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
Description:
The project's goal is to use a Hyrel 3D printer to print out highly complex electronic circuits without any human interaction. To show the complexity of our printing method, our final print job will be a human neuron in the form of a computer chip. Undergraduate student goals will include fixing the many problems that come along the way, such as improving on the mechanics of the current printer, updating or adding a new software for printing, changing the cartridge material used, etc.
Research categories:
Medical Science and Technology, Other
School/Dept.:
School of Engineering Technology
Professor:
Richard
Voyles
Accelerator Architecture Lab at Purdue (AALP): Optimizing Simulators for Advanced Processor Development
Description:
Modern processor design and research in both industry and academia rely on early-stage modeling and simulation. The ideas that make up every CPU, GPU, and accelerator you have ever used started their life in cycle-level C++ processor simulation. Today, much of the progress we see in the processor industry comes from specialization (i.e. Google’s TPU) and acceleration (i.e. GPGPU computing, such as NVIDIA’s CUDA). The Accelerator Architecture Lab at Purdue (AALP) develops a popular open-source GPU simulator called Accel-Sim that models modern NVIDIA GPUs executing compute workloads, like those commonly used in machine learning. Intimate details of the actions taken on each cycle of a real processor are modeled in Accel-Sim’s C++ code, such that new architectural ideas can be explored and empirically evaluated on real workloads. Simulating such an advanced, scaled system consumes a significant amount of CPU-time and memory (for some workloads - on the order of a TB!). This summer project involves understanding the high-level design of GPUs, the basic operation of CUDA, and optimizing the simulator infrastructure to consume orders of magnitude less memory at runtime, enabling larger and more complex workloads to be simulated. The successful completion of the project will see the student contribute to a highly-visible piece of open-source software and develop foundational skills to work at hardware design companies like Intel, AMD, NVIDIA, Qualcomm, Microsoft, and many others.
More information: https://accel-sim.github.io
Group Website: https://engineering.purdue.edu/tgrogers/group/aalp.html
More information: https://accel-sim.github.io
Group Website: https://engineering.purdue.edu/tgrogers/group/aalp.html
Research categories:
Big Data/Machine Learning, Deep Learning, Other
Preferred major(s):
Computer Engineering, Computer Science
Desired experience:
C/C++ and Python experience
Knowledge of computer architecture a plus
School/Dept.:
ECE
Professor:
Timothy
Rogers
More information: https://engineering.purdue.edu/tgrogers/
Accelerator Architecture Lab at Purdue (AALP): Modeling Diverse GPU Architectures in C++ Simulation
Description:
Modern processor design and research in both industry and academia rely on early-stage modeling and simulation. The ideas that make up every CPU, GPU, and accelerator you have ever used started their life in cycle-level C++ processor simulation. Today, much of the progress we see in the processor industry comes from specialization (i.e. Google’s TPU) and acceleration (i.e. GPGPU computing, such as NVIDIA’s CUDA). The Accelerator Architecture Lab at Purdue (AALP) develops a popular open-source GPU simulator called Accel-Sim that models modern NVIDIA GPUs executing compute workloads, like those commonly used in machine learning. Intimate details of the actions taken on each cycle of a real processor are modeled in Accel-Sim’s C++ code, such that new architectural ideas can be explored and empirically evaluated on real workloads. Although the basic design of GPUs share many similarities across generations and vendors, each part and company have subtle differences that can greatly affect their performance on critical applications, such as those found in machine learning. This summer project involves understanding the high-level design of GPUs, the basic operation of CUDA, and modeling the performance of bleeding-edge GPU parts from both NVIDIA (an Ampere A100) and AMD. The successful completion of the project will see the student contribute to a highly-visible piece of open-source software and develop foundational skills to work at hardware design companies like Intel, AMD, NVIDIA, Qualcomm, Microsoft, and many others.
More information: https://accel-sim.github.io
Group Website: https://engineering.purdue.edu/tgrogers/group/aalp.html
More information: https://accel-sim.github.io
Group Website: https://engineering.purdue.edu/tgrogers/group/aalp.html
Research categories:
Big Data/Machine Learning, Deep Learning, Other
Preferred major(s):
Computer Engineering, Computer Science
Desired experience:
C/C++.
Computer Architecture and Digital Design Background.
