2021 Research Projects
Projects are posted below; new projects will continue to be posted. To learn more about the type of research conducted by undergraduates, view the archived symposium booklets and search the past SURF projects.
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:
Other (32)
4D Printer Project
Accelerator Architecture Lab at Purdue (AALP): Optimizing Simulators for Advanced Processor Development
More information: https://accel-sim.github.io
Group Website: https://engineering.purdue.edu/tgrogers/group/aalp.html
More information: https://engineering.purdue.edu/tgrogers/
Accelerator Architecture Lab at Purdue (AALP): Modeling Diverse GPU Architectures in C++ Simulation
More information: https://accel-sim.github.io
Group Website: https://engineering.purdue.edu/tgrogers/group/aalp.html
More information: https://engineering.purdue.edu/tgrogers/
Advanced Vehicle Automation and Human-Subject Experimentation
The goal of this project is to describe and measure the ways in which a person interacts with advanced vehicle automation. Students will assist with multiple activities and will learn a combination of the following: how to a) develop/code advanced driving simulation scenarios, b) collect driving performance data, c) analyze driver and performance data (using methods via software packages), and d) write technical reports and/or publications. Students may also gain experience collecting and analyzing complementary physiological measures, such as eye movement data, brain activity, skin conductance, and heart rate. The students will work closely with graduate student mentors to enhance learning.
More information: https://engineering.purdue.edu/NHanCE
Analyzing educational teamwork dataset using quantitative and NLP techniques
More information: https://info.catme.org/
Automatically Detecting and Fixing Software Bugs and Vulnerabilities
Earlier work can be found here: https://www.cs.purdue.edu/homes/lintan/publications/deeplearn-tse18.pdf
More information: https://www.cs.purdue.edu/homes/lintan/
Building Software for Environmental Modeling
Bursting of Leading Edge Vortices on Swept Wings
The purpose of this SURF project will be to visualize the bursting of the vortex at the leading edge of a delta wing in order to investigate methods of preventing bursting. The experiment will be performed in the water tunnel at the Purdue Aerospace Research Laboratories, which will be adapted for this purpose. The ideal candidate will understand wing aerodynamics and have some experience with wind tunnel testing and the design of electro-mechanical drive mechanisms. This research project will be performed under the guidance of Dr. Paul Bevilaqua, a Purdue AAE graduate, retired Chief Engineer of the Lockheed Martin Skunk Works, and Neil Armstrong Distinguished Visiting Fellow in AAE.
Design, construction and simulation of scaled test facility for gas cooled reactor cavity building blowdown
Design, fabrication, and testing of an environmental chamber for X-ray characterization
More information: https://engineering.purdue.edu/~msangid/
Designing Epidemic Mitigation Methods with Limited Resources
In the project, the students will participate in designing a dynamic epidemic model for COVID-19 spreading in a community. Further, the students will fill in the role of a decision-maker of the community. Given a restricted budget, the students will try to alternate the system parameters which correspond to actions such as allocating medical equipment, imposing lock-down, and distributing vaccines, so that the virus will be eradicated quickly. Once the virus is eradicated, we will study how to prevent the occurrence of subsequent waves with relatively moderate policies. Furthermore, we will extend the problem to study how to mitigate the spreading of the virus with the lowest budget possible. The students will learn to apply geometric programming ideas to solve these problems.
More information: https://sites.google.com/view/philpare/home
Epidemic Analysis Via Social Networks
More information: https://sites.google.com/view/philpare/home
Epidemic Modeling and Prediction with COVID-19 Dataset
There are abundant well-organized Covid-19 datasets available online, including the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. By leveraging these datasets, we plan to design a project-based learning experience that participants will model and predict epidemic spread over a nine-week schedule. The project includes five major stages: 1) data collection, 2) model selection, 3) parameters optimization, 4) model verification, and 5) prediction. The participants will learn to model and analyze epidemic processes with compartmental models, and they will get the first-hand experience using a programming language of their choice to implement the modeling, optimization, and prediction pipeline.
