Climate and Space Sciences and Engineering 2024 SURE Projects

CLASP Project #1: SunRISE Ground Radio Lab (GRL)

Faculty Mentor: Mojtaba Akhavan-Tafti, [email protected]

Prerequisites: 

  • Programming Competency, Strong Mathematics 

Project Description: The SunRISE Ground Radio Lab (GRL) engages citizen science using a multi-frequency radio telescope to observe radio emissions from Jupiter, the Sun, the Milky Way Galaxy, and Earth. The SunRISE GRL aims to complement SunRISE’s measurements in space, and to engage the public and educate the next generations of Science, Technology, Engineering, Arts, and Math (STEAM) scholars through hands-on citizen science campaigns. The SunRISE GRL is seeking a student to contribute to developing a date pipeline and analyzing radio measurements, from more than a dozen radio antennas deployed at high schools nationwide. 

Research Mode: In Lab

CLASP Project #2: Space Debris Identification and Tracking (SINTRA)

Faculty Mentor: Mojtaba Akhavan-Tafti, [email protected]

Prerequisites: 

  • Programming Competency, Strong Mathematics 

Project Description: Roughly 70% of the objects orbiting the Earth are classified as mission-ending space debris. This number only represents currently-trackable space debris, which are larger than 10 cm in diameter. Current capabilities fall short when it comes to smaller debris, despite their potential to cause substantial damage to spacecraft. NASA estimates the existence of over 100 million non-trackable, mission-threatening space debris greater than 1 mm encircling the Earth. This Space Debris Identification and Tracking (SINTRA) project aims to improve by 10,000 times our space debris detection capability to include the size range of lethal non-trackable space debris (10μm-10mm) using ground-based radars to detect non-thermal electromagnetic (NTEM) radiation. Therefore, the SINTRA project is seeking a student to contribute to ground-based radar data collection, processing, and analysis.

Research Mode: In Lab

CLASP Project #3: Magnetic switchbacks in Parker Solar Probe (PSP) and Solar Orbiter (SolO)

Faculty Mentor: Mojtaba Akhavan-Tafti, [email protected]

Prerequisites: 

  • Programming Competency, Strong Mathematics 

Project Description: Parker Solar Probe (PSP) is the closest object ever launched to our Sun. Our project is seeking a student to contribute to investigating switchbacks using magnetic and plasma measurements aboard PSP and Solar Orbiter. The student will help with collecting, processings and analyzing data, contribute to preparing manuscripts for publication, as well as for presentations at international conferences.

Research Mode: In Lab

CLASP Project #4: Active Solar Tracking in Three-Dimensional Solar Modules

Faculty Mentor: Mojtaba Akhavan-Tafti, [email protected]

Prerequisites: 

  • Programming Competency, Prototyping and microcontroller (Arduino, etc.) Experience

Project Description: The University of Michigan’s patented 3D photovoltaics are compact, they collect and convert direct and reflected light vertically, thereby reducing installation footprint. The project is seeking a student to assist with designing, developing, and testing an intelligent solar tracking system to maximize energy output of a 3D solar module by actively tracking the Sun in the Sky throughout a day and in the course of a year. The project involves first preparing a trade study of single and dual-axis tracking mechanisms, including materials, complexity, and cost, in order to identify an economically-scalable tracking system. The next step is to simulate, design, develop, and test the scalable tracking system. To achieve this, the student will be equipped with microcontrollers (Arduino, etc.) and hardware. The student will conclude the project by delivering prototypes of the proposed scalable tracking systems.

Research Mode: In Lab

CLaSP Project #5: Energy Transfer through the Earth’s Magnetopause to Unravel Solar Wind –
Magnetosphere Coupling

Faculty Mentor: Matti Ala-Lahti, [email protected], Tuija Pulkkinen, [email protected]

Prerequisites:

  • Some data analysis and programming experience and using MATLAB will be useful but not required.

Project Description: The Earth’s magnetopause is a current layer that separates the solar wind from the Earth’s magnetic environment. Energy transfer through the boundary is enabled by a variety of plasma physics processes which couple the two domains. Solar wind – magnetosphere coupling has
space weather consequences that can damage satellites and disturb technological systems on
ground. Recording the energy transfer and its spatial variations at the magnetopause has a
fundamental role in understanding how space plasmas interact. Until the recent state-of-the-
art Magnetospheric Multiscale (MMS) mission, no spacecraft mission has provided adequate
measurements for a such effort.
This project utilizes an MMS magnetopause crossing database to build robust statistics about
the energy transfer. While the focus is on data analysis of local in-situ measurements, being
part of a research group that uses numerical simulations to study these processes will give an
opportunity to use model results to put the observations in context, and thereby gain a global
picture of the magnetospheric dynamics.

Research Mode: In Lab/Hybrid

CLASP Project #6: Extreme Thermal Testing for Lunar Magnetometers

Faculty Mentor: Mark Moldwin, [email protected]

Prerequisites:

  • Some basic lab equipment experience

Project Description: We are developing magnetometer sensors to be deployed on the surface of the Moon and therefore will be exposed to wide temperature environments. The SURE research assistant will conduct both operation and survival temperature tests of the UM/PNI magnetometer using cryogenic liquids and thermal chambers in the Magnetometer Lab. SURE students (EE, ME, Space Instrumentation, Data Science, Physics, Earth Science, or another STEM major) can participate in this project.

Research Mode: (In Lab)

CLaSP Project #7: Unveiling Hidden Signatures in in-situ Solar Wind Measurements through Machine Learning and Artificial Intelligence

Faculty Mentor: Liang Zhao, [email protected]

Prerequisites: 

  • Some programming experience

Project Description: The solar wind, a stream of supersonic ionized particles accelerating away from the Sun, shapes the structure of the heliosphere and drives space weather throughout the geo-space environment and beyond. What are the physics processes in the coronal base that ionize, heat, and accelerate the solar wind is a crucial question to space weather science and forecasting, and are of great interest to the Heliophysics community. To answer this question, solar wind in-situ properties are the key because they are inextricably tied to solar wind origins on the Sun and in the inner solar corona. Given the growing size and complexity of solar wind in-situ observations and the advancements in ML and AI techniques, it is crucial that we incorporate modern ML and AI data analysis methods into the solar wind data analysis. By doing so, we will be able to maximize the scientific outcomes we can learn from the data. This research project is aimed at applying ML and AI algorithms on solar wind in-situ dataset (such as Solar Orbiter) in order to efficiently and objectively classify the solar wind data and accurately predict the data for the future Heliophysics phenomena.

Research Mode: In Lab, Online, Remote, or Hybrid

CLaSP Project #8: Real-time Solar Wind Prediction 

Faculty Mentor: Zhenguang Huang, [email protected]

Prerequisites: 

  • Some programming and data analysis experience

Project Description:

 The solar wind is a continuous plasma flow streaming from the solar surface and propagating through the heliosphere to Earth. It plays a crucial role in space weather prediction as it provides the background for space weather events. The Alfven Wave Solar Model (AWSoM) developed at University of Michigan is one of the most commonly used solar wind models in the space science community. AWSoM has been extensively validated and can provide reasonable agreement with various solar wind observations. However, there are a few adjustable parameters of the model, which affect the accuracy of the solar wind prediction. This project will focus on understanding and evaluating AWSoM performance in different phases of the solar cycle, preparing the transition of AWSoM to a real-time solar wind prediction tool in the near future.

Research Mode: In Lab