Moncrief Grand Challenge Award Winners: Venkat Genesan (2014 winner), Chad Landis (2012 winner) and Yen-Hsi Richard Tsai (2013 winner).
From the vantage point of the 21st century, the bulky, wired-tethered computers of the 1980s don’t stand out as particularly high-tech. But back when consumer computers were just getting their start, the U.S. government had already realized that computing technology would be the power broker of the future.
“The bottom line is that any country which seeks to control its future must effectively exploit high performance computing,” reads a report from the White House Science Council from 1985.
To jumpstart and direct computational research, the U.S. government designated a list of broad research goals in 1987 as “Grand Challenges” that were defined by two essential characteristics: their status as a “fundamental” problem in science or engineering and solutions that “would be enabled by high-performance computing resources.”
Examples from the original list of challenges include computational fluid dynamics for the design of hypersonic aircraft, efficient automobiles and improved weather prediction; plasma dynamics for fusion technology, and computations for speech recognition, computer vision, and automated reasoning. Much progress has been made and new challenges added since that first list. At the forefront of it all is the Institute for Computational Engineering and Sciences.
The institute is uniquely suited to tackle Grand Challenges because of its interdisciplinary research centers and high performance computing resources at the Texas Advanced Computing Center. To promote the challenges further, the institute offers a unique research award: The Moncrief Grand Challenge Awards.
The award gives faculty with ideas on how to tackle Grand Challenges the time and money to jumpstart their work, with recipients receiving up to $75,000 to fund their research and a semester off from teaching.
Despite its uncertainty, this type of exploratory science is what’s necessary to make breakthroughs, according to Institute Director J. Tinsley Oden.
“If you’re looking at challenges that are truly grand challenges, meaning you don’t yet know how to meet the challenge, it’s going to have to be high risk basic research,” Oden said. “If it’s an important problem and you know how to solve it, it’s not a challenge.”
Recent award winners have made important progress in areas across, including computational material modeling, pathway simulation, and visualization. What unites them is their classification as Grand Challenge worthy work.
Read on to hear about three recipients and how they used the award to advance science. And check back in at the institute in March to hear about who has received the 2015 awards.
From beverage bottles to dialysis machine filters, polymer membranes are used across industries as gatekeepers, making sure some molecules stay in and others out. The effectiveness of different materials is best understood on a macroscopic level; measuring concentrations of a particular substance on either side of a membrane is routine chemistry. However, the understanding of how molecular properties influence macroscopic ones, and the connections between the two scales is much more fuzzy.
Venkat Genesan, who holds the Kenneth A. Kobe Professorship in Chemical Engineering, used his 2014 Grand Challenge Research award to improve understanding of polymer behavior by modeling the transport of materials through polymers on microscopic level.
“It’s a question of translation of the results,” Ganesan said. “If you understand what’s happening at the microscopic level, you can extrapolate what will happen at the macroscopic level.”
Being able to precisely refine membrane function could help engineers make membranes specifically optimized for certain tasks, such as water desalination, or better functioning batteries. Ganesan said the support of the Grand Challenge Award allowed him to try new research directions, and collect the preliminary evidence and models that he can put toward a more formal proposal.
“The Grand Challenge Award gives you much more freedom to take some risks. Now I have a stepping stone to put out a regular proposal somewhere else,” Ganesan said. “The bottom line is it allows me to get some preliminary results without worrying about funding for it.”
As anyone who has inadvertently cracked a smart phone screen knows, cracking patterns take a complex, multi-branched path.
Chad Landis, a professor of aerospace engineering and engineering mechanics and member of the ICES Computational Mechanics Group, used his 2012 Grand Challenge Research Prize to create a program that can generate the path of a crack driven by pressured fluid through an environment.
While the program won’t do anything to help fix your cracked electronics, understanding crack pattern propagation is an important part to improving fracture dependent technologies, such as hydraulic fracturing and carbon sequestration.
“There are several reasons why it’s important to get a good technological understanding of these [cracking] processes,” Landis said. “And whether it’s applied to hydraulic fracturing or perhaps sequestration, we’re developing the tools to represent the physics in an accurate way.”
Currently, most techniques for computing crack propagation involve tracking the movement of a crack by using specifically encoded rules to determine how the crack moves at each step through space. However, this technique is vulnerable to failing in complex or novel situations, and even coming up with an exhaustive set of propagation rules can be difficult, especially in three dimensions.
Looking to improve the effectiveness of crack modeling across applications, Landis used his award to investigate a new technique called the “phase-field method” that determines crack paths from solutions to differential equations instead of rule-based approaches. Landis believes this is better because it more “seamlessly” simulates the intricate crack propagation patterns that can arise.
“The benefit of this approach is to track more complex crack configurations,” Landis said. “So if cracks want to join, intersect, or bifurcate from one to two cracks, these features are handled very naturally, as opposed to other methods that would require specifically defined rules to handle those situations.”
By the end of the award, Landis and his research team have created a phase-field program that accounts for the governing physics of crack propagation. In addition, a graduate student , Zach Wilson, working with Landis’ received a fellowship from the multinational oil company Statoil to continue the research.
The Grand Challenge Award helped the research address some underestimated challenges in the modeling, said Landis, by providing time and funding to focus on overcoming them.
“Research never goes as it’s expected to go, but now we have a better handle on the problems and we’re hoping to have a publication come out it that addresses many of the problems that we thought were going to be easy at first,” Landis said.
When you find yourself in an unfamiliar place, one of the best ways to navigate it is to make a mental map of the location. What are notable landmarks? Where are entries and exits? Where do hallways intersect, curve or stop?
Yen-Hsi Richard Tsai, a mathematics professor and member of the ICES Center for Numerical Analysis, used his 2013 Grand Challenge Award to continue the development a mathematical algorithm that can guide this map-making process across applications—from medical and scientific imaging to robotic surveillance.
The algorithm works by analyzing initial input data—for example, remote sensing or diffusion MRI readings— in order to make recommendation on how further readings can be taken most efficiently to give a useful overview of an area.
“My contribution is how to most efficiently take measurements to achieve the best reconstruction. We want to use the minimum amount of measurements in the most judiciously chosen way,” Tsai said.
The efficiency factor is a very important aspect of the work that makes it more feasible for practical uses. For example, in a map-making robot, efficient use prevents backtracking and extra-readings, making for speedier results and longer lasting battery life.
“I really aim at that,” Tsai said. “I don’t just want to develop something new mathematically or computationally. I want to make sure things have a real world connection by trying to be bring an impact.”
Fostering the transition of his algorithm into application, Tsai spent a large part of the Grand Challenge Award meeting with research colleagues around the globe. He talked about diffusion MRI imaging capabilities with University of California, Los Angeles’ Brain Research Initiative; environment mapping with the U.S. Department of Defense; and electron microscope imaging of cell division at the Karolinska Institute in Solna, Sweden.
“The Grand Challenge Award is such a great gift because being able to visit colleges and have the relief of teaching allows me to think about new problems and new approaches to problems,” Tsai said, “Right now, the papers and projects that I’m involved with were formulated at that time.”