Scientific computing techniques to understand the solar atmosphere dynamics and its impact on the Earth’s ionosphere. She will utilize both advanced data analysis methods and numerical models in her research.
Shasha Zou, Climate and Space Sciences and Engineering
Modeling space physics and predicting space weather with a combination of first-principles models, machine learning and data assimilation.
Gabor Toth, Climate and Space Sciences and Engineering
Jeremy Bricker is an Associate Professor in the department of Civil and Environmental Engineering. His research is focused on hydraulic engineering to investigate the resilience of structures and infrastructure exposed to both increasing hazard due to climate change and increasing consequences due to expansion of development in coastal and flood-prone areas.
Computational methods are useful in hydraulic engineering for assessing the safety of coastal and hydraulic structures, estimating the flood risk experienced by communities, and predicting damage to buildings during floods, hurricanes, and tsunamis. At a large scale of hundreds to thousands of kilometers, shallow water equation models simulate tsunami propagation, storm surge and wave generation, and river flood occurrence. At scales of kilometers to tens of kilometers, these models resolve overland inundation due to flood events, allowing empirical or analytical estimates of forces on structures and damage to buildings and infrastructure. At a small scale of tens to hundreds of meters, computational fluid dynamics (CFD) directly calculates pressures and forces on submerged and emergent structures from floodwaters and waves. This can be linked with a dynamic response model to assess whether resonance could lead to structural failure, or linked with a Finite Element Method (FEM) model to assess stresses within the structure. Such modeling is useful for forensic analysis of the failure of bridges, buildings, and other infrastructure after floods, as well as for planning and design of new structures.
Crash simulation plotting von Mises stress. A discretization of Kirchhoff-Love shells based on analysis-suitable T-splines is used. This simulation includes elastoplastic material behavior, fracture criteria, contact algorithms, and spot-weld modeling. Material failure takes place around the largest hole of the B-pillar.
Yin Lu (Julie) Young is a Professor in the department of Naval Architecture and Marine Engineering. Her research focuses on the dynamic fluid-structure interaction response and stability of smart/adaptive multi-functional marine structures such as marine propulsors, turbines and control surfaces. One of her research focus is the fluid-structure interaction response and stability of marine and coastal structures. She is the current director of the Aaron Friedman Marine Hydrodynamics Laboratory. Her research has been supported by the Office of Naval Research (ONR), the Naval Surface Warfare Center (NSWC), and the National Science Foundation (NSF).
Shasha Zou is an Associate Professor of Climate and Space Science and Engineering. Her general research interest is about studying the dynamic interaction between the Sun’s extended atmosphere, i.e., solar wind, and the near-Earth space environment. In particular, she is interested in the physical processes of formation and evolution of ionospheric structures and their impact on technology, such as global navigation and communication satellite system (GNSS), during space weather disturbances using multi-instrument observations and numerical models. Numerical models often used include magnetohydrodynamic (MHD) model of the global magnetosphere, and physics-based global ionosphere and thermosphere model.
Dr. Avestruz is a computational cosmologist. She uses simulations to model, predict, and interpret observed large-scale cosmic structures. Her primary focus is to understand the evolution of galaxy clusters. These are the most massive gravitationally collapsed structures in our universe, comprised of hundreds to thousands of galaxies. Other aspects of her work prepare for the next decade of observations, which will produce unprecedented volumes of data. In particular, she is leading software development efforts within the clusters working group of the Large Synoptic Survey Telescope to calibrate galaxy cluster masses from simulation data. Dr. Avestruz also incorporates big data methods, including machine learning, to extract gravitational lensing signatures that probe the mass distribution of massive galaxies and galaxy clusters.