More information: https://sites.google.com/view/philpare/home
Human Factors: Enhancing Performance of Nurses and Surgeons
The SURF student will participate in data collection in the operating room at Indiana University School of Medicine, data analysis and interpretation, and write his/her results for a journal publication. The student will regularly communicate his/her progress and results with faculty, graduate mentors, and surgeon collaborators.
More information: https://engineering.purdue.edu/YuGroup
IoT4Ag P1: Autonomous recharging of ground and aerial mobile agricultural robot platforms
A new Engineering Research Center on the Internet of Things for Precision Agriculture (IoT4Ag) has recently been established to ensure food, energy, and water security by advancing technology to increase crop production, while minimizing the use of energy and water resources and the impact of agricultural practices on the environment. The center will create novel, integrated systems that capture the microclimate and spatially, temporally, and compositionally map heterogeneous stresses for early detection and intervention to better outcomes in agricultural crop production. The Center will create internet of things (IoT) technologies to optimize practices for every plant; from sensors, robotics, and energy and communication devices to data-driven models constrained by plant physiology, soil, weather, management practices, and socio-economics. We are looking to hire a cohort of SURF students to work on different activities in the center.
IoT4Ag 1: Autonomous recharging of ground and aerial mobile agricultural robot platforms
# students: 2 - US Citizens or permanent residents only
In this project, students will be tasked to design and implement an autonomous battery recharging system for ground and aerial mobile agricultural robot platforms.
Student 1:
Survey of state of the art - robot battery swapping systems
Mechanical design of battery swapping system - on robot/at charging station
Research and implement robot path planning to charging nearest charging station
Develop and test visual servoing algorithms for robot to dock in charging station
Student 2:
Survey of state of the art - wireless charging techniques
Waveform design for wireless charging
Proof of concept demonstration of candidate system(s)
Electrical system design/specifications for robot-side wireless charging system
More information: iot4ag.us
IoT4Ag P2: IsoBlue integration to UGV/UAV platforms
A new Engineering Research Center on the Internet of Things for Precision Agriculture (IoT4Ag) has recently been established to ensure food, energy, and water security by advancing technology to increase crop production, while minimizing the use of energy and water resources and the impact of agricultural practices on the environment. The center will create novel, integrated systems that capture the microclimate and spatially, temporally, and compositionally map heterogeneous stresses for early detection and intervention to better outcomes in agricultural crop production. The Center will create internet of things (IoT) technologies to optimize practices for every plant; from sensors, robotics, and energy and communication devices to data-driven models constrained by plant physiology, soil, weather, management practices, and socio-economics. We are looking to hire a cohort of SURF students to work on different activities in the center.
IoT4Ag P2: IsoBlue integration to UGV/UAV platform
# students: 1, US Citizens or permanent residents only
In this project, the student will be tasked with system architecture design, integration, and mechanical design for an integrated IsoBlue communications module with existing UGV and UAV platforms. IsoBlue is an on-going project for an open source telematics and edge computing device, which connects to the CANbus of agricultural machines in order to read and log machine sensors and to create the capability for machine control. IsoBlue is also a general purpose sensor hub capable of communications using WiFi, Bluetooth Low Energy, TV whitespaces, and LoRa and it creates a bridge to the cloud using LTE cellular.
The ECE student on this project will:
Survey the state of the art in telematics, edge computing, and sensor networking
Specify the electrical and mechanical interfaces needed to integrate with the UGV platform
Modify the existing IsoBlue design to implement IsoBlue/UGV integration
More information: oatscenter.org
IoT4Ag P3: Biophysical modeling and integration with in-situ and remotely sensed data
A new Engineering Research Center on the Internet of Things for Precision Agriculture (IoT4Ag) has recently been established to ensure food, energy, and water security by advancing technology to increase crop production, while minimizing the use of energy and water resources and the impact of agricultural practices on the environment. The center will create novel, integrated systems that capture the microclimate and spatially, temporally, and compositionally map heterogeneous stresses for early detection and intervention to better outcomes in agricultural crop production. The Center will create internet of things (IoT) technologies to optimize practices for every plant; from sensors, robotics, and energy and communication devices to data-driven models constrained by plant physiology, soil, weather, management practices, and socio-economics. We are looking to hire a cohort of SURF students to work on different activities in the center.
