Tribology is a discipline that studies friction, lubrication, and wear. Those topics affect almost all machines that have moving parts. For example, in fluid power application, which consumes 3% of the energy contributes 8% of the greenhouse gas, the lubricating interface tribological behavior of the positive displacement machines determines the system's total efficiency. The lubricating interface is formed by two solid boundaries a few microns apart. Therefore, the deformation of the solid bodies is crucial to the friction and wear of the sliding interface. The objective is to explore the nonlinear elastic deformation of the lubricating interface solid boundaries using commercial FEA software. The challenge is to generate enough simulation data to train a machine-learning algorithm. Your work will constitute a new modeling approach in the fluid power field. Therefore, it is also very possible to be published with you as a co-author. The main tasks are 1) familiarize yourself with the CAD and FEA software, 2) learn how to conduct batch FEA simulations, 3) generate code to pre- and post-process the simulation result, and compare simulation results with different simulation assumptions.

Fluid Modelling and Simulation, Material Modeling and Simulation

• No Major Restriction

->Some coursework in Solid Mechanics would help speed up the initial inertia. ->Proficient in MATLAB (pre- and post-process simulation data) ->Basic experience with stress analysis using Ansys.

Mechanical Engineering or Agricultural and Biological Engineering

Lizhi Shang

Ethylene is one of the most important building blocks of the chemical industry1. Its global market was estimated at ~160 million Tons in 2020 and it is forecast to reach ~210 million Tons by 20272. Between 1.0 and 1.6 tons of CO2 are emitted per ton of Ethylene produced. This means Ethylene production accounted for around 0.47-0.75% of the world’s total carbon emissions in 2020, estimated at 34 billion tons3. The U.S. has set a course to reach net-zero emissions economy-wide by no later than 20507,8. This makes it imperative decarbonizing Ethylene production.
Ethylene is mainly produced by Steam Cracking (SC), where hydrocarbons transform into ethylene in the presence of steam at high temperatures11. SC normally implements hydrocarbon combustion to produce the necessary energy for reaction. This is the main reason why SC emits so much CO21. The NSF Center for Innovative and Strategic Transformation of Alkane Resources (CISTAR)5 is currently researching the coupling of SC with renewable electricity. This would allow a significant reduction of CO2 emissions during SC4.
As part of its research, CISTAR carries out detailed Computational Fluid Dynamics (CFD) simulations. This allows evaluating the impact of fluid behavior during reactions. Several geometries are currently under evaluation. As part of the SURF Program, CISTAR is interested in recruiting one student to support the CFD simulations team. The goal is to evaluate the performance of the different reactor geometries considered, as well as propose potentially attractive new configurations. No previous experience with CFD simulations is necessary. However, it is advisable the student has a strong motivation for computer simulations. Experience working with Ansys Fluent and Aspen Plus could be beneficial.

Chemical Unit Operations, Energy and Environment, Fluid Modelling and Simulation, Material Modeling and Simulation, Thermal Technology

• Chemical Engineering
• Mechanical Engineering
• Electrical Engineering

• It is advisable the student has a strong motivation for computer simulations • Experience working with Ansys Fluent and Aspen Plus could be beneficial

Davidson School of Chemical Engineering

Rakesh Agrawal

Water and energy are tightly linked resources that must both become renewable for a successful future. The United Nations predicts that 6 billion people will face water scarcity by 2050. This warrants the need to develop efficient and realizable engineering solutions for desalination using the vast availability of solar energy.
This project aims to design, prototype, and test novel configurations for membrane-based desalination (reverse osmosis), powered by solar-thermal engines. The student will be part of a team of graduate and undergraduate students responsible for process design, thermal-fluid modeling and simulation, hydraulic circuit prototyping and testing, and experimental data analysis.
All students will be required to read relevant, peer-reviewed literature and keep a notebook or log of weekly research progress. At the end of the semester or term, each student will present a talk or poster on their results.

