September 11, 2023
Preparing Future Faculty for Inclusive Excellence (PFFIE) is a selective program designed to help recent Ph.D. graduates transition to full-time faculty positions.
“It’s an extraordinary opportunity,” Nunez said. “It’s helpful on many levels. Typically in a Ph.D. program, you’re completely devoted to research and maybe help teach a class. But you have no idea about the inner workings of academic life, what is required for tenure, the importance of service—essentially life as a faculty member. There’s a learning curve you have to go through, and this program eases that transition.”
Nunez earned his Ph.D. in mathematics from Mizzou in 2021. Last year, as part of a different post-doctoral fellowship, he got involved in engineering research through Professor Giovanna Guidoboni, who uses mathematical modeling to study physiological systems.
Now, for the PFFIE fellowship, Nunez has joint appointments in the Department of Mechanical and Aerospace Engineering (MAE) and Mathematics Department. As part of the position, he’s working in MAE Chair Hongbin “Bill” Ma’s Multiphysics Energy Research Center, where he’s using his mathematical expertise to improve upon algorithms that predict the performance of heat transfer devices known as oscillating heat pipes.
Applying Machine Learning and Mathematical Models
Oscillating heat pipes transfer heat from one place to another. They’re used in computers to pull heat away from microchips, in aircraft to redistribute heat, and many other applications.
“They’re a great solution for heat management problems, but the drawback is that their physical innerworkings are so complicated it’s hard to predict their behavior before they are manufactured and tested,” he said.
Currently, manufacturing of oscillating heat pipes for a given application relies mostly on a trial and error approach that is costly and time consuming.
Machine learning — a subset of artificial intelligence — theoretically could help researchers predict the performance of oscillating heat pipes before fabrication. However, machine learning works best when it has massive amounts of data, which isn’t readily available in this case.
“Data for oscillating heat pipes come from experiments, which are hard to conduct and expensive” Nunez said. “In this setting, machine learning hits a wall, and you typically don’t have all the data you need for the algorithms to perform as well as you want them to.”
Nunez and colleagues are trying to overcome that challenge by combining machine learning with knowledge of the physics of oscillating heat pipes to help algorithms produce more accurate predictions. Then, a computer program could recommend design specs such as dimensions and materials.
An interdisciplinary approach
While his doctoral program was solely focused on pure mathematics, Nunez has come to appreciate the idea of using abstract mathematics to solve real-world problems.
“Interdisciplinary research is essential, as no one has the breadth of expertise to understand all of the complexities of certain problems,” he said. “I’ve found that faculty both in engineering and mathematics are quite open to interdisciplinary collaboration.”
That’s one reason Nunez hopes to begin his academic career here following the two-year PFFIE fellowship.
“My goal is to remain at Mizzou as a faculty member after the post-doc ends,” he said. “Mathematics is a language that may be very obscure to non-experts, but history shows that there’s a wealth of knowledge there that can be exploited in engineering.”