Students should be comfortable reading assembly language (ECE 362 equivalent)
School/Dept.:
ECE
Professor:
Tim
Rogers
More information: https://engineering.purdue.edu/tgrogers/
Advanced Vehicle Automation and Human-Subject Experimentation
Description:
Vehicle automation is developing at a rapid rate worldwide. While fully autonomous vehicles will be pervasive on the roadway for the next several years, many research initiatives are currently underway to understand and design approaches that will make this technology a future reality. This work ranges from the development of sensors and controls algorithms, to schemes for networks and connectivity, to the creation of in-vehicle driver interfaces. Here, one component that is key to the effective design of next-generation autonomous driving systems is the human driver and, thus studying human-vehicle interactions and defining driver’s roles/tasks will be important.
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.
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.
Research categories:
Big Data/Machine Learning, Learning and Evaluation, Other
Preferred major(s):
Industrial Engineering, Mechanical Engineering, and/or Computer Science Engineering
Desired experience:
Human Factors, Matlab, transportation, some experience in statistics, some computer programming and machine learning experience (in any language)
School/Dept.:
Industrial Engineering
Professor:
Brandon
Pitts
More information: https://engineering.purdue.edu/NHanCE
Analyzing educational teamwork dataset using quantitative and NLP techniques
Description:
Teamwork is an essential competency highly valued by both academia and industry, especially for engineers who usually work in a small group. With tens of years' development, our research group, the Comprehensive Assessment of Team Member Effectiveness (CATME), had collected millions of survey data, including peer comments. The selected SURF student will join our research group to assist with data cleaning, preparation, and analysis for educational or technical research related to teamwork, and perhaps NLP (NLP is not necessary but a plus).
Research categories:
Big Data/Machine Learning, Deep Learning, Learning and Evaluation, Other
Preferred major(s):
ECE, CS, IE, education, social science, management, linguistics, and others
Desired experience:
data analysis experience with R, Python, and etc.; familiar with NLP and software programming would be a plus.
School/Dept.:
Engineering Education
Professor:
Matthew
Ohland
More information: https://info.catme.org/
Automatically Detecting and Fixing Software Bugs and Vulnerabilities
Description:
In this project, we will develop cool machine learning approaches to automatically learn bug/vulnerability patterns and fix patterns from historical data to detect and fix software bugs and security vulnerabilities. This project is partially funded by a Facebook Research Award (https://research.fb.com/programs/research-awards/proposals/probability-and-programming-request-for-proposals/).
Earlier work can be found here: https://www.cs.purdue.edu/homes/lintan/publications/deeplearn-tse18.pdf
Earlier work can be found here: https://www.cs.purdue.edu/homes/lintan/publications/deeplearn-tse18.pdf
Research categories:
Big Data/Machine Learning, Cybersecurity, Deep Learning, Other
Preferred major(s):
Compuer Science; Computer Engineering
Desired experience:
Good programming skills and strong motivation in research are required. Background in security or machine learning is a plus.
School/Dept.:
Computer Science
Professor:
Lin
Tan
More information: https://www.cs.purdue.edu/homes/lintan/
Building Software for Environmental Modeling
Description:
Agricultural and Biological Engineering Department has contributed several tools for environmental modeling community. This project involves building Graphical User Interface (GUI) to connect with various components of a recently updated environmental model. The SURF student will be required to understand the current application, create a list of changes to be made in collaboration with the project supervisor, get a head start on developing new GUIs and document the process. The SURF student will work with a staff programmer.
Research categories:
Other
Preferred major(s):
Agricultural Engineering , Civil Engineering, Computer Science or related disciplines
Desired experience:
Programming skills in any language with some experience in frontend and backend web development is desired.
School/Dept.:
Agricultural and Biological Engineering
Professor:
Dharmendra
Saraswat
Bursting of Leading Edge Vortices on Swept Wings
Description:
The vortex generated at the sharp leading edge of a swept wing at high angles of attack maintains lift by preventing the free stream from separating from the upper surface of the wing. This is important for reducing landing speeds and for enhancing the maneuver performance of fighter aircraft. However this benefit is limited to moderate angles of attack because the vortex breaks down, or bursts, at some point along its length where it changes from a strong spiral motion to a disorganized turbulent eddy with a consequent loss of lift.
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.
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.