IoT4Ag P3: Biophysical modeling and integration with in-situ and remotely sensed data
# of students: 3, US Citizens or permanent residents only
This interdisciplinary project will focus on acquisition and processing of remotely sensed data acquired by sensors on UAVs and wheel-based vehicles, developing empirical models, and working collaboratively with teams in the College of Agriculture to integrate empirical machine learning models with biophysical modeling to detect plant stress and predict yield. The project will provide opportunities for students to learn about sensors via field-based data acquisition from remote sensing platforms, expand their understanding of techniques for processing data, use data products for applications related to cropping systems (plant breeding, production management, in-season treatments) and engage in development of hybrid models that include both data analytics and biophysically based approaches. Use of existing models may require use of APIs for data acquisition, familiarity with file types, and aptitude for functions and systems thinking.
The project will involve both field-based and computer laboratory focused research. Courses /experience in python programming, data analytics and image processing, and particularly related to remote sensing technologies, are desirable. Interest in interdisciplinary research is essential.
More information: iot4ag.us
IoT4Ag P4: Frontiers in Thermal Stress Sensing
A new Engineering Research Center on the Internet of Things for Precision Agriculture (IoT4Ag) has recently been established to ensure food, energy, and water security by advancing technology to increase crop production, while minimizing the use of energy and water resources and the impact of agricultural practices on the environment. The center will create novel, integrated systems that capture the microclimate and spatially, temporally, and compositionally map heterogeneous stresses for early detection and intervention to better outcomes in agricultural crop production. The Center will create internet of things (IoT) technologies to optimize practices for every plant; from sensors, robotics, and energy and communication devices to data-driven models constrained by plant physiology, soil, weather, management practices, and socio-economics. We are looking to hire a cohort of SURF students to work on different activities in the center.
IoT4Ag P4: Frontiers in Thermal Stress Sensing
2 students - US Citizens or permanent residents only
Crop canopy temperatures are modulated by transpiration of water vapor from leaf surfaces when water exits via leaf stomata. Although thermal sensors are being deployed on drones and autonomous robots, too little is known about the relationship between evaporative cooling and stomatal conductance that can be measured directly via leaf photosynthesis assessment (e.g. with a LiCor 6400 or 6800 portable photosynthesis system) . The simultaneous and direct measurement of thermal properties of corn canopies from above the canopy and below the canopy is suggested here to coincide with leaf photosynthesis measurements. The project goal is to investigate the differential between air temperatures and both upper and lower leaf temperatures via both thermal sensors and leaf stomatal conductance assessment for corn plots under a range of water deficit conditions. Knowing these relationships could help guide the timing (diurnal and weekly frequency) for thermal canopy assessments at different growth stages. Field experiments will be established in spring 2021 at the Agronomy Center for Research and Education. Corn treatments may include both hybrid and management variables intended to create a spectrum of crop water stress. Corn biomass measurements will also be taken to study crop growth rates occurring in the actual range of “water productivity” treatments.
More information: iot4ag.us
Laboratory study of key thermal characteristics of common pharmaceutical reagents
This project is well-suited for chemical engineers interested in the pharmaceutical industry and process safety. Very few students have the opportunity to use such a calorimeter, which will stand out on resumes.