Ecology and Sustainability, Energy and Environment, Fluid Modelling and Simulation, Internet of Things, Nanotechnology, Thermal Technology

• No Major Restriction

Applicants should have an interest in thermodynamics, water treatment, and sustainability. Applicants with experience in some (not all) of the following are preferred: experimental design and prototyping, manufacturing, Python, LabView, EES, MATLAB, 3D CAD Software, & Adobe Illustrator. Rising Juniors and Seniors are preferred.

Mechanical Engineering

David Warsinger

Machine learning, image processing, fluid-structure interaction, linear and nonlinear deformation, elasto-hydrodynamic lubrication... You may have learned or have heard some of those topics. But you may never see how those interdisciplinary techniques can be used together to solve a real-life engineering problem. This project offers you a unique experience to participate in my research group developing a first-in-kind machine learning-based simulation model for nonlinear contact problems in high-pressure hydrostatic machines. The objective of the SURF project is to create a machine learning algorithm capable of fast predicting the two-dimensional, nonlinear deformation distribution from the pressure distribution. The project will be rewarding and challenging, and your work will constitute a new modeling approach in the fluid power field. Therefore, it is also very possible to be published with you as a co-author.

You will be challenged to 1) learn to program a machine learning algorithm in TensorFlow, 2) generate a training dataset for machine learning models using a state-of-the-art numerical simulation tool, and 3) integrate the neural network into the existing modeling suite.

You will be supported by your graduate mentor, who specializes in these topic areas and will provide guidance throughout the project. You will also be supported by a group of 8 developers of the hydrostatic machine modeling toolset that are working on different aspects of the code.

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

• No Major Restriction

• Intermediate knowledge of fluid mechanics • Basic programming knowledge (Python, MATLAB, C++, or similar languages)

Mechanical Engineering

Lizhi Shang

Simulation software development has become an important skill for engineering researchers. However, unlike your coding class, you will not be the first person to contribute to the code in most software development scenarios. This SURF project allows you to experience simulation software development for a real-life application from an in-house developed API (roughly 75,000 lines already in place, current 8 active developers). The real-life application is a tribology test rig that will test friction and wear with pressurized fluid (up to 500 bar). Your 'customer' is a Ph.D. student who designed the test rig and is working with a manufacturing company to ensure the device will arrive in the lab by September. The 'product' will be a simulation software for this test rig that can predict friction, leakage, pressure, thermal behavior of the test rig, considering hydrodynamics, elastic deformation, macro and micro motions, fluid properties, and thermal condition. Your graduate mentor will guide you through the simulation API (you are not expected to understand the entire 75k lines of code) and a series of examples that will equip you with all the knowledge on the coding side. At the same time, you will need to actively communicate with your 'customer' to understand the dynamic and kinematic of the engineering problem. Your work will constitute world-leading tribological research in the fluid power field. Therefore, it is also very possible to be published with you as a co-author.

Fluid Modelling and Simulation, Material Modeling and Simulation

• No Major Restriction

Familiarity with C++, MATLAB, Reading CAD models Some coursework related to Fluid Mechanics.

Mechanical Engineering

Lizhi Shang

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, Energy and Environment, Environmental Characterization, Fluid Modelling and Simulation

• Physics
• Planetary Sciences
• Atmospheric Science/Meteorology
• Computer Science
• Mathematics - Computer Science
• Mathematics
• Environmental Geosciences
• Mechanical Engineering
• Civil Engineering
• Aeronautical and Astronautical Engineering
• Computer Engineering
• Engineering (First Year)
• Multidisciplinary Engineering
• Natural Resources and Environmental Science (multiple concentrations)

Familiar with Machine Learning or prior knowledge of convolutional neural networks (CNNs); Have basic level training on PHYS172 Modern Mechanics or PHYS 15200 Mechanics or equivalent courses from other institutions; Familiar with Python scripting and visualization