Research categories:
Other
Preferred major(s):
Aerospace Engineering (1st choice) or Mechanical Engineering (2nd choice)
Desired experience:
Undergraduate level course in fluid mechanics is required; undergraduate level course in aerodynamics would be beneficial
School/Dept.:
AAE
Professor:
Sally
Bane
Design, construction and simulation of scaled test facility for gas cooled reactor cavity building blowdown
Description:
The main goal of the research is to develop a scaled experimental facility to study a High Temperature Gas-cooled Reactor (HTGR) building response in the event of a depressurization accident caused by a break in the primary coolant boundary and obtain first-of-a-kind data on the oxygen concentration distribution for validation of reactor safety codes and Computational Fluid Dynamics (CFD) models. It is proposed to conduct experiments in a well-scaled test facility representing reference GA-MHGTR reactor building cavities and obtain oxygen concentration as function of time and space for range of reactor building vent locations, flow paths, and break sizes, locations and orientations. To support the experimental program, it is proposed to perform analysis of the reactor building response with a system level reactor safety code complimented by a CFD analysis for detailed localized predictions. The task under this project include study of the HTGR reactor components, where actual dimensions of the systems components are collected data, using scaling design scaled facility, and perform CFD analysis. Students interested on hands on experience in the laboratory, willing to build test facility, perform experiment, and analyze data are welcome. Great opportunity to develop thermal hydraulics laboratory skills.
Research categories:
Energy and Environment, Thermal Technology, Other
Preferred major(s):
Nuclear Engineering or Mechanical Engineering or Technology
Desired experience:
Desired course work: Courses on Thermal and fluids,
Skills: Willing to work on hardware, construction of test facility, experimental work, CFD modeling, Data analysis
Desirable experience : Experience in AUTOCAD or similar tool , machining, CFD FLUENT or CFX
School/Dept.:
Nuclear Engineering
Professor:
Shripad
Revankar
Design, fabrication, and testing of an environmental chamber for X-ray characterization
Description:
High energy X-rays produced by synchrotron sources can be used to characterize the 3D microstructure and evolution of the lattice strains (and thereby stresses) in each grain during thermo-mechanical loading. For this project, we would use high energy X-rays to characterize the evolution of a fatigue crack in a corrosive environment. This project would entail the design, fabrication, and testing of an environmental chamber. The chamber would enclose the specimen in a corrosive environment, and at the same time, applying loading to the specimen. The design would need to limit the impedance of the incoming/outgoing X-ray sources during characterization.
Research categories:
Composite Materials and Alloys, Energy and Environment, Material Modeling and Simulation, Thermal Technology, Other
Preferred major(s):
AAE or ME
Desired experience:
Experience inCAD tools, structural and thermal finite element analysis. Background in Matlab or Python coding.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Michael
Sangid
More information: https://engineering.purdue.edu/~msangid/
Designing Epidemic Mitigation Methods with Limited Resources
Description:
By the end of 2020, the COVID-19 infection has caused more than one million deaths and a large amount of financial loss globally. To reduce the losses, social planners are implementing appropriate methods to mitigate the spread of the epidemic, such as developing vaccines, maintaining social distancing, quarantine, investing in effective medicines, etc. Meanwhile, each type of mitigation method has different costs and the decision-makers often have to carry out the policies under limited budgets. Our plan is to design 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.
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.
Research categories:
Learning and Evaluation, Other
Desired experience:
Preferred: Mathematical background, programming skills, data processing experience
School/Dept.:
ECE
Professor:
Philip
Pare
More information: https://sites.google.com/view/philpare/home
Epidemic Analysis Via Social Networks
Description:
Social media has significantly increased the rate at which news spreads through the population, enabling shifts in people’s beliefs towards the news. One such example is the disagreement on the severity of the disease over different communities during the COVID-19 pandemic. The contention over COVID-19 affects people’s attitudes and behaviors towards the policies and suggestions from the government and scientific institutions, respectively. Our question is if it is possible to mitigate the spreading of the epidemic by impacting the opinions over the social networks. Our proposed solution is to capture the opinions of the COVID-19 pandemic through dynamical social networks with both cooperative and antagonistic interactions. We will validate the network model with social network data. Through the data-based model, we will explore the role of opinion dissemination on epidemic spreading in reality. The undergraduate researchers will learn to model signed social networks via the opinions on COVID-19. The students will gain fundamental knowledge in systems and control, social network modeling and analysis, and hands-on experience in data collection, analysis, and model validation.
