Q&A: Looking for Failure
Karen Daniels describes herself as being on a random walk though science. Originally, she went to college to study engineering. But Daniels was so transfixed by her physics lectures—statistical mechanics in particular—that she never made it to an engineering class, and after two semesters she switched her major to physics. She is now a professor of physics at North Carolina State University. Driven by curiosity and a fascination with intriguing problems, Daniels has worked on myriad questions from how patterns develop in a moving fluid to how cracks propagate in a gel. Her current focus is granular materials, which can be found on the breakfast table, inside the body, at the beach, and practically everywhere else. Physics spoke to Daniels to find out about the techniques she’s using to predict how and when these systems will fail.
What is a granular material?
It’s basically a material that’s made up of chunks of other materials. More formally, a granular material is a collection of solid bodies, or particles, that interact with each other elastically and through friction. Familiar examples are foods like cereal, sugar, and ground coffee. But other materials like biological tissues (made of cells) or emulsions (made of droplets) can also be thought of as granular systems.
What is it about granular materials that fascinates you?
I love that they are involved in so many natural phenomena. One example is the behavior seen in earthquakes, where the ground along a fault line gets stuck for a long time and then suddenly slips, generating a tremor. This “stick-slip” motion shows up in many granular materials and on many length scales. It’s seen, for instance, when granola jams up as you try to dispense it from a bulk bin.
What problem currently captures your attention?
I’m trying to understand how force “networks” within a granular material control its bulk properties, like softness or the ability to transmit sound. By force network, I mean the paths along which forces between the particles propagate.
In my lab, we map out the networks using a technique that lets us spy inside each particle. We place a layer of flat, transparent disks on a table and then squeeze the disks together. The disks are photoelastic. This means that when we shine light through them, points of high stress appear bright, so we can directly see how force propagates from disk to disk.
What have you learned from these networks?
First, when sound propagates through a granular material, it tends to pass through the points where the particles push against each other hardest. This route is like a sound-wave highway. By adjusting this highway, you could control and guide sound through a material.
Second, knowing the network, we can predict where a material will most likely fracture. Each prediction is just a forecast, but we can make it with reasonable statistical confidence. These predictions could let us design materials that fail in a particular way, for example, by breaking catastrophically or by falling apart gently and diffusely.
When might you want failure of one type or the other?
For protective equipment such as bike helmets, the ability to fail via lots of tiny cracks distributed diffusely throughout the material is much safer than a helmet that breaks in half and falls off your head. But breaking in half is precisely the behavior that you want when cracking an egg to cleanly release its contents.
You’ve said that collecting these force maps leaves you with a “painful” amount of data to analyze. Have you thought of using machine-learning algorithms to help?
Yes, but I have barely dipped my toe in the machine-learning water. Artificial intelligence could be better than we are at finding patterns in a giant dataset. But then I worry about not understanding what those patterns mean.
What advice do you wish someone had given you when you started your physics career?
I have worked on a lot of different and interesting problems, changing my research focus when something new grabbed my attention. I wish someone had told me early on that doing this was okay. The scientists we commonly lionize are typically experts on one topic or system and have a string of amazing findings. But there are also very successful scientists who zigzag across topics, going where the inspiration strikes. This second path tends to be less common, but it’s also a great way to be a scientist.
Know a physicist with a knack for explaining their research to others? Write to firstname.lastname@example.org. All interviews are edited for brevity and clarity.