Aero Project 1: Develop a Simulation Environment Using MeshCat.jl for 3D Visualization in Apple Vision Pro

Faculty Mentor: Giusy Falcone, [email protected] 

Prerequisites: Experience with Julia programming; familiarity with spacecraft dynamics and control systems; basic understanding of 3D visualization tools.

Project Description: The objective of this project is to build a 3D visualization tool using MeshCat.jl in Julia to visualize robotics systems, specifically spacecraft dynamics and control, within the Apple Vision Pro environment. The student will integrate the ABTS simulator with MeshCat.jl to provide real-time, interactive 3D visualizations of spacecraft trajectories and attitude.

Aero Project 2: Integrate Apple Vision Pro with Motion Capture (Mocap) Systems 

Faculty Mentor: Giusy Falcone, [email protected] 

Prerequisites: Experience with motion capture technology.

Project Description: This project aims to develop a system that combines Apple’s Vision Pro with motion capture (Mocap) technology to track physical objects or users and integrate them into extended reality (XR) environments. The student will work on achieving precise alignment between virtual and physical spaces, enabling interactive simulations where physical movements are reflected in the virtual environment.

Aero Project 3: Develop a Synthetic Earth Image Generator Using Blender for AI Training

Faculty Mentor: Giusy Falcone, [email protected] 

Prerequisites: Understanding of AI training requirements; interest in remote sensing.

Project Description: This project involves creating a tool that generates high-resolution, realistic Earth images using Blender. The tool will incorporate randomized cloud patterns, lighting conditions, and environmental features to build a diverse dataset for AI model training in remote sensing and pattern recognition.

Aero Project 4: Implement Machine Learning Models for Fast Keplerian Orbit Solutions

Faculty Mentor: Giusy Falcone, [email protected] 

Prerequisites: Strong foundation in orbital mechanics; experience with machine learning frameworks; programming skills in Python or Julia.

Project Description: This project aims to develop machine learning models that quickly solve Keplerian orbit equations by approximating solutions using neural networks. The student will generate datasets of orbital elements and corresponding positions, train models to predict orbital states and assess the approach’s accuracy and computational efficiency.

Aero Project 5: Create an Educational Module on AI and XR Technologies in Space Applications

Faculty Mentor: Giusy Falcone, [email protected] 

Prerequisites: N/A

Project Description: This project focuses on developing educational materials introducing students to AI and XR technologies in space missions. The student will create lectures, tutorials, and lab exercises, offering hands-on experience with tools like Unity, Blender, MeshCat.jl, and AI frameworks.

Aero Project 6: Hardware Testbed Development for Safe Inspection of Cislunar Stations

Faculty mentor: Oliver Jia-Richards, [email protected]

Prerequisites: Familiarity with controls (e.g., AEROSP 470) and hands-on electronics experience are desired.

Project description: Future orbital outposts, like Lunar Gateway, will provide a stepping stone for human and robotic exploration of cislunar space and beyond. However, these stations will potentially be uncrewed for extended periods of time, and demand significant robotic autonomy to handle maintenance, interior payload management, and more. Operating in close proximity to such structures, on-orbit inspection and servicing small spacecraft offer a solution to external surveying and automated servicing and repair. Such tasks will require responsiveness to online system model changes including both payload manipulation tasks, like repair and repositioning, and robustness to failures (e.g. thruster failure) while performing surveying. Candidate control algorithms are actively under development in order to enable spacecraft to adapt to model changes and avoid potential collisions between the free-flying small spacecraft and the station. This SURE project will focus on the building of a hardware testbed based on a single-axis air spindle for testing of candidate control algorithms. The primary work will be on designing and building an air bearing platform with actuators, sensors, and on-board computing. Extended work, time permitting, will involve testing of simple control algorithms on the air bearing platform to determine their performance subject to thruster degradation or failure.

Research mode: In-person

Aero Project 7: Flight Simulation and Visualization of Very Flexible Aircraft

Faculty mentor: Prof. Carlos Cesnik

Prerequisites: The ideal candidate will have a passion for flying and flight simulation.Interested in computational analysis and creative visualization techniques. The project builds towards flight simulation capabilities focused on very flexible aircraft. Advanced experience with Python, MATLAB and/or C++ is desirable. Junior or Senior standing is preferred

Project Description: Computer simulation tools, particularly novel, research-based academic software, suffer from a lack of real-time visualization capabilities. This project will leverage an existing flight simulation tool developed at U-M’s A2SRL and link its output to a professional visualization environment (Unreal Engine 5, a video game development engine used to create realistic visuals). The student will build APIs to interact with the results of and visualize flight simulations to enable tracking the aircraft (and their dynamic deformations) during flight. The outcome will be full 3D deformable mesh(es) representing the aircraft as it undergoes flight and an automated postprocessing method to interact with the results of such flight simulations. The student is expected to document their work in a timely and efficient manner and will be provided with helpful mentorship and guidance throughout the semester.

