2014 Grand Challenge Awardees (l to r) Tan Bui-Thanh, Thomas J. R. Hughes, Michael Baldea, and Kui Ren.
Four faculty received ICES’ 2014 W. A. "Tex" Moncrief Grand Challenge Awards, based on their highly compelling research proposals related to the Grand Challenges in computational engineering and sciences that affect the competitiveness and international standing of the nation.
Michael Baldea, assistant professor of chemical engineering; Tan Bui-Thanh, assistant professor of aerospace engineering and engineering mechanics; Thomas J. R. Hughes, professor of aerospace engineering and engineering mechanics; and Kui Ren, assistant professor of mathematics will receive stipends of up to $75,000 per award per semester to cover salary and other expenses necessary to further their research.
As the first stepping stone toward a scalable parallel solver and time
stepping scheme for the dynamical core of weather simulation/prediction
model, Bui-Thanh will use his award toward developing hybridized
high-order DG methods for simulating hydrostatic and non-hydrostatic
Baldea’s research will focus on smart manufacturing. In particular, he will investigate a new visualization and analysis framework which relies on large-scale data collected from chemical and petrochemical plant operations to anticipate and minimize the occurrence of environmentally harmful flare events. The project aims to develop a strategy for detecting and mitigating plant disturbances and faults that can lead to flaring, thereby improving the sustainability of chemical and petrochemical processes.
The purpose of Hughes’ research is the development of patient-specific computational tools and their application facilitating the judicious interpretation of clinical data (retrospective analysis) to better understand the origin and development of Peripheral arterial disease; inspire new clinical trials; optimize imaging practice for the early detection of the disease; and tune therapeutic regimens based on patient-specific attributes.
Ren’s project is concerned with the computational study of quantitative photoacoustic tomography (QPAT), a hybrid biomedical imaging modality that combines ultrasound imaging with optical imaging to provide high-resolution and high-contrast images of the optical properties of biological tissues. The main objective of the study is to develop fast and robust image reconstruction algorithms for QPAT in practically relevant physical settings.