There are two related projects, both focused on making air safe, including from bioaersols like COVID.

1) Photocatalysis for Air Purification: Photocatalysis is one method for helping degrade harmful airborne particles, like COVID-19, which our lab is investigating in a partnership with a start-up company. Undergraduates interested in designing experimental setups and microbiological experiments are well-suited for this project. Candidates with experience in culturing microorganism/relevant wet lab experience is preferred.

2) Acoustic removal of aerosols: Sound waves can interact with small particles like aerosols, and be used to manipulate their motion. In this project, we aim to invent the first system that can make air safe with sound waves.

Biological Characterization and Imaging, Biological Simulation and Technology, Energy and Environment, Engineering the Built Environment, Fluid Modelling and Simulation, Material Modeling and Simulation, Material Processing and Characterization, Nanotechnology

• No Major Restriction

All applicants should have an interest in photochemistry, microbiology, aerosol sciences, and experimental research. In addition to the required skills mentioned in the points above, applicants with additional experience with some of the following programs are preferred: Python and Adobe Illustrator. What experience will you gain? • Hands on research experience and potential co-authorship in high impact journals • Application of engineering fundamentals to important societal problems • Research credit hours (and potential opportunities for financial compensation in the summer) • Networking opportunities with academic and industry leaders

Mechanical Engineering

David Warsinger

In the coming years, countries around the world will make concerted efforts to decarbonize various industries and technologies to help prevent and reverse climate change. Currently, thermal dehydration accounts for 10-20% of all industrial energy consumption and relies heavily on the combustion of fossil fuels. Vapor compression heat pumps, like those used in building air conditioners, offer a high-efficiency, electrically driven heat source for industrial drying applications, however there are many barriers preventing broad implementation. Our team at Purdue has proposed a new thermal drying system concept that employs unique materials and exploits clever thermodynamic design to provide up to 40% energy and emissions savings. As part of this work, we are developing system models/simulations, designing and building prototype systems, and performing advanced materials research, thus providing a breadth of exciting opportunities for aspiring scientists and engineers. This research is also heavily tied to our work on energy efficient thermal systems for buildings and water/energy sustainability, and the student who joins the project will be exposed to many research topics within the Water-Energy Nexus.

Composite Materials and Alloys, Energy and Environment, Engineering the Built Environment, Fluid Modelling and Simulation, Material Modeling and Simulation, Material Processing and Characterization, Microelectronics, Nanotechnology, Thermal Technology

• No Major Restriction

Applicants should have a general interest in energy and sustainability. Should also have a strong background/interest in thermodynamics, heat transfer, and/or materials science. Applicants with experience in some (not all) of the following are preferred: LabVIEW, Python (Jupyter, Google Colab, etc.) Engineering Equation Solver, MATLAB, 3D-CAD Software, prototype design/manufacturing, and Adobe Illustrator. 2nd semester Sophomores, Juniors, and 1st semester Seniors are preferred.

Mechanical Engineering

Jim Braun

High temperature gas-cooled reactors (HTGR) designs are likely candidates for the Next Generation Nuclear Plant (NGNP) due to their varied potential applications including process heat for chemical reactions and direct (Brayton) cycle power conversion. An HTGR fault that needs study is a break in the primary coolant boundary that leads to depressurization of the reactor vessel and loss of forced cooling of the core. In this accident, although most air in the reactor cavity and surrounding building is initially swept out by the helium, any remaining air in this space and the air re-entering from the surrounding building cavities can enter the primary coolant circuit through the break, and can cause severe damage to the graphite structures via oxidation. The amount of air entering the pressure vessel is a complex function of the primary helium inventory, and the discharged helium that displaces, and mixes, with the air in the cavity. A test facility is now built to simulate these phenomena and currently tests are conducted. The SURF students will help in conducting tests using test procedures, and acquiring data for various test condition such as helium flow rate, pressure and temperature and preform data analysis. Adequate training and background will be provided to perform the tests. It is team project with faculty, graduate students and undergraduate students.

Energy and Environment, Fluid Modelling and Simulation, Thermal Technology

• Mechanical Engineering, Nuclear Engineering, Chemical Engineering, Indutrial Engineering, Electrical Engineering
• Nuclear Engineering
• Physics

Course work in fluid mechanics, heat transfer desirable, aptitude to work on experiments, interest in developing laboratory skill, willing to work in team and learn

School of Nuclear Engineeing

Shripad Revankar

Atmospheric blocking events and 'Bomb Cyclones' are an important contributor to high impact extreme weather events. Both these weather extremes lead to heat waves, cold spells, droughts, and heavy precipitation episodes, which have dire consequences for the public health, economy, and ecosystem. For example, the blocking-induced heat waves of 2003 in Europe led to tens of thousands of human casualties and tens of billions of dollars of financial damage.

Traditionally, prediction of extreme weather events is based on direct numerical simulation of regional or global atmospheric models, which are expensive to conduct and involve a large number of tunable parameters. However, with the rapid rise of data science and machine learning in recent years, this proposed work will apply convolutional neural network to an idealized atmospheric model to conduct predictability analysis of extreme weather events within this model. With this proposed machine-learning algorithm, our project will provide a robust forecast of heat waves and atmospheric blocking with a lead-time of a few weeks. With more frequent record-breaking heat waves in the future, such a prediction will offer a crucial period of time (a few weeks) for our society to take proper preparedness steps to protect our vulnerable citizens.

This project is based on developing and verifying the machine learning algorithm for detecting extreme weather events in an idealized model. We will use Purdue’s supercomputer Bell to conduct the simulations. The undergraduate student will play an active and important role in running the idealized model, and participate in developing the algorithms. As an important component of climate preparedness, the proposed work aims to develop a physics-informed machine learning framework to improve predictability of extreme weather events.

Closely advised by Prof. Wang, the student will conduct numerical simulations of an idealized and very simple climate model, and use python-based machine learning tools to predict extreme weather events within the model. Prof. Wang will provide weekly tutorial sessions to teach key techniques along with interactive hands-on sessions. The students will get access to the big datasets on Purdue’s Data Depot, analyze and visualize data of an idealized atmospheric model. The student will use convolutional neural networks (CNNs) to train and assess a Machine-Learning model. The student will further use feature tracking algorithm to backward identify the physical structure in the atmosphere that is responsible for the onset of extreme weather events.

Big Data/Machine Learning, Deep Learning, Fluid Modelling and Simulation

• No Major Restriction

Earth, Atmospheric, and Planetary Sciences

Lei Wang

Continuous random walks (CRW) – i.e. processes with diffusion and drift – are ubiquitous
in chemistry, appearing in a wide range of fields such as heat and mass transfer, Brownian dynamics (BD) simulations, polymer physics, nucleation theory, and chemical
reaction pathways. Oftentimes in the aforementioned applications, one is concerned with simulating specific types of rare events such as random paths that stay within a particular region of phase space, those which end in a particular region, or those which reach one region before another. Illustrative examples include (a) generating polymer conformations with a specific topology (e.g., rings); (b) examining random pathways in a reaction coordinate space that produce one product compared to others (e.g., polymorphs in crystallization); (c) examining diffusion trajectories of proteins that stay in a region for a sufficiently long time before reaction occurs.

In this project, we are thinking of ways to generate such rare pathways efficiently. The SURF student will work with a graduate student to develop efficient approximations for random walks with a constraint, by examining the partial differential equations that describe different random walks. The student will also look at some example problems in polymer physics where this application could be used.

Biological Simulation and Technology, Fluid Modelling and Simulation

• Chemical Engineering
• Physics
• Mathematics
• Mechanical Engineering
• Materials Engineering
• Mechanical Engineering
• Chemistry

The student should have a background in differential equations, probability, and a basic knowledge of coding. Knowledge in partial differential equations is desired (if possible).

Chemical Engineering

Vivek Narsimhan

This project is one of several SCALE SURF research projects. SCALE projects are restricted to students who are U.S. Citizens. By applying to this project, you can be considered for any of the SCALE projects with one application. See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.

For the typical 3D integration scheme, die-to-wafer bonding is a key technology that can enable the stacking of different chips, such as logic, memory, or power devices. Compared with wafer-to-wafer bonding, it is challenging for die-to-wafer bonding to achieve high throughput while maintaining a high alignment accuracy. Researchers have been investigating different self-alignment technologies to improve the high-precision chip alignment accuracy for die-to-wafer bonding, such as Surface tension-driven with hydrophilic chip surfaces. In this topic, we will explore innovative self-alignment methods for advanced die-to-wafer bonding, enabling high throughput heterogeneous integration.

Reference: Fukushima, Takafumi, et al. "Self-assembly technologies with high-precision chip alignment and fine-pitch microbump bonding for advanced die-to-wafer 3D integration." 2011 IEEE 61st Electronic Components and Technology Conference (ECTC). IEEE, 2011.)

In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.

For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.

Advanced Packaging, Composite Materials and Alloys, Fluid Modelling and Simulation, Heterogeneous Integration, Material Modeling and Simulation, Material Processing and Characterization, Microelectronics, Nanotechnology, Thermal Technology

• Electrical Engineering
• Mechanical Engineering
• Materials Engineering

1. Microelectronics, micro/nanotechnology courses 2. Clean room fabrication experience 3. Enthusiasm for material fabrication and characterizations 4. Familiar with SEM, TEM analysis 5. Fluid mechanics Academic Years Eligible: Rising juniors and seniors with the desired experience will be preferred, but rising sophomores are also eligible to apply.

ME

Tiwei Wei

Modern machine learning techniques, like deep learning, require a high amount of data for the training process. Acquiring a high amount of experimental data can be a resource-intensive task and can slow down deep learning workflows. For fluids mechanics problems, synthetic data can be used as a good substitute for experimental data as it can be generated in a way that follows the underlying physics of the problem by following the flow behavior. Additionally, the images can be generated to mimic the distribution of experimental data and hence minimize distribution shift. This project aims to develop a software module to generate synthetic particle image data that follows the physics of underlying flows and mimics experimental data for a low distribution shift.

Fluid Modelling and Simulation

ME 308/ME 309: Fluid Mechanics

Mechanical Engineering

Steven Wereley