Guglielmo Scovazzi Wins DOE Early Career Award

2014-05-08 13:44

Guglielmo Scovazzi
Guglielmo Scovazzi, an associate professor in both civil and environmental engineering as well as mechanical engineering and materials science, is the recipient of the 2014 U.S. Department of Energy (DOE) Early Career Award, in the Advanced Scientific Computing Research (ASCR) Program.

Through the DOE Early Career Research Program, the U.S. Department of Energy supports the development of individual research programs of outstanding scientists early in their careers. The award comes with a five-year grant of $750,000 to stimulate research careers in the disciplines supported by the DOE Office of Science.

Scovazzi’s research is in the field of computational fluid dynamics and specializes in simulating the interaction of airflows with physical structures in aerospace, petroleum, wind and nuclear engineering. Fluid/structure interaction problems arise in many important modern technologies, including wind turbine blades, high-speed impacting bodies and at the core of nuclear reactors. With the help of his team at Duke, Scovazzi seeks to create new algorithms to accurately and reliably simulate systems of very complex geometrical shapes, which are still a major challenge in the computer-based simulation sciences.

Modern computational methods decompose physical objects and airflows into computational lattices (or grids) used for numerical solution and integration. Complex geometries, however, often put physical structures and airflows at odd angles to one another, making standard grid generation techniques impractical. Scovazzi’s approach is to attack the problem by means of a new, more flexible, class of immersed boundary and embedded discontinuity methods, in which fluid and structural grids are allowed to overlap. This work will be carried out while making connections with current approaches to simulation, such as finite element, finite volume or finite difference methods, and also aiming at implementation on future, massively parallel, exascale-capable computer architectures.