Research categories:
Big Data/Machine Learning, Learning and Evaluation, Other
Desired experience:
Preferred: Mathematical background, programming skills, data processing experience
School/Dept.:
ECE
Professor:
Philip
Paré
More information: https://sites.google.com/view/philpare/home
Epidemic Modeling and Prediction with COVID-19 Dataset
Description:
COVID-19 has been a major challenge in the year 2020 and the epidemic modeling community has yet to come up with an accurate and reliable method for epidemic spread prediction. Some difficulties of the epidemic spread prediction problem include testing delays, testing inaccuracy and feedback effects from local health authorities’ disease mitigation policies. These complexities in the dataset will lead to inaccurate prediction and poor disease mitigation strategies if not resolved properly.
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.
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.
Research categories:
Big Data/Machine Learning, Learning and Evaluation, Other
Desired experience:
Preferred: Mathematical background, programming skills, data processing experience
School/Dept.:
ECE
Professor:
Philip
Paré
More information: https://sites.google.com/view/philpare/home
Human Factors: Enhancing Performance of Nurses and Surgeons
Description:
High physical and cognitive workload among surgeons and nurses are becoming more common. The purpose of this project is to examine the contributors to these and develop technology to understand and enhance their performance.
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.
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.
Research categories:
Big Data/Machine Learning, Learning and Evaluation, Medical Science and Technology, Other
Preferred major(s):
Industrial Engineering, Computer Science, Biomedical Engineering
Desired experience:
Human Factors, Machine Learning, Sensors, Programming
School/Dept.:
Industrial Engineering
Professor:
Denny
Yu
More information: https://engineering.purdue.edu/YuGroup
IoT4Ag P1: Autonomous recharging of ground and aerial mobile agricultural robot platforms
Description:
By 2050, the US population is estimated to grow to 400 million and the world population to 9.7 billion. Current agricultural practices account for 70% of global water use, energy accounts for one of the largest costs on a farm, and inefficient use of agrochemicals is altering Earth’s ecosystems. With finite arable land, water, and energy resources, ensuring food, energy, and water security will require new technologies to improve the efficiency of food production, create sustainable approaches to supply energy, and prevent water scarcity.
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
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
Research categories:
IoT for Precision Agriculture, Other
Preferred major(s):
ME, ECE, CS
Desired experience:
Student 1: upperclassman in mechanical engineering or equivalent program; experience with mechatronics, image processing, robotics, programming
Student 2: upper level student in electrical and computer engineering or equivalent, experience with battery systems
School/Dept.:
Mechanical Enginering
Professor:
David
Cappelleri
More information: iot4ag.us
IoT4Ag P2: IsoBlue integration to UGV/UAV platforms
Description:
By 2050, the US population is estimated to grow to 400 million and the world population to 9.7 billion. Current agricultural practices account for 70% of global water use, energy accounts for one of the largest costs on a farm, and inefficient use of agrochemicals is altering Earth’s ecosystems. With finite arable land, water, and energy resources, ensuring food, energy, and water security will require new technologies to improve the efficiency of food production, create sustainable approaches to supply energy, and prevent water scarcity.
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
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
Research categories:
IoT for Precision Agriculture, Other
Preferred major(s):
ECE or CS
Desired experience:
ECE or CS with background in embedded systems with C programming
School/Dept.:
ECE
Professor:
James
Krogmeier
More information: oatscenter.org
IoT4Ag P3: Biophysical modeling and integration with in-situ and remotely sensed data
Description:
By 2050, the US population is estimated to grow to 400 million and the world population to 9.7 billion. Current agricultural practices account for 70% of global water use, energy accounts for one of the largest costs on a farm, and inefficient use of agrochemicals is altering Earth’s ecosystems. With finite arable land, water, and energy resources, ensuring food, energy, and water security will require new technologies to improve the efficiency of food production, create sustainable approaches to supply energy, and prevent water scarcity.
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.
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.
Research categories:
Big Data/Machine Learning, IoT for Precision Agriculture, Other
Preferred major(s):
ABE, CE, ECE, IE
Desired experience:
Courses /experience in python programming, data analytics and image processing, and particularly related to remote sensing technologies, are desirable.
Strong computer and math skills, preferably experience with data wrangling and visualization (Python preferred)
Interest in interdisciplinary research is essential.
