Wu’s Work Supports 3-D Manufacture of Complex Parts in Hypersonic Vehicles

Third-year student Nick Wu has earned a distinguished undergraduate hypersonics research scholarship to pursue his interest in applied mathematics at the University of Virginia School of Engineering and Applied Science. The hypersonics research scholarship program is funded by the U.S. Department of Defense Joint Hypersonics Transition Office and the Naval Surface Warfare Center.

Wu is majoring in engineering science with a minor in computer science; he’s also fulfilling the requirements for second major in math.

“Computers and math are a big part of my life,” Wu said. Wu, who is from Leesburg, Virginia, taught himself chemistry during his senior year of high school. Through this self-directed learning, Wu discovered that he liked describing things in the language of a physical scientist.

As a first-year undergraduate at UVA, Wu became intrigued by materials science. He met Prasanna Balachandran, assistant professor of materials science and engineering and mechanical and aerospace engineering, at an event hosted by the Department of Materials Science and Engineering to interest students in majoring in engineering science.

Wu and Balachandran talked about the intersection of materials science and computer science and the limitations of silicon-based technology.

“To keep scaling up, we need to develop new materials for computing,” Wu said. “That’s a really cool topic.”

Wu develops predictive computer models that can reduce the variability in processing 3-D manufactured parts, which provides a better understanding of the process without resorting to trial-and-error experimentation.

Whereas Wu decided that mathematics was the best fit for his degree program, he still wanted to explore materials science and machine learning, a specialty of Balachandran’s materials informatics research group. Working in Balachandran’s lab this summer, Wu applied machine learning and statistical techniques to understand how to 3-D print parts.

Additive manufacturing, especially the selective laser melting processes perfected at UVA Engineering, is attractive to the aerospace industry. 3-D printing makes it possible to manufacture parts on a faster, more creative scale, with geometries that are more difficult or expensive to manufacture using conventional techniques.

“Hypersonic vehicles depend on advanced thermal protection systems; these systems require very long and complex tubing that’s difficult to manufacture using conventional techniques,” Wu said, citing one example.

“Advanced high-temperature materials are the Joint Hypersonics Transition Office’s number one priority when it comes to the development of new technology for hypersonic vehicles,” said Christopher Goyne, associate professor of mechanical and aerospace engineering and director of the UVA Engineering aerospace research laboratory. “It’s wonderful that our nation has high-caliber students like Nick who are working hard to solve problems of such high national importance.”

Wu is developing predictive computer models that aim to reduce the variability in processing 3-D manufactured parts. This approach provides a better understanding of the process without resorting to trial-and-error experimentation, which is time-consuming and expensive.

“It’s an extremely complicated process with lots of variables to keep in mind and lots of considerations I haven’t encountered before – the sort of complications you would encounter in the real world,” Wu said.

Wu’s more specific task is to design a model that can provide a measure of accuracy for its prediction that a part will be good or defective when the printer executes a given program.

“The typical way to quantify and propagate uncertainty is to make tens of thousands of predictions and apply a statistical approach. This takes a lot of time,” Wu said.

He is exploring an alternative framework called polynomial chaos expansion to speed up the uncertainty quantification. Wu re-conceptualized this approach well enough to synthesize it with an existing 3-D printing model and generate the desired output.

“It’s satisfying to watch another puzzle piece fall into place, to see how the work that others have done fits into our picture,” Wu said.

Wu said he is grateful for the opportunity to see common mathematical tools, programs and approaches applied to a specific domain like additive manufacturing.

“The intersection of domain and principles is really important; this research experience is a big step toward a career in applied mathematics,” Wu said.