Research Mode: In-person or hybrid.

Aero Project 8: Stress Testing and Hardening the US National Airspace System for Safe, Efficient, and Resilient Growth

Faculty Mentor: Max Li, [email protected]

Prerequisites: General mathematics and/or computer programming maturity; Knowledge of/experience with optimization (e.g., LPs), large language models, and aviation is a plus.

Project Description: This SURE project will be part of the NASA University Leadership Initiative (ULI) Center for Air Transport Resilience (CATRes). The student will work with Prof. Max Li on one out of two projects (to be chosen by the student). One project relates to using generative models for strategic air traffic management planning. The other project relates to enhancing air transport links through multimodal options (e.g., gate-to-gate buses such as Landline).

Aero Project 9: Facility upgrades for advanced in-space propulsion testing

Faculty mentor: Benjamin Jorns

Prerequisites: Hands-on project work in both mechanical and electrical engineering. Programming experience in MatLab and LabView.

Project Description: The purpose of this project is to support the Plasmadynamics and Electric Propulsion Laboratory as we upgrade our facilities to support testing of electric propulsion systems at higher powers (> 10 kWe).  Projects under this heading will be related to performing thermal and flow analysis of the vacuum test chamber, installing and testing new pumping surfaces, and possibly exploring advanced cooling methods.  

Aero Project 10: Flow system for non-conventional propellants in electric propulsion system

Faculty mentor: Benjamin Jorns

Prerequisites: Hands-on project work in both mechanical and electrical engineering. Programming experience in MatLab and LabView. 

Project Description: The purpose of this project is to assist the faculty mentor and his graduate students to develop a flow system for operating Hall thrusters on non-conventional propellants.  The project will include the design, implementation, and subscale testing of the system with our electric propulsion thrusters.

Aero Project 11: Modeling of Long-term Contrail Evolution and Environmental Impact

Faculty Mentor: Mirko Gamba

Prerequisites: Ideal candidate will have a background in fluid dynamics, numerical methods, and data processing. Experience with Python required. 

Description: A significant percentage of aviation climate impact is from contrail formation, not just greenhouse gas emissions. This project will use existing tools and develop new ones to improve modeling of contrail formation, evolution, and long-term climate impact. Contrail prediction models will be used to simulate contrail evolution using flight data and weather conditions. The student will use these tools to develop a long-term prediction method for long time horizon impact of varied aviation engines and flight paths. The eventual goal is optimization of aircraft parameters to minimize contrail climate impact. 

Research Mode: In-person

Deliverables: Final report of activities, the model developed in this effort, and results demonstrating the model in parametric studies.

Aero Project 12: Empirical Study of Contrail Formation and Evolution

Faculty Mentor: Mirko Gamba

Prerequisites: Ideal candidate will have a background in experimentation and laboratory work, and computer science. Python experience required. Experience in data processing desired. 

Description: A significant percentage of aviation climate impact is from contrail formation, not just greenhouse gas emissions. In this project the student will design an apparatus to gather contrail data and study contrail formation from flights above FXB. They will design and build a system to image the sky and monitor for the presence of contrails, record the contrail evolution, and ambient conditions. Then, they will develop an algorithm to identify properties of the control and to map the contrail to the most likely flight that generated it. The student will have the opportunity to compare findings to existing models and draw conclusions about the efficacy of existing contrail prediction approaches based on their findings.

Research Mode: In-person

Deliverables: Design of apparatus, fabricated apparatus, algorithm to control apparatus, collect image and post-process images, final report of activities.

Aero Project 13: Development of Machine Learning Methods for the Detection and Tracking of Shock Waves

Faculty Mentor: Mirko Gamba

Prerequisites: Ideal candidate will have a background in experimentation and laboratory work, numerical methods, ML and computer science. Python experience required. Experience in data processing desired. Experience in compressible flow desired

Description: In high-speed air-breathing propulsion systems, the internal flow of the engine evolves through a complex system of shock waves that leads to the combustor. This system of shock waves is referred to as the shock train. The shock train is supported by the combustion process and it is affected by the boundary layers that develops along the flowpath. This system of waves is not stationary, but it is characterized by unsteadiness that originates from both local disturbances and disturbances that originate from the combustor or the inlet. The scope of this project is to use machine learning methods to construct a shock motion detection algorithm from a sparse set of measurements of pressure variation along the length of the isolator. A successful 

Research Mode: In-person

Deliverables: Final report of activities, the algorithm developed in this effort, and results demonstrating the algorithm based on existing or new measurements in an isolator model.

