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Designing extensible fluids (machine learning-guided research)


Term:

Spring

Department:

2: Mechanical Engineering

Faculty Supervisor:

A. John Hart

Faculty email:

ajhart@mit.edu

Apply by:

Feb 2, 2020

Contact:

Please share your resume/CV and brief statement of interest with Crystal.

Project Description

What makes warm mozzarella so stretchy and parmesan so crumbly? It’s mostly a difference in their “extensional rheology”. Useful for more than just Italian toppings, extensional rheology is a measure of the extensibility of fluids with added polymers and other macromolecules or microparticles. Using a previously developed platform for measuring the extensional viscosity of fluids, this project seeks to finish fully automating the research device (1 month) and then implementing a machine learning program to guide the development of new mixtures of fluids with desirable extensional viscosity (3 months). The UROP may be involved in hands-on work in the lab, including full automation using basic equipment like arduinos and computer-controlled motion stages to learn the system, making a series of manual measurements (or scraping data from published results) as a training data set, and will further test and aid development and implementation of a program for computer-directed development of novel extensible fluids.

Pre-requisites

Demonstrated experience with developing machine learning architecture and/or computer vision and image analysis are strongly preferred. We especially welcome applicants in their junior year who are interested in a multi-semester UROP, and/or a UROP leading to a senior thesis.