AEM faculty spotlight:
Ryan Elliott
Cutting-edge computer games utilize worlds made of materials that actually resemble their real-world counterparts. Beyond color or texture, these materials appear to react similar to how they would in reality. But beyond appearance of reaction, AEM Assistant Professor Ryan S. Elliott is working to develop a type of modeling where the models react to stimuli exactly as they would in reality. These types of models, which are called first principles models, hold potential to simplify material creation and allow for custom-designed materials. What follows is a discussion with Professor Elliott on his techniques and what the future may bring for shape-memory materials.
Ryan Elliott
What type of research do you do?
My work attempts to model shape-memory materials and fundamentally understand why they exhibit their surprising and unusual behavior. I am doing this by starting at the smallest scale and studying the atomistic interactions. I want to see if we can develop models that will accurately predict this type of behavior. If so, we could come up with new materials that have better properties in this way. I’ve been exploring a number of different ways to do this, work that’s a continuation of my Ph.D. thesis. The techniques I’m studying – which I call branch-following and bifurcation (BFB) methods – allow you to efficiently explore how a first principles material model reacts to changes in temperature or some other stimulus. It’s a nice, systematically way to determine the behavior of different materials. The difficulty, of course, is trying to understand the materials and build in enough fundamental physics to the model so you can get accurate behavior predictions.
Do you have an example of an unusual application of shape-memory materials on a current technology?
Every year a large number of people visit the repair shop to have a rattle in their car fixed. It’s been suggested that the amount of time and money you need to spend for the auto mechanics to take your car apart, find, and then tighten the loose bolt could be reduced significantly by the use of shape memory materials. The idea is to use a shape-memory alloy washer on the bolts that hold the car’s components together and to connect it to electrical leads so that if the nut starts rattling, it can be detected. Then, that particular shape memory washer can be heated up so that it automatically tightens the bolt. This could be done without ever having to disassemble any part of the car.
What initially excited you about this field?
The first thing is the unusual behavior of these materials. The examples that you can show, the demonstrations you can do are really intriguing. For instance, if you have a piece of this material, say a wire, and you wrap it around your finger, it forms a spring shape, as would steel. But if you drop this spiraled wire into a hot cup of water, it will actually straighten out, like before you deformed it initially. It is a very unintuitive thing; it is not something we encounter in our day-to-day lives. I definitely want to understand why it is going on and how it happens, all in terms of basic principles of physics. Once I have a good idea I can build a material model based on that idea.
What is a model?
If I have a material model I could say that I will apply a certain temperature to the model, and it returns the material’s response. There are a lot input parameters, like temperature, that I could give, and the model will give different responses for different sets of input. It’s too big a problem to feed the model every possible set of data and look at the outputs. The BFB methods I have developed are a systematic way to scan through a limited set of input data, pull out the information we are most interested in and ignore the superfluous. A good model equals good predictions.
Is there a particular metal of interest right now?
The shape memory nickel titanium. It is used extensively in the medical industry for vascular stents and surgical tools. Stents are little wire meshes wrapped into a cylindrical tube. Making stents from shape memory alloys allows, at body temperature, for it to be completely crushed, put into the body, and then to regain its original tube shape. The shape memory material is useful because it is biocompatible, can regain its shape and also can deal with cyclic deformation associated with the body’s pulse. It has moderately good fatigue life, but it is not perfect. Improvements to all of these properties would lead to wider and more successful medical applications of shape memory materials and, consequently, a healthier population. My research aims to use first principles material models to discover and design materials with these improved properties.
What does the future hold for you in terms of research?
In the immediate future I am going to be continuing the development and improvement of my BFB exploration techniques. They need to be automated so that we can run large simulations without a lot of user interaction. I will continue modeling materials and working to predict shape memory material behavior. The longer-term goal would be to develop a tool that could be made available to not only researchers in academia but those in industry. They would use this tool to help understand and design new materials. The modeling tools would be able to predict the behavior of new materials before they are built in the lab.
Last Modified: Tuesday, 06-Nov-2007 08:33:06 CST -- this is in International Standard Date and Time Notation