a plumbing system's age and also differences in drinking water across buildings. This project will be a mix of field and laboratory work. One study site is located in West Lafayette, IN while others are elsewhere. The student would accompany the researchers to those sites. Prior study can be found here: https://www.ncbi.nlm.nih.gov/pubmed/29253792

 

Effects of Aging Treatment on the Microstructure, Surface and Mechanical Properties of Food and Pharmaceutical Relevant Materials

Research categories:  Agricultural, Environmental Science, Material Science and Engineering
School/Dept.: ABE
Professor: Teresa Carvajal
Preferred major(s): ABE, MSE, ChE, ME
Desired experience:   Physical Chemistry, Thermodynamics, Material properties such as Mechanical Stress and Response of Materials, Mohr's circles, Organic Chemistry, Polymers Statistics. Overall, very motivated student eager to innovate.

Characterization of the physicochemical, surface and mechanical properties in a wide range of soft materials (food and pharmaceuticals) will be conducted. Of interest, the environmental conditions during manufacturing and storage that could change the properties of materials leading to potential detrimental changes on the performance and quality in the food or pharmaceutical product. The study is directed to the question of what stimulates aging on the microstructures, which might contribute to stability and performance during processing. The microstructure-level controlling surface interactions will be also addressed by using various analytical tools. The bulk properties such as powder flow behavior will be characterized such that structure-property-processing relationships can be established.

 

Purdue AirSense: An Air Pollution Sensing Network for West Lafayette

Research categories:  Agricultural, Chemical, Civil and Construction, Computer Engineering and Computer Science, Electronics, Environmental Science, Innovative Technology/Design, Mechanical Systems, Nanotechnology, Physical Science
School/Dept.: Civil Engineering
Professor: Brandon Boor
Preferred major(s): The position is open to students from all STEM disciplines.
Desired experience:   Proficient in Python, Java, MATLAB; experience with Raspberry Pi or Arduino.

Air pollution is the largest environmental health risk in the world and responsible for 7 million deaths each year. We are presently developing a new air pollution sensing network for the Purdue campus to monitor and analyze air pollutants in real-time. We are recruiting an undergraduate student to assist with the development of our Raspberry Pi-based air quality sensor module. You will be responsible for integrating the Raspberry Pi with air quality sensors, developing laboratory calibration protocols, building an environmental enclosure for the sensors, creating modules on our website for real-time data analysis and visualization, and maintaining state-of-the-art aerosol instrumentation at our central air quality monitoring site at the Purdue Agronomy Center for Research and Education (ACRE).

 

Remote sensing of soil moisture using P-band Signals of Opportunity: Model development and experimental validation.

Research categories:  Agricultural, Aerospace Engineering, Computer Engineering and Computer Science, Electronics, Environmental Science, Physical Science
School/Dept.: AAE
Professor: James Garrison
Preferred major(s): ECE, Physics, Geophysics, With appropriate coursework: AAE, ABE, Civil, Geomatics,
Desired experience:   Signal processing; Programming: C, Python, MATLAB; Electronic hardware experience preferred; Drivers license and access to car required.

Root Zone Soil Moisture (RZSM), defined as the water profile in the top meter of soil where most plant absorption occurs, is an important environmental variable for understanding the global water cycle, forecasting droughts and floods, and agricultural management. No existing satellite remote sensing instrument can measure RZSM. Sensing below the top few centimeters of soil requires the use of microwave frequencies below 500 MHz, a frequency range known as “P-band”. A P-band microwave radiometer would require an aperture diameter larger than 10 meters. Launching such a satellite into orbit will present big and expensive technical challenge, certainly not feasible for a low-cost small satellite mission. This range for frequencies is also heavily utilized for UHF/VHF communications, presenting an enormous amount of radio frequency interference (RFI). Competition for access to this spectrum also makes it difficult to obtain the required license to use active radar for scientific use.

Signals of opportunity (SoOp) are being studied as alternatives to active radars or passive radiometry. SoOp re-utilizes existing powerful communication satellite transmissions as “free” sources of illumination, measuring the change in the signal after reflecting from soil surface. In this manner, SoOp methods actually make use of the very same transmissions that would cause interference in traditional microwave remote sensing. Communication signal processing methods are used in SoOp, enabling high quality measurements to be obtained with smaller, lower gain, antennas.

Under NASA funding, Purdue and the Goddard Space Flight Center have developed an airborne prototype P-band remote sensing instrument to demonstrate the feasibility of a future satellite version. Complementing this technology development, a field campaign in the Purdue Agricultural research fields is being planned. This campaign will make reflected signal measurements from towers installed over instrumented fields. Measurements will be obtained over bare soil first, and then throughout the corn or soybean growth cycle. Complementing these remote sensing measurements, a comprehensive set of ground-truth data will also be collected for use in developing models and verifying their performance.

