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:
Multiphase Fluid Flows in Tight Spaces
|Research categories:||Bioscience/Biomedical, Chemical, Computational/Mathematical, Physical Science|
|Preferred major(s):||Mechanical Engineering, Chemical Engineering, Applied Mathematics, Computational Science|
|Desired experience:||1. Thorough understanding of undergraduate fluid mechanics. 2. Programming experience with high-level language such as Python or MATLAB. 3. Experience with shell/command-line environments in Linux/Unix; specifically, remote login, file transfers, etc. 4. Experience researching difficult questions whose answers are not found in a textbook. 5. Desire to learn about new fluid mechanics phenomena and expand computational skillset.|
Multiphase flows are fluid flows involving multiple fluids, multiple phases of the same fluid, and any situation in which the dynamics of an interface between dissimilar fluids must be understood. Examples include water displacing hydrocarbons in secondary oil recovery, a mixtures of particle-laden fluids being injected into a hydraulically fractured reservoirs ("fracking"), introduction of air into the lungs of pre-maturely born infants to re-open their liquid-filled lungs and airways, and a whole host of other physico-chemical processes in biological and industrial applications.
The goal of this SURF project will be to study, using computational tools such as ANSYS Workbench and/or the OpenFOAM platform, how multiphase flows behave in tight spaces. To accomplish this goal, the SURF student will work with a PhD student. Specifically the dynamics of interfaces between different phases and/or fluids will be studied through numerical simulation, and the effect of the flow passage geometry will be addressed. Some questions that we seek to address are whether/how geometric variations can stabilize or destabilize an interface and whether/how geometry affects the final distribution of particles in particle-laden multiphase flow passing through a constriction/expansion. Applications of these effects to biological and industrial flows will be explored quantitatively and qualitatively.
Network for Computational Nanotechnology (NCN) / nanoHUB
|Research categories:||Chemical, Computational/Mathematical, Computer Engineering and Computer Science, Electronics, Material Science and Engineering, Mechanical Systems, Nanotechnology, Other|
|Preferred major(s):||Electrical, Computer, Materials, Chemical or Mechanical Engineering; Chemistry; Physics; Computer Science; Math|
|Desired experience:||Serious interest in and enjoyment of programming; programming skills in any language. Physics coursework.|
NCN is looking for a diverse group of enthusiastic and qualified students with a strong background in engineering, chemistry or physics who can also code in at least one language (such as Python, C or MATLAB) to work on research projects that involve computational simulations. Selected students will typically work with a graduate student mentor and faculty advisor to create or improve a simulation tool that will be deployed on nanoHUB. Faculty advisors come from a wide range of departments: ECE, ME, Civil E, ChemE, MSE, Nuclear E, Chemistry and Math, and projects may be multidisciplinary. To learn about this year’s research projects along with their preferred majors and requirements, please go to the website noted below.
If you are interested in working on a nanoHUB project in SURF, you will need to follow the instructions below. Be sure you talk about specific NCN projects directly on your SURF application, using the text box for projects that most interest you.
1) Carefully read the NCN project descriptions (website available below) and select which project(s) you are most interested in and qualified for. It pays to do a little homework to prepare your application.
2) Select the Network for Computational Nanotechnology (NCN) / nanoHUB as one of your top choices.
3) In the text box that asks about your “understanding of your role in a project that you have identified”, you may discuss up to three NCN projects that most interest you. For each NCN project, be sure to tell us why you are interested in the project and how you meet the required skill and coursework requirements.
For more information and examples of previous research projects and student work, click on the link below.
Preparative and Imaging Mass Spectrometry
|Preferred major(s):||Chemistry or Chemical Engineering|
|Desired experience:||Analytical chemistry with labs, physical chemistry with labs|
Two projects are available in my laboratory. The first project is focused on the development of preparative mass spectrometry as a tool for the controlled synthesis of layered thin films and doping 3D materials with cluster ions. This project addresses fundamental challenges related to the development of new materials for energy conversion and storage. the second project is focused on the development of mass spectrometry imaging for quantitative mapping of numerous compounds in biological samples.
Production of essential aromatic amino acids from cyanobacteria
|Research categories:||Chemical, Life Science|
|Preferred major(s):||Chemical Engineering|
|Desired experience:||CHE 205, CHE 348|
The amino acids phenylalanine and tryptophan are valuable as feed additives. Currently they are produced from microbial fermentations from sugar. We are examining their direct photosynthetic production in cyanobacteria. Previously, our group has generated cyanobacterial strains that produce the amino acids. This project is do find the growth conditions that are optimal for maximizing amino acid production. The student will grow the cyanobacteria, measure the production of amino acids, and mathematically model to determine optimal conditions for high productivity.