Lake Michigan Shoreline Erosion - Measurements and Modeling
With these aims in mind, this summer research project aims to leverage students' strengths to contribute to the best of their abilities. Research activities can include boat work on Lake Michigan, beach surveys with LiDAR-equipped drones, data analysis using Matlab and/or Arc-GIS, laboratory experiments involving water flumes and acoustic instrumentation, and setting up/running sophisticated computer models that aim to simulate how waves and currents move sand along the shoreline. This project is best suited for a student really interested in water, potentially setting you down a path to become a hydraulic (water) or coastal engineer, working to create more sustainable and resilient coastlines and waterways.
More information: https://engineering.purdue.edu/CE/People/view_person?resource_id=24098
Magnetic RAM for space applications
In this project, the student will develop a model to predict failures for new and emerging types of memories and logic. It will consist of models for radiation in the space environment, as well as the susceptibility of devices to various types of ionizing radiation. The end goal will be to predict failures for certain classes of devices for validation in a beam-line, which may ultimately be used to adapt off-the-shelf electronics to space applications.
Measurement and Modeling of Mass Transfer Characteristics During Pharmaceutical Lyophilization
Lyophilization is a desiccation method whereby a solvent is removed from a frozen system via sublimation. In industry, the process is typically performed at a slow rate due to large uncertainties associated with key mass transfer mechanisms. Current data is highly scattered and valid for a tightly constrained set of operating points. Frequently, these points are not optimal and the data provides little benefit to the user. Students will be responsible for computationally and experimentally characterizing the mass transfer properties of various representative pharmaceutical formulations over a range of process conditions. The goal of the project is to generalize and consolidate key results into a standardized database which will be directly integrated into LyoHUB’s LyoPronto simulation tool (http://lyopronto.rcac.purdue.edu/). The software is freely available and widely used among major pharmaceutical companies. The student will also perform benchmarking studies against current published mass transfer models.
The student will learn the basics of the freeze drying process and will get the skills of experimental work in the lab with different lyophilizers.
This project will include online meetings and hands-on lab work.
More information: www.lyohub.org
Measurement and Modeling of Vial Heat Transfer Characteristics During Pharmaceutical Lyophilization
Lyophilization is a desiccation method whereby a solvent is removed from a frozen system via sublimation. In industry, the process is typically performed over the course of days for weeks due to large uncertainties associated with key heat transfer mechanisms. Accurate determination of these heat transfer characteristics is therefore critical to fully understanding and optimizing the process. Students will be responsible for computationally and experimentally characterizing the heat transfer properties of various vial geometries under a range of process conditions. The goal of the project is to consolidate key results into a standardized database which will be directly integrated into LyoHUB’s LyoPronto simulation tool (http://lyopronto.rcac.purdue.edu/). The software is freely available and widely used among major pharmaceutical companies. The student will also perform benchmarking studies against current published heat transfer models.
The student will learn the basics of the freeze drying process and will get the skills of experimental work in the lab with different lyophilizers.
This project will include online meetings and hands-on lab work
More information: www.lyohub.org
Operation and characterization of SPT-100 Hall thruster
In this project student will work with Hall thruster SPT-100. The project will include operating the thruster and hollow cathode neutralizer, and measurements of electrical parameters of the thruster, exhaust plasma jet properties, and thrust. The student will use Langmuir probes for measurements of plasma parameters and hanging pendulum thrust stand for the thrust measurements. In addition, the student is going to prepare and update related documentation for AAE 521 Plasma Lab.
More information: https://engineering.purdue.edu/EPPL
Reliable Deep Learning Software
Machine learning systems including deep learning (DL) systems demand reliability and security. DL systems consist of two key components: (1) models and algorithms that perform complex mathematical calculations, and (2) software that implements the algorithms and models. Here software includes DL infrastructure code (e.g., code that performs core neural network computations) and the application code (e.g., code that loads model weights). Thus, for the entire DL system to be reliable and secure, both the software implementation and models/algorithms must be reliable and secure. If software fails to faithfully implement a model (e.g., due to a bug in the software), the output from the software can be wrong even if the model is correct, and vice versa.