Work under this project will involve installing microwave electronic equipment in the field, writing software for signal and data processing, and making field measurements of soil moisture and vegetation properties.

Students interested in this project should have good programming skills and some experience with C, python and MATLAB. They should also have a strong background in basic signal processing. Experience with building computers or other electronic equipment will also be an advantage. Preference will be given to students who have an interest in applying their skills to solving problems in the Earth sciences, environment, or agriculture.

NOTE: The project will involve regular travel to and from the local research field, so students should have a drivers license and reliable access to a car.

 

Self-Learning Mobile Hydraulic Equipment

Research categories:  Agricultural, Computer Engineering and Computer Science, Educational Research/Social Science, Mechanical Systems
School/Dept.: Agricultural & Biological Engineering/Mechanical Engineering
Professor: Monika Ivantysynova
Desired experience:   Senior, MATLAB, statistics, Excel, proficiency in presentation skill, and a basic understanding of instrumentation. A knowledge in hydraulics is a plus.

Failures rarely occur at convenient times, especially on mobile equipment, such as excavators, tree skidders, agricultural tractors, mining equipment, airplanes, etc. Hydraulic failures in the field often cause costly repairs that also result in significant machine downtime. The failures can potentially be life-threatening. Manufacturers and equipment operators desire a solution to predict failures before they occur. This area of research is known as prognostics. The machine compares real-time data and stored data to determine the “health” of the hydraulic pumps and motors. The SURF student would assist the graduate student mentor in collecting machine data from mobile hydraulic machines and create an algorithm to determine the “healthy” state of the hydraulic pumps and motors. Data analysis, data clustering, and machine self-learning are topics that will be used in this research.

Please note: Research lab location is in Lafayette. Student is responsible for their own transportation.

 

Stochastic Storm Generation of Storms and Their Inner Structure

Research categories:  Agricultural, Civil and Construction, Computer Engineering and Computer Science, Environmental Science
School/Dept.: Agricultural & Biological Engineering
Professor: Bernie Engel
Preferred major(s): Agricultural engineering, environmental engineering, computer science

Advanced field and watershed scale hydrologic models for engineering design, soil erosion, land use planning, and global-change research require detailed continuous temporal and spatial inputs of precipitation to execute the hydrologic processes integrated into their formulations. Accurate estimates of processes such as infiltration, runoff routing, and water quality algorithms need precipitation values on the order of minutes apart. In the United States, the National Oceanic and Atmospheric Administration (NOAA) collects 15-min time increment precipitation data in ~2000 locations. However, observed precipitation is yet rarely available in many sites and lack spatial coverage. In ongoing research, a stochastic storm generator developed at Purdue University allows generating storm characteristics such as inter-event time, duration, and volume, as well as within-storm intensities using the available 15-min resolution data. The current project proposes to extend the application of the current version of the storm generator from a single station to a more detailed network of meteorological stations. The final goal seeks to perform a test of available interpolation method between the statistical parameters defining the available locations so that time series of precipitation data in ungauged areas can be generated.

Activities:

1. Collect short-time increment precipitation from NOAA and other sources. The SURF student will learn how to search available precipitation data available in the different agencies.
2. Organize and run a clean-up data analysis. The SURF student will deal with different files containing precipitation data and formats as well as its spatial representation by GIS tools.
3. Identify independent storms over the time period. The SURF student will be able to learn how to run Python, MATLAB, and R scripts and to understand the concepts defining independent rainfall events.
4. Fit storm characteristics (time between storms, duration, and volume) to a suitable storm distribution. The SURF student will be able to perform statistical distribution fitting and how to measure the goodness of fit of the available procedure in the storm generator.
5. Generate correlated storm characteristics by Monte Carlo numerical simulation implemented in a stochastic storm generator develop at the National Soil Erosion Research Laboratory (NSERL). The SURF student will experience the use of complex mathematical algorithms incorporated into the storm generator.
6. Characterize storm patterns of the observed storms.
7. Identify representative patterns of storms by cluster analysis over the storm patterns data. The SURF student will explore the concept of machine learning and cluster analysis.
8. Generate storms patterns by Monte Carlo numerical simulation also implemented in a stochastic storm generator develop at the NSERL. The SURF student will continue experiencing the use of complex mathematical algorithms incorporated into the storm generator.
9. Propose an interpolation method of the storm parameters between the stations previously analyzed. The SURF student will apply available spatial interpolation methods in precipitation statistical parameters.