School/Dept.:
CE, ECE, Agronomy
Professor:
Melba
Crawford
More information: iot4ag.us
IoT4Ag P4: Frontiers in Thermal Stress Sensing
Description:
By 2050, the US population is estimated to grow to 400 million and the world population to 9.7 billion. Current agricultural practices account for 70% of global water use, energy accounts for one of the largest costs on a farm, and inefficient use of agrochemicals is altering Earth’s ecosystems. With finite arable land, water, and energy resources, ensuring food, energy, and water security will require new technologies to improve the efficiency of food production, create sustainable approaches to supply energy, and prevent water scarcity.
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.
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.
Research categories:
IoT for Precision Agriculture, Other
Preferred major(s):
Agronomy, Botany, or other plant science field; CE/ECE
Desired experience:
Background in Agronomy, Botany, or other plant science field
CE/ECE with background in sensor-based data acquisition
School/Dept.:
Agronomy
Professor:
Tony
Vyn
More information: iot4ag.us
Laboratory study of key thermal characteristics of common pharmaceutical reagents
Description:
The understanding of chemical reactivity plays a key role in the design of pharmaceutical facilities. This project will entail taking calorimetric measurements using an Advanced Reactive System Screening Tool (ARSST). The systems studied will include various reagents commonly used in the pharmaceutical industry. Work will begin with lab safety training and familiarization with the use of the ARRST, while conducting a literature search of existing heat of reaction data for the chemical systems to be studied. Overall, the work will entail making a series of measurements of various systems at a variety of conditions and then analyzing the data using computer models.
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.
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.
Research categories:
Chemical Unit Operations, Other
Preferred major(s):
Chemical Engineering
Desired experience:
No prior training is required. Safety will be of the utmost importance in conducting this laboratory study. The researcher will take department safety training and then rigorous training on the use of the ARSST with close supervision.
School/Dept.:
Chemical Engineering
Professor:
Ray
Mentzer
Lake Michigan Shoreline Erosion - Measurements and Modeling
Description:
In the Great Lakes, water levels have been at record highs in the last few years , and the damage to the shorelines has been immense and costly (just google "Lake Michigan erosion" to see newspaper articles and videos). As engineers, we need to be better able to predict this erosion and design resilient shorelines that can withstand the huge variations in water levels that may be a consequence of climate change. The aims of this research are two-fold: (1) Quantify recent erosion along Lake Michigan's shoreline, using both direct measurements and remote sensing; (2) Develop a computational model that can predict this erosion.
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.
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.
Research categories:
Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Other
Preferred major(s):
Civil or Environmental Engineering
Desired experience:
Must love water.
Must not hate Matlab.
Must love to be outside. (no guarantees w/Covid!!)
Must be a great team member and communicate well.
Must be willing to work hard, get frustrated, and persevere.
School/Dept.:
Civil Engineering
Professor:
Cary
Troy
More information: https://engineering.purdue.edu/CE/People/view_person?resource_id=24098
Magnetic RAM for space applications
Description:
Radiation in outer space can greatly affect the operation and long-term performance of microelectronics. Radiation hardening is making electronic components and circuits resistant to damage or malfunction caused by high levels of ionizing radiation in this environment. Transient effects include single-event effects like memory bit flips; permanent effects include single-event latchups that prevent individual devices from operating.
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.
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.
Research categories:
Other
Preferred major(s):
ECE, NE, ME, MSE
Desired experience:
Experience with programming in Python, C/C++, and/or MATLAB
Enthusiasm for scientific programming
Understanding of radiation transport and electromagnetism
School/Dept.:
Electrical & Computer Engineering
Professor:
Peter
Bermel
Measurement and Modeling of Mass Transfer Characteristics During Pharmaceutical Lyophilization
Description:
Freeze-drying, also called lyophilization, is widely used in manufacturing of injectable pharmaceuticals, vaccines, biotech products, chemical reagents, food and probiotic cultures. The SURF undergraduate researchers will have an opportunity to be involved in one of the ongoing projects in LyoHUB technology demonstration facility in Discovery Park in collaboration with one or more of 20+ LyoHUB industry members.
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.
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.
Research categories:
Material Modeling and Simulation, Other
Preferred major(s):
AAE, ABE, CHE, CS, ECE, ME
School/Dept.:
CHE/AAE
Professor:
Alina
Alexeenko
More information: www.lyohub.org
Measurement and Modeling of Vial Heat Transfer Characteristics During Pharmaceutical Lyophilization
Description:
Freeze-drying, also called lyophilization, is widely used in manufacturing of injectable pharmaceuticals, vaccines, biotech products, chemical reagents, food and probiotic cultures. The SURF undergraduate researchers will have an opportunity to be involved in one of the ongoing projects in LyoHUB technology demonstration facility in Discovery Park in collaboration with one or more of 20+ LyoHUB industry members.