Aero Project 14: Model-Based Systems Engineering (MBSE) Lab Facility Development

Faculty Mentor: Prof. George Halow

Prerequisites: Demonstrated experience with hardware builds and debugging, software. Strong English language writing skills. Prior experience (and high grade) in AEROSP 288/388 a significant plus.

Project Description: Procurement, installation, and debugging of equipment to supplement the Aerospace Engineering MBSE Leadership Lab. Types of equipment will include high-end computing and processing machines for high-fidelity systems modeling, as well as verification hardware facilities and devices (e.g. optical scanners, PCB printer). May be more than one position could be accommodated – potentially including effort software and applications (Siemens Teamcenter tools, Dassault 3Dx tools, Cameo requirements tool,digital engineering dashboard) in addition to laboratory hardware. It is expected that the SURE student(s) will write clear and concise instructional manuals, for safe and efficient operation of all equipment staged (plus potentially others), as well as instructional guides for labs, and grading rubrics, and possibly some lecture material.

Research Mode: In-person, in laboratory; some limited virtual work can be accommodated. Safety training will likely be required.

Aero Project 15: Model-Based Systems Engineering (MBSE) Pedagogical Material Development

Faculty Mentor: Prof. George Halow

Prerequisites: Prior experience (and high grade) in AEROSP 288/388. Strong oral and written communication in English.

Project Description: This project will involve research into formal application methods of model-based systems engineering (MBSE) and complex product development. The research results will be reviewed with the faculty mentor, and then converted into enhanced teaching material. Specific tasks will include:

•  Researching formal application methods of model-based systems engineering (MBSE) and complex product development.
• Interfacing with corporate sponsor partners on both existing and proposed new content.
• Completion of enhancements to course material, identified through student and corporate sponsor surveys as well as instructional staff experiences/notes.
• Creating and proving out teaching materials for delivery in Fall, 2025
• Development of the Syllabus for Fall, 2025
• Creation of the Canvas sites for both AEROSP 288 and 488 for Fall, 2025
•  (may likely include some of the same work for AEROSP 200)

Research Mode: In-person, in laboratory; some limited virtual work can be accommodated. Safety training will likely be required.

Aero Project 16: Predictive Control in Computational Aerodynamics

Faculty Mentors: Krzysztof Fidkowski and Dennis Bernstein

Prerequisites:

• Strong interest in dynamics, control, and computational fluid dynamics
• Knowledge of fundamentals of mechanics, ordinary and partial-differential equations, basic numerical methods
• Familiar with programming in Matlab and C/C++

Project Description: This project will involve applying a predictive controller to computational fluid dynamics simulations of airfoils, wings, and flow control problems.  The goal of the project is to identify and run fluid-dynamics cases that test and stress the controller, to compare the performance of the controller to standard methods, and to contribute to the continued improvement of the control algorithm.  On the fluids side, the project will involve setting up and running simulations in an in-house, high-order computational fluid dynamics code.

Aero Project 17: Aerodynamic Shape Optimization

Faculty Mentor: Joaquim R.R.A. Martins

Project Description: Dive into cutting-edge aerodynamic design through our undergraduate research opportunity focused on shape optimization for minimum drag. You will learn to use powerful tools for aerodynamic shape optimization and apply them to a design problem of your choice, such an aircraft wing, propeller, rotor, hydrofoil, wind turbine, or car spoiler. Collaborate with a team of passionate MDO Lab researchers to pave the way for next-generation aerodynamic design.

Aero Project 18: Design Optimization of Hydrogen-powered Aircraft

Faculty Mentor: Joaquim R.R.A. Martins

Project Description: Hydrogen-powered aircraft have the potential to significantly reduce the environmental impact of air travel, enabling sustainable aviation. In this undergraduate research opportunity, you will dive into the conceptual design of aircraft that harness the power of hydrogen propulsion and other novel propulsion architectures. Engage in innovative analysis and design, exploring how these advanced technologies can be integrated into efficient aircraft. Join the MDO Lab in pioneering next-generation sustainable air transportation.

Aero Project 19: Technologies for Small Satellite Missions

Faculty Mentor: James Cutler

Prerequisites: Hands-on project work: mechanical, electrical, and software.

Project Description: We are developing technologies for next-generation small satellite missions.  Our next mission, MC-10, will fly novel solar cell technology (organic photovoltaics) and a novel, low-noise magnetometer.  We are also collaborating to develop new thruster systems and interferometric cubesat constellations. 

Research Mode: onsite