This project aims to use novel approaches including differential testing to detect and localize bugs in DL software (including code and data) to address the testing oracle challenge. Good programming skills and strong motivation in research are required. Background in deep learning and testing is a plus.
More information: https://www.cs.purdue.edu/homes/lintan/
Resilient Extraterrestrial Habitat Engineering
The testbed will consider a habitat system and will aim to emulate the extreme temperature fluctuations that happen in deep space. To achieve this goal, a thermal transfer system is being developed, consisting of a chiller, an array of glycol lines, in-line heaters, actuated valves, and a series of sensors. Operated under a tuned controller, the thermal transfer system can cool or heat a certain surface area of the structure of the habitat to maintain a given temperature. However, to fully control the thermal transfer system is not straightforward. One of the critical challenges is its deep uncertainty, which results from inaccurate or long-delay sensors, variant test setup, complex controller design, etc. Therefore, a systematic study is needed to quantify the uncertainties to facilitate the thermal transfer system development. Emulation of a particular scenario considering a meteoroid impact will be performed, with random variations in the location and size of the impact and resulting consequences.
We are looking for undergraduate students to play key roles in this project, under the guidance of a graduate student and faculty members. The students are also expected to prepare a poster presentation on the results, and author a research paper if the desired results are achieved. Participating undergraduate researchers would be tasked to focus on the following research projects:
• Stochastic model for analyzing and exploring the behavior variability of the thermal transfer system, functioning in different scenarios.
• Experimental study to calibrate the developed model, involving parametric identification of the transfer system and experimental validation of the stochastic model.
• Numerical and experimental studies to detect and localize meteoroid impact and damage to the structure and other subsystems of the habitat, and use that information to make decisions regarding emergency actions to take.
• Numerical investigations to understand the limitations of fault damage detection methods when incomplete or erroneous sensor data is available.
More information: https://www.purdue.edu/rethi/
Smart Water for Smart Cities
SoCET: System on Chip Extension Technologies
The technical objective of the SoC Team is to create and keep improving on an SoC design that we can then customize for special application and research needs. The team's major project is that of creating an SoC that is optimized for very small scale and low power machine learning applications, but there are numerous problems one can work on including modelling of a secure SoC architecture, design of chiplets, FPGA prototyping, extending a RISCV open source processor design, testing of recent chips designed by SoCET, analog circuit design, and using industry grade design verification techniques.
More information: https://engineering.purdue.edu/SoC-Team
Study of Betavoltaic characteristics
Synthesis of energetic materials
More information: www.davinpiercey.com
The impact of COVID-19 on user perceptions of public transit, shared mobility/micro-mobility services, and emerging vehicle types.
The student will assist with literature review efforts to establish a baseline of user perceptions for public transit, shared mobility/micro-mobility services, and emerging vehicle types before the pandemic. The student will also assist the research team with analyzing the data from surveys that will include questions about travel behavior, such as change in travel habits because of new technologies, trip purpose and patterns, use of emerging and shared mobility services as well as questions related to how COVID-19 has affected these travel activities. The student will also interact with other undergraduate and graduate students at the Sustainable Transportation Systems Research (STSR) group as well as the project sponsors.
More information: https://engineering.purdue.edu/STSRG
Understanding & Reducing Major Industrial Plant Disasters
1- which less serious incidents can lead to more serious incidents, if not for luck. A key fundamental of safety analysis is if less severe incidents are eliminated the more serious ones will as well. Recent studies have shown that only a small fraction of lower level incidents can actually lead to major incidents.
2- much has been written about the 'domino effects' during incidents, typically involving flammable substances, propagating and escalating to more serious incidents (e.g., vessel rupture, followed by a fire & explosion.) This work will review the literature on domino effects and extract learnings from historic incidents that may have prevented or mitigated such incidents.
The project will culminate in a professional report on the research.