When we look at a cloud, there are many ways we might describe it: wispy, fluffy, dense, dark or ominous.
When Jinlong Wu looks at a cloud, however, he sees a complex dynamical system that presents an important, yet vexing, modeling challenge. “Modeling and simulating them is critical to predicting future climate—but it turns out to be much more complicated than they look like,” says Wu, whose research focus is mainly on modeling and simulating such complex systems. “They consist of different types of physics at different scales—for example, condensation of water vapor, collision of cloud droplets, and convection at large scale. One current challenge of simulating complex dynamical systems like clouds is that existing models are often not good enough.”
Wu, who joined the Department of Mechanical Engineering in August 2022 as an assistant professor, says that advances in machine learning make it possible to improve those models by leveraging different types of data. Yet, he says, their predictive capabilities are still lagging, and that’s also one reason for challenges in building digital twins (virtual counterparts of real objects or systems) to simulate real-world engineering applications.
Wu aims to change that. Through his research, he plans to develop predictive data-driven modeling techniques to simulate complex dynamical systems and build reliable digital twins for applications in areas that include energy harvesting, advanced manufacturing, and robotics.
As an undergraduate, Wu channeled his curiosity about energy harvesting into a thermal energy and power engineering bachelor’s degree from Southeast University in China. During his master’s degree work in power engineering there, he also took a handful of fluid dynamics courses. While the courses piqued his interest in fluid dynamics, he also noticed that some of history’s famous fluid dynamicists—for example, Theodore von Kármán—worked in aerospace engineering. “Therefore, I went to an aerospace engineering PhD program, hoping to get some new inspiration by studying in the aerospace engineering major and learning more about fluid dynamics.”
And as a PhD student in aerospace engineering at Virginia Tech, he found that inspiration in the form of turbulence, noting that some of the most exciting milestones in turbulence modeling had actually been achieved by pioneers in large-scale flow problems—challenges on the order of atmospheric and oceanic dynamics.
Wu went with the flow, pursuing postdoctoral research in large-scale fluid dynamics—motivated, in part, by damage to the global climate caused by excessive fossil energy use over the past century. “With several years of studying thermal energy and power engineering, I also wanted to learn more about how to quantify and predict the impact of energy harvesting and engineering practices on climate and environment,” he says.
Now, as he begins his faculty career, Wu says he sees the future of his research as an interdisciplinary program that bridges methodology development in applied math and statistics along with the need for modeling complex dynamical systems in engineering applications. “UW-Madison has put lots of effort in promoting interdisciplinary research programs, and I hope my research program can further contribute to this great interdisciplinary environment.”
That environment, and the opportunity to collaborate with colleagues in mechanical engineering and across campus, is among the reasons he’s excited to join the mechanical engineering faculty. He’s equally enthusiastic about teaching, and says he is looking forward to the chance to shape the next generation of problem-solvers and engineering leaders.
Among his priorities? “Setting up my research lab, preparing for the first lecture, and kayaking on Madison’s lakes,” he says.