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
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
Research categories:
Material Modeling and Simulation, Other
School/Dept.:
CHE/AAE
Professor:
Alina
Alexeenko
More information: www.lyohub.org
Operation and characterization of SPT-100 Hall thruster
Description:
Hall thrusters are widely utilized for spacecraft propulsion. Mars exploration missions currently planned by NASA utilize Deep Space Transport which is going to be propelled by Hall thruster technology. The technology has been originally developed in Soviet Union and got adopted in the U.S. in 1990s. In Hall thruster neutral gas propellant is ionized and accelerated in ExB-field configuration to reach high propellant exhaust velocities in the range 10 - 50 km/s.
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.
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.
Research categories:
Other
School/Dept.:
AAE
Professor:
Alexey
Shashurin
More information: https://engineering.purdue.edu/EPPL
Reliable Deep Learning Software
Description:
We will build cool and novel techniques to make deep learning code such as TensorFlow and PyTorch reliable and secure. We will build it on top of our award winning project (https://www.cs.purdue.edu/homes/lintan/publications/variance-ase20.pdf), which won an ACM SIGSOFT Distinguished Paper Award! There may be opportunities to collaborate with Microsoft. See below for more details.
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.
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.
Research categories:
Big Data/Machine Learning, Deep Learning, Other
Preferred major(s):
Computer Science; Computer Engineering; Data Science
School/Dept.:
Computer Science
Professor:
Lin
Tan
More information: https://www.cs.purdue.edu/homes/lintan/
Resilient Extraterrestrial Habitat Engineering
Description:
There is growing interest from Space agencies such as NASA and the European Space Agency in establishing permanent human settlements outside Earth. To advance knowledge in the field, the Resilient Extra-Terrestrial Habitat Institute (RETHi) is taking steps to develop technologies that will enable resilient habitats in deep space, that will adapt, absorb and rapidly recover from expected and unexpected disruptions without fundamental changes in function or sacrifices in safety. To study, demonstrate, and evaluate the technologies developed in pursuit of this mission, a multi-physics cyber-physical testbed is being founded at the Ray W. Herrick Laboratories at Purdue University with collaboration from partners at three universities and two industrial partners. It allows to examine emergent behaviors in habitat systems and the interactions among its virtual (computational) and physical components.
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.
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.
Research categories:
Big Data/Machine Learning, Engineering the Built Environment, Thermal Technology, Other
Preferred major(s):
ME, AAE, CE, CS
Desired experience:
Students interested in this project should be critical thinkers, and have good experimental skills, some programming skills, CAD skills, and experience in MATLAB/Simulink.
School/Dept.:
Mechanical, Civil, Aero Eng. Departments
Professor:
Shirley
Dyke
More information: https://www.purdue.edu/rethi/
Smart Water for Smart Cities
Description:
Water is centrally important to environmental sustainability: it supports human societal needs and the natural environment, and powers the growth of economic sectors, geographic regions, and cities. Data science should be harnessed to better understand how much and where water is consumed. The undergraduate researcher will be apply methods to quantify and model industrial water consumption at fine spatial and industry-sector resolution, visualize the results with geographic information systems, and interpret the impacts of water consumption on the urban environment.
Research categories:
Big Data/Machine Learning, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Other
Preferred major(s):
EEE, CE, or IE
Desired experience:
Minimum GPA: 3.0.
Preferred majors: Environmental and Ecological Engineering, Civil Engineering, or Industrial Engineering.
Preferred coursework: CE/EEE 350 or CE/EEE 355 or EEE 250
Preferred skills: Proficiency with programming in R or Python Python, experience with ArcGIS or similar programs.
School/Dept.:
CE and EEE
Professor:
Inez
Hua
SoCET: System on Chip Extension Technologies
Description:
The processors inside your cell-phone, automobile, television, etc. are some of the most complex and smallest devices created in human history, but with access to the right tools, design techniques, and fabrication facilities you can create new capabilities to be fabricated on silicon. Such processors are implemented in the form of a System-on-Chip (SoC). Design of SoC's and access to fabrication facilities are ordinarily extremely expensive and very restricted. However, thanks to industry and governmental support, interested undergraduates are able to join in the design, fabrication, and test of custom SoC's. The primary reason for the existence of the SoC team is to give students an integrated circuit design experience that as close as possible to what they would encounter in industry.