Earth, Atmospheric, and Planetary Sciences

Lei Wang

Water and energy are tightly linked resources that must both become renewable for a successful future. However, today, water and energy resources are often in conflict with one another, especially related to impacts on electric grids. Further, advances in nanotechnology, material science and artificial intelligence allow for new avenues to improve the widespread implementation of desalination and water purification technology. The team is pursuing multiple projects that aim to explore solar and wind-powered desalination, nanofabricated membranes, light-driven reactions, artificial intelligence control algorithms, and thermodynamic optimization of energy systems. The student will be responsible for fabricating membranes, building hydraulic systems, modeling thermal fluid phenomenon, analyzing data, or implementing control strategies in novel system configurations. More information here: www.warsinger.com

Big Data/Machine Learning, Chemical Catalysis and Synthesis, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Fluid Modelling and Simulation, Material Modeling and Simulation, Nanotechnology, Thermal Technology

• Mechanical Engineering
• Civil Engineering
• Environmental and Ecological Engineering
• Chemistry
• Chemical Engineering
• Materials Engineering

Applicants should have an interest in thermodynamics, water treatment, and sustainability. Applicants with experience in some (not all) of the following are preferred: experimental design and prototyping, manufacturing, Python, LabView, EES, MATLAB, 3D CAD Software, & Adobe Illustrator. Rising Juniors and Seniors are preferred.

Mechanical Engineering

David Warsinger

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

• Mechanical Engineering

ME 308/ME 309: Fluid Mechanics

Mechanical Engineering

Steven Wereley

The main goal of the research is to perform tests on an experimental facility that simulates nuclear reactor building response in the event of a depressurization accident caused by a break in the primary coolant boundary of a high temperature nuclear reactor and obtain first-of-a-kind data on the oxygen concentration distribution for validation of reactor safety codes and Computational Fluid Dynamics (CFD) models.
The SURF researcher will: (i) Participate in an experimental testing program along with team members- test preparation-that include checking loop, instruments, conduct of tests, and data acquisition. (ii) Suppot data analysis. (iii) Support CFD analysis using ANSYS-FLUENT

Energy and Environment, Fluid Modelling and Simulation

• Nuclear Engineering
• Mechanical Engineering

Fluid mechanics, thermodynamics, heat transfer, nuclear engineering courses. Prefer previous experience in Experimental work on thermal and fluid systems, CFD such as ANSYS - FLUENT. Willingness to learn and work with a team on a thermalhydraulics test facility

School of Nuclear Engineering

Shripad Revankar

The continued miniaturization of electronic devices, with expanded functionality at reduced cost, challenges the viability of products across a broad spectrum of industry applications. The electronics industry is driven by global trends in storage, transmission, and processing of extreme quantities of digital information (cloud computing, data centers), increasing electrification of the transportation sector (electric vehicles, hybrid aircraft, batteries), and the proliferation of interconnected computing devices (mobile computing, IoT, 5G). Proper thermal management of electronic devices is critical to avoid overheating failures and ensure energy efficient operation. In view of these rapidly evolving markets, most of the known electronics cooling technologies are approaching their limits and have a direct impact on system performance (e.g., computing power, driving range, device size, etc.).

Research projects in the Cooling Technologies Research Center (CTRC) are exploring new technologies and discovering ways to more effectively apply existing technologies to addresses the needs of companies and organizations in the area of high-performance heat removal from compact spaces. One of the distinctive features of working in this Center is training in practical applications relevant to industry. All of the projects involve close industrial support and collaboration in the research, often with direct transfer of the technologies to the participating industry members. Projects in the Center involve both experimental and computational aspects, are multi-disciplinary in nature, and are open to excellent students with various engineering and science backgrounds. Multiple different research project opportunities are available based on student interests and preferences.

Big Data/Machine Learning, Energy and Environment, Fluid Modelling and Simulation, Material Modeling and Simulation, Nanotechnology, Thermal Technology

• No Major Restriction

School of Mechanical Engineering

Justin Weibel