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.
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.
Research categories:
Cybersecurity, Internet of Things, Mobile Computing, Nanotechnology, Other
Preferred major(s):
Electrical Engineering, Computer Engineering, Computer Science
Desired experience:
A wide range of circuit and software design skills are needed on this project. No one team member is expected to have this full set of skills, but to be able to contribute to some aspect of SoCET over the course of a summer, you will need skills or course work in at least the following areas. *Verilog/System Verilog coding skills for logic synthesis and test bench design, *Analog and digital integrated circuit design background including circuit simulation and layout, *testing of digital and analog circuits, *Microcontroller programming in C and assembly language, *Managing code repositories in git, *Compiler design, *Operating systems and especially Real Time Operating Systems.
School/Dept.:
ECE
Professor:
Mark
Johnson
More information: https://engineering.purdue.edu/SoC-Team
Study of Betavoltaic characteristics
Description:
The main goal of the research is to study betavoltaic cell characteristics using a facility for its voltage and current responses with load. A betavoltaic cell creates electricity similar to a photovoltaic or solar cell. Betavoltaic devices are self-contained power sources that convert high energy beta (β) particles emitted from the decay of radioactive isotopes into electrical current. In the cell the electrons are produced indirectly via the kinetic energy of the beta particles interacting within the semiconductor. The project also will involve testing on a hydrogen loading facility to simulate the tritium loading in betavoltaic cell film. The betavoltaic used are commercial cells that are tested in a radiation approved facility. Experimental facility preparation, testing and data acquisition is needed. Students interested on hands on experience in the laboratory, willing to build test facility, perform experiment, and analyze data are welcome. Great opportunity to develop radiation laboratory skills.
Research categories:
Energy and Environment, Material Modeling and Simulation, Other
Preferred major(s):
Nuclear Engineering
Desired experience:
Desired course work: Courses on basic nuclear engineering, radiation shielding, : Willing to work on hardware, experimental test facility modeling, Data analysis Desirable experience : Experience in MATLAB and data analysis. Required: Radiation training in handling sealed radiation material.
School/Dept.:
Nuclear Engineering
Professor:
Shripad
Revankar
Synthesis of energetic materials
Description:
Synthesis of energetic materials and related precursors.
Research categories:
Other
Preferred major(s):
Chemistry, Materials Engineering, Mechanical Engineering, Chemical Engineering.
Desired experience:
Organic chemistry lab
School/Dept.:
MSE
Professor:
Davin
Piercey
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.
Description:
The objective of this project is to investigate the impact of COVID-19 on user perceptions of public transit, shared mobility services, and emerging vehicle types (electric, connected, and autonomous vehicles). As transportation systems remain at the forefront of the COVID-19 pandemic, it is critical to examine the transportation trends and behaviors of shared modes’ and emerging vehicle types’ users to best plan for transportation policies in the long-run. This study aims to provide a well-documented and easy-to-use framework that can support both planning and policy decisions in order to enhance urban shared mobility by better understanding the attributes which are affected and providing alternative options. The developed research framework will be applied in three urban areas with different transportation systems and densities, and corresponding policy and planning implications will be compared and contrasted.
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.
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.
Research categories:
Energy and Environment, Engineering the Built Environment, Other
Preferred major(s):
Engineering or Statistics
Desired experience:
survey design, data analytics, statistics; good oral and written communication skills; experience working with diverse teams
School/Dept.:
CE/ABE
Professor:
Konstantina (Nadia)
Gkritza
More information: https://engineering.purdue.edu/STSRG
Understanding & Reducing Major Industrial Plant Disasters
Description:
Purdue is well known in industry for its focus on chemical process safety and home of the 'Purdue Process Safety & Assurance Center' which aims to eliminate major industrial incidents, such as fires & explosions. Numerous undergraduates, MS and PhD students are currently working on various projects with direct industry engagement. This project will examine major incidents in terms of:
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.
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.
Research categories:
Chemical Unit Operations, Other
Preferred major(s):
Chemical Engineering
Desired experience:
No required course work, but researcher must be self motivated, well organized and thorough in discerning key facts from articles.
School/Dept.:
Chemical Engineering
Professor:
